Overview of the Recent Amino Acids Work in Growing Pigs - Anne Clark, Kansas State Research and Extension, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Paulo Arruda - Continuing Diagnostic Investigation–Novel SapelovirusJohn Blue
Continuing Diagnostic Investigation–Novel Sapelovirus - Dr. Paulo Arruda, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Jenny Patterson - How Synchronization Works and How to Make It BetterJohn Blue
How Synchronization Works and How to Make It Better - Dr. Jenny Patterson, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Dave Baumert - Impact of Batch Farrowing on Health and ProductivityJohn Blue
Impact of Batch Farrowing on Health and Productivity - Dr. Dave Baumert, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Methods for Sensor Based Farrowing Prediction and Floor-heat Regulation: The ...Aparna Udupi
Piglet mortality is a current issue in the pig production and the large variability between herds suggests a management component to the mortality. Studies show that the mortality may be reduced by the supervision of farrowing or through climate regulation in the farrowing pens. However, this is possible only if the farrowing time is known and thus provides sufficient time for the management to make and execute the decisions. The gestation period of a sow is approximately 115 (SD=2) days. However, an initial cost-benefit analysis recommended increased precision in the prediction of farrowing to make the increased management efforts cost-effective for the pig producer. Recently, a wide range of sensor technology have become available to monitor the behavioural and physiological changes of sow. Evidence show that appropriate utilization of sensor technology may increase the precision of prediction of onset of farrowing. Prediction is feasible only if the prediction is online and automated.
The thesis is focused on constructing a system that can give predictions about the expected time to farrowing of individual sows based on automatic sensor recordings such as water consumption, video based activity measurements, and photo-cells based activity measurements. The warnings could serve to activate the floor heating system to ensure a sufficiently high temperature for the new born piglets, as well as to help the farmer to organize extra surveillance around farrowing. The thesis is based on three submitted manuscripts, describing the system at different stages.
The kernel in the thesis is a Markov process with four subsequent states Before Nest-Building, Nest-Building, Resting and the absorbing state Farrowing; the states were selected based on ethological knowledge about sow behaviour. However, the sojourn time distribution in each state is not exponential. Therefore a continuous time discrete state semi-Markov process based on a Phase-Type distribution (in this case Erlang distributed) was formulated. Finally, the Markov process was transformed to a discrete time process. A Hidden Markov Model (HMM) was used for this process. The model is called Hidden Phase-type Markov Model (HPMM), and the time steps corresponded to each updating with sensor information at which the time to farrowing was predicted.
The POMDP model for optimizing the floor-heat regulation system was used to demonstrate the decision support tool as an extension to the prediction algorithm.
The tools for the prediction of onset of farrowing, estimation of HPMM and optimal decision making provides a framework for handling a large amount of sensor data available and gives an overview of how to integrate information from several sensors on the pen level. The complexity of the models imply that the prediction algorithm and decision tool may be run on the herd level computer; whereas the parameters were estimated on the central level computers.
Dr. Paulo Arruda - Continuing Diagnostic Investigation–Novel SapelovirusJohn Blue
Continuing Diagnostic Investigation–Novel Sapelovirus - Dr. Paulo Arruda, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Jenny Patterson - How Synchronization Works and How to Make It BetterJohn Blue
How Synchronization Works and How to Make It Better - Dr. Jenny Patterson, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Dave Baumert - Impact of Batch Farrowing on Health and ProductivityJohn Blue
Impact of Batch Farrowing on Health and Productivity - Dr. Dave Baumert, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Methods for Sensor Based Farrowing Prediction and Floor-heat Regulation: The ...Aparna Udupi
Piglet mortality is a current issue in the pig production and the large variability between herds suggests a management component to the mortality. Studies show that the mortality may be reduced by the supervision of farrowing or through climate regulation in the farrowing pens. However, this is possible only if the farrowing time is known and thus provides sufficient time for the management to make and execute the decisions. The gestation period of a sow is approximately 115 (SD=2) days. However, an initial cost-benefit analysis recommended increased precision in the prediction of farrowing to make the increased management efforts cost-effective for the pig producer. Recently, a wide range of sensor technology have become available to monitor the behavioural and physiological changes of sow. Evidence show that appropriate utilization of sensor technology may increase the precision of prediction of onset of farrowing. Prediction is feasible only if the prediction is online and automated.
The thesis is focused on constructing a system that can give predictions about the expected time to farrowing of individual sows based on automatic sensor recordings such as water consumption, video based activity measurements, and photo-cells based activity measurements. The warnings could serve to activate the floor heating system to ensure a sufficiently high temperature for the new born piglets, as well as to help the farmer to organize extra surveillance around farrowing. The thesis is based on three submitted manuscripts, describing the system at different stages.
The kernel in the thesis is a Markov process with four subsequent states Before Nest-Building, Nest-Building, Resting and the absorbing state Farrowing; the states were selected based on ethological knowledge about sow behaviour. However, the sojourn time distribution in each state is not exponential. Therefore a continuous time discrete state semi-Markov process based on a Phase-Type distribution (in this case Erlang distributed) was formulated. Finally, the Markov process was transformed to a discrete time process. A Hidden Markov Model (HMM) was used for this process. The model is called Hidden Phase-type Markov Model (HPMM), and the time steps corresponded to each updating with sensor information at which the time to farrowing was predicted.
The POMDP model for optimizing the floor-heat regulation system was used to demonstrate the decision support tool as an extension to the prediction algorithm.
The tools for the prediction of onset of farrowing, estimation of HPMM and optimal decision making provides a framework for handling a large amount of sensor data available and gives an overview of how to integrate information from several sensors on the pen level. The complexity of the models imply that the prediction algorithm and decision tool may be run on the herd level computer; whereas the parameters were estimated on the central level computers.
Dr. Christina Phillips - The Impact of Wean Age and Feeding Program on Nurser...John Blue
The Impact of Wean Age and Feeding Program on Nursery Performance - Dr. Christina Phillips, Director of Production Research, Smithfield, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Erin Harris - Feeding Strategies for Dried Distillers Grain with Solubles...John Blue
Feeding Strategies for Dried Distillers Grain with Solubles with Immunologically Castrated Pigs-Considerations for Producers and Packers - Dr. Erin Harris, University of Minnesota, from the 2014 Allen D. Leman Swine Conference, September 15-16, 2014, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2014-leman-swine-conference-material
Introduction
The mineral content in animal body is 2-5%.
• Most abundant minerals in
body:
– 36-39% Ca (bone ash)
– 17-19% P (bone ash)
Conclusion
STTD Ca requirements for 11 to 25 kg pigs:
– ADG is between 0.36 and 0.56%, G:F is 0.43%
– Bone ash, bone Ca, and bone P is between 0.48 and 0.56%
– Ca retention and P retention is between 0.48 and 0.52%
Enterococci but not E. coli counts in drinking water are positively associate...Michelo Simuyandi
Background and Objective
Studies of the association between faecal indicator bacteria and diarrhoeal disease risk have yielded mixed findings that range from no association to significant associations. We conducted a prospective study collecting repeated measures of water quality and health indicators and related covariates at household level in a peri-urban area south of Lusaka. The aim was to measure the association between the levels of Enterococci and E. coli in household drinking water and self-reported highly credible gastrointestinal illness (HCGI) experienced by household members in the previous seven days.
Methods
We carried out a prospective household based observational study of 290 households which involved household interviews, household observations and testing of household stored drinking water and source water for bacterial indicators of faecal contamination. Residents were interviewed regarding demographics, socio-economic status, drinking water access, treatment and storage, hygiene and sanitation practices, household-level environmental health related exposures, diarrhoea and other gastro-intestinal symptoms. The associations between Enterococci, E. coli, total coliforms in household drinking water and the HCGI experienced by household members were investigated using mixed-effects logistic regression.
Results
Univariate analyses showed Enterococci count was significantly associated with HCGI (OR 26.55 CI: 1.45, 486.04)for unadjusted and (OR 31.33 CI:(2.13, 461.73) for the adjusted. that Log2, but not E. coli or total coliforms,. For every doubling of enterococci count, the odds of HCGI increased by a factor of 1.54 (95% CI :( 1.13, 2.10), p=0.01). Reported treatment of household water (OR 0.41, p<0.01),><0.01) were negatively associated with HCGI, whilst having a place to wash hands near toilet was positively associated with HCGI outcomes (OR 2.12, p=0.03). A positive association of HCGI with age under 5 in the household did not reach statistical significance (OR 1.53, p=0.07.). In a multivariate model, log2 Enterococci count remained significantly associated with HCGI. A doubling of enterococci count increased the odds of HCGI by a factor of 1.67 (95% CI: 1.09, 2.56, p=0.02, after adjustment for water treatment.
Conclusion
In this urban setting, enterococci counts have a stronger association with HCGI than E.coli or total coliform count.
Enterococci but not E. coli counts in drinking water are positively associate...Michelo Simuyandi
Background and Objective
Studies of the association between faecal indicator bacteria and diarrhoeal disease risk have yielded mixed findings that range from no association to significant associations. We conducted a prospective study collecting repeated measures of water quality and health indicators and related covariates at household level in a peri-urban area south of Lusaka. The aim was to measure the association between the levels of Enterococci and E. coli in household drinking water and self-reported highly credible gastrointestinal illness (HCGI) experienced by household members in the previous seven days.
Methods
We carried out a prospective household based observational study of 290 households which involved household interviews, household observations and testing of household stored drinking water and source water for bacterial indicators of faecal contamination. Residents were interviewed regarding demographics, socio-economic status, drinking water access, treatment and storage, hygiene and sanitation practices, household-level environmental health related exposures, diarrhoea and other gastro-intestinal symptoms. The associations between Enterococci, E. coli, total coliforms in household drinking water and the HCGI experienced by household members were investigated using mixed-effects logistic regression.
Results
Univariate analyses showed Enterococci count was significantly associated with HCGI (OR 26.55 CI: 1.45, 486.04)for unadjusted and (OR 31.33 CI:(2.13, 461.73) for the adjusted. that Log2, but not E. coli or total coliforms,. For every doubling of enterococci count, the odds of HCGI increased by a factor of 1.54 (95% CI :( 1.13, 2.10), p=0.01). Reported treatment of household water (OR 0.41, p<0.01),><0.01) were negatively associated with HCGI, whilst having a place to wash hands near toilet was positively associated with HCGI outcomes (OR 2.12, p=0.03). A positive association of HCGI with age under 5 in the household did not reach statistical significance (OR 1.53, p=0.07.). In a multivariate model, log2 Enterococci count remained significantly associated with HCGI. A doubling of enterococci count increased the odds of HCGI by a factor of 1.67 (95% CI: 1.09, 2.56, p=0.02, after adjustment for water treatment.
Conclusion
In this urban setting, enterococci counts have a stronger association with HCGI than E.coli or total coliform count.
Towards identifying novel phenotypes in climate adapted livestock productionSIANI
This presentation was held by Mizeck Chagunda/SRUC at the international seminar 'Livestock Resources for Food Security in the Light of Climate Change' co-hosted by SIANI and SLU Global in Uppsala on the 11th of March 2016.
Dr. Dave Rosero - Influence of Wean Age and Disease Challenge on Progeny Life...John Blue
Influence of Wean Age and Disease Challenge on Progeny Lifetime Performance - Dr. Dave Rosero, The Hanor Company, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Steve Dritz - Latest Field Research and Available Tools to Evaluate and I...John Blue
Latest Field Research and Available Tools to Evaluate and Improve Feed Efficiency - Dr. Steve Dritz, College of Veterinary Medicine, Kansas State University, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
CDD: Vault, CDD: Vision and CDD: Models for Drug Discovery CollaborationsSean Ekins
A talk given at SERMACS 7th Nov 2015 in Memphis, describes CDD Vault, CDD Vision and CDD Models. In addition it also describes how the software is used in large and smaller scale collaborations for drug discovery.
Jordan Hoewischer - OACI Farmer Certification ProgramJohn Blue
OACI Farmer Certification Program - Jordan Hoewischer, Ohio Farm Bureau, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Fred Yoder - No-till and Climate Change: Fact, Fiction, and IgnoranceJohn Blue
No-till and Climate Change: Fact, Fiction, and Ignorance - Fred Yoder, Former President, National Corn Growers Association, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
More Related Content
Similar to Anne Clark - Overview of the Recent Amino Acids Work in Growing Pigs
Dr. Christina Phillips - The Impact of Wean Age and Feeding Program on Nurser...John Blue
The Impact of Wean Age and Feeding Program on Nursery Performance - Dr. Christina Phillips, Director of Production Research, Smithfield, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Erin Harris - Feeding Strategies for Dried Distillers Grain with Solubles...John Blue
Feeding Strategies for Dried Distillers Grain with Solubles with Immunologically Castrated Pigs-Considerations for Producers and Packers - Dr. Erin Harris, University of Minnesota, from the 2014 Allen D. Leman Swine Conference, September 15-16, 2014, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2014-leman-swine-conference-material
Introduction
The mineral content in animal body is 2-5%.
• Most abundant minerals in
body:
– 36-39% Ca (bone ash)
– 17-19% P (bone ash)
Conclusion
STTD Ca requirements for 11 to 25 kg pigs:
– ADG is between 0.36 and 0.56%, G:F is 0.43%
– Bone ash, bone Ca, and bone P is between 0.48 and 0.56%
– Ca retention and P retention is between 0.48 and 0.52%
Enterococci but not E. coli counts in drinking water are positively associate...Michelo Simuyandi
Background and Objective
Studies of the association between faecal indicator bacteria and diarrhoeal disease risk have yielded mixed findings that range from no association to significant associations. We conducted a prospective study collecting repeated measures of water quality and health indicators and related covariates at household level in a peri-urban area south of Lusaka. The aim was to measure the association between the levels of Enterococci and E. coli in household drinking water and self-reported highly credible gastrointestinal illness (HCGI) experienced by household members in the previous seven days.
Methods
We carried out a prospective household based observational study of 290 households which involved household interviews, household observations and testing of household stored drinking water and source water for bacterial indicators of faecal contamination. Residents were interviewed regarding demographics, socio-economic status, drinking water access, treatment and storage, hygiene and sanitation practices, household-level environmental health related exposures, diarrhoea and other gastro-intestinal symptoms. The associations between Enterococci, E. coli, total coliforms in household drinking water and the HCGI experienced by household members were investigated using mixed-effects logistic regression.
Results
Univariate analyses showed Enterococci count was significantly associated with HCGI (OR 26.55 CI: 1.45, 486.04)for unadjusted and (OR 31.33 CI:(2.13, 461.73) for the adjusted. that Log2, but not E. coli or total coliforms,. For every doubling of enterococci count, the odds of HCGI increased by a factor of 1.54 (95% CI :( 1.13, 2.10), p=0.01). Reported treatment of household water (OR 0.41, p<0.01),><0.01) were negatively associated with HCGI, whilst having a place to wash hands near toilet was positively associated with HCGI outcomes (OR 2.12, p=0.03). A positive association of HCGI with age under 5 in the household did not reach statistical significance (OR 1.53, p=0.07.). In a multivariate model, log2 Enterococci count remained significantly associated with HCGI. A doubling of enterococci count increased the odds of HCGI by a factor of 1.67 (95% CI: 1.09, 2.56, p=0.02, after adjustment for water treatment.
Conclusion
In this urban setting, enterococci counts have a stronger association with HCGI than E.coli or total coliform count.
Enterococci but not E. coli counts in drinking water are positively associate...Michelo Simuyandi
Background and Objective
Studies of the association between faecal indicator bacteria and diarrhoeal disease risk have yielded mixed findings that range from no association to significant associations. We conducted a prospective study collecting repeated measures of water quality and health indicators and related covariates at household level in a peri-urban area south of Lusaka. The aim was to measure the association between the levels of Enterococci and E. coli in household drinking water and self-reported highly credible gastrointestinal illness (HCGI) experienced by household members in the previous seven days.
Methods
We carried out a prospective household based observational study of 290 households which involved household interviews, household observations and testing of household stored drinking water and source water for bacterial indicators of faecal contamination. Residents were interviewed regarding demographics, socio-economic status, drinking water access, treatment and storage, hygiene and sanitation practices, household-level environmental health related exposures, diarrhoea and other gastro-intestinal symptoms. The associations between Enterococci, E. coli, total coliforms in household drinking water and the HCGI experienced by household members were investigated using mixed-effects logistic regression.
Results
Univariate analyses showed Enterococci count was significantly associated with HCGI (OR 26.55 CI: 1.45, 486.04)for unadjusted and (OR 31.33 CI:(2.13, 461.73) for the adjusted. that Log2, but not E. coli or total coliforms,. For every doubling of enterococci count, the odds of HCGI increased by a factor of 1.54 (95% CI :( 1.13, 2.10), p=0.01). Reported treatment of household water (OR 0.41, p<0.01),><0.01) were negatively associated with HCGI, whilst having a place to wash hands near toilet was positively associated with HCGI outcomes (OR 2.12, p=0.03). A positive association of HCGI with age under 5 in the household did not reach statistical significance (OR 1.53, p=0.07.). In a multivariate model, log2 Enterococci count remained significantly associated with HCGI. A doubling of enterococci count increased the odds of HCGI by a factor of 1.67 (95% CI: 1.09, 2.56, p=0.02, after adjustment for water treatment.
Conclusion
In this urban setting, enterococci counts have a stronger association with HCGI than E.coli or total coliform count.
Towards identifying novel phenotypes in climate adapted livestock productionSIANI
This presentation was held by Mizeck Chagunda/SRUC at the international seminar 'Livestock Resources for Food Security in the Light of Climate Change' co-hosted by SIANI and SLU Global in Uppsala on the 11th of March 2016.
Dr. Dave Rosero - Influence of Wean Age and Disease Challenge on Progeny Life...John Blue
Influence of Wean Age and Disease Challenge on Progeny Lifetime Performance - Dr. Dave Rosero, The Hanor Company, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
Dr. Steve Dritz - Latest Field Research and Available Tools to Evaluate and I...John Blue
Latest Field Research and Available Tools to Evaluate and Improve Feed Efficiency - Dr. Steve Dritz, College of Veterinary Medicine, Kansas State University, from the 2016 Allen D. Leman Swine Conference, September 17-20, 2016, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2016-leman-swine-conference-material
CDD: Vault, CDD: Vision and CDD: Models for Drug Discovery CollaborationsSean Ekins
A talk given at SERMACS 7th Nov 2015 in Memphis, describes CDD Vault, CDD Vision and CDD Models. In addition it also describes how the software is used in large and smaller scale collaborations for drug discovery.
Jordan Hoewischer - OACI Farmer Certification ProgramJohn Blue
OACI Farmer Certification Program - Jordan Hoewischer, Ohio Farm Bureau, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Fred Yoder - No-till and Climate Change: Fact, Fiction, and IgnoranceJohn Blue
No-till and Climate Change: Fact, Fiction, and Ignorance - Fred Yoder, Former President, National Corn Growers Association, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. John Grove - Fifty Years Of No-till Research In KentuckyJohn Blue
Fifty Years Of No-till Research In Kentucky - Dr. John Grove, Univerity of Kentucky, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Warren Dick - Pioneering No-till Research Since 1962John Blue
Pioneering No-till Research Since 1962 - Dr. Warren Dick, OSU-OARDC (retired), from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Christine Sprunger - The role that roots play in building soil organic ma...John Blue
The role that roots play in building soil organic matter and soil health - Dr. Christine Sprunger, OSU - SENR, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Leonardo Deiss - Stratification, the Role of Roots, and Yield Trends afte...John Blue
Stratification, the Role of Roots, and Yield Trends after 60 years of No-till - Dr. Leonardo Deiss, OSU, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Steve Culman - No-Till Yield Data AnalysisJohn Blue
No-Till Yield Data Analysis - Dr. Steve Culman, OSU Soil Fertility Extension Specialist, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Alan Sundermeier and Dr. Vinayak Shedekar - Soil biological Response to BMPs John Blue
Soil biological Response to BMPs - Alan Sundermeier, OSU Extension, and Dr. Vinayak Shedekar, USDA-ARS, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Curtis Young - Attracting And Protecting PollinatorsJohn Blue
Attracting And Protecting Pollinators - Dr. Curtis Young, OSU Extension, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Sarah Noggle - Cover Crop Decision Tool SelectorJohn Blue
Cover Crop Decision Tool Selector - Sarah Noggle, OSU Extension, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Hemp Regulations - Jim Belt, ODA, Head of Hemp for Ohio, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
John Barker - UAVs: Where Are We And What's NextJohn Blue
UAVs: Where Are We And What's Next - John Barker, OSU Extension, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Rajbir Bajwa - Medical uses of MarijuanaJohn Blue
Medical uses of Marijuana - Dr. Rajbir Bajwa, Coordinator of legal medical marijuana sales, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Jeff Stachler - Setting up a Corn and Soybean Herbicide Program with Cove...John Blue
Setting up a Corn and Soybean Herbicide Program with Cover Crops - Dr. Jeff Stachler, OSU Extension, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Chad Penn - Developing A New Approach To Soil Phosphorus Testing And Reco...John Blue
Developing A New Approach To Soil Phosphorus Testing And Recommendations - Dr. Chad Penn, USDA-ARS, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Jim Hoorman - Dealing with Cover Crops after Preventative PlantingJohn Blue
Dealing with Cover Crops after Preventative Planting - Jim Hoorman, Hoorman Soil Health Services, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Sjoerd Duiker - Dealing with Poor Soil Structure and Soil Compaction John Blue
Dealing with Poor Soil Structure and Soil Compaction - Dr. Sjoerd Duiker, Extension Agronomist, Penn State University, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Christine Brown - Canadian Livestock Producers Efforts to Improve Water QualityJohn Blue
Canadian Livestock Producers Efforts to Improve Water Quality - Christine Brown, Ontario Ministry of Agriculture, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Dr. Lee Briese - Details Matter (includes details about soil, equipment, cove...John Blue
Details Matter (includes details about soil, equipment, cover crops...) - Dr. Lee Briese, North Dakota, 2017 International Crop Adviser of the Year, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Anne Clark - Overview of the Recent Amino Acids Work in Growing Pigs
1. Overview of Recent AA Work
in Growing Pigs
A.B. Clark1*, M.D. Tokach1, S.S. Dritz1, K.J. Touchette2, J.M.
DeRouchey1, R.D. Goodband1, and J.C. Woodworth1
1Kansas State University, Manhattan,
2Ajinomoto Heartland, Inc., Chicago, IL.
2. Background
• Inclusion of synthetic amino acids allows producers to
meet specific requirements while reducing both diet
cost by replacing specialty protein sources.
Current literature lacks:
Research where pigs were fed below their lysine
requirement
The application of best-fitting nonlinear statistical
models
3. Objective
To determine the SID Lys, Val:Lys, and Ile:Lys
requirement for 7 to 11 kg nursery pigs
using best-fitting mixed models.
4. Lysine
300 nursery pigs (PIC 327 × 1050, 21 d of age,
6.7 initial BW)
6 d on a common starter diet
Pens allotted to treatments in a completely
randomized design
6.7 kg initial BW
10 replications/treatment
5 pigs/pen
5. Materials and Methods, Lys Exp.
6 treatment diets increasing in SID Lys:
1.10, 1.20, 1.30, 1.40, 1.50, and 1.60 % SID Lys
NRC requirement – 1.35%, PIC – 1.46%
Diets for 1.10 and 1.60% SID Lys manufactured
and blended at feed mill
All diets were fed in meal form.
With increasing Lys level, soybean meal was
replaced with crystalline amino acids and corn
NRC, 2012. PIC Nutrient Specifications Manual, 2016.
6. Pigs were fed treatment diets for 14 d,
followed by a common diet for 14 d.
Pigs were weighed on d 0, 7, 14, 21, and 28 to
determine ADG, ADFI, and G:F.
Each pen (1.2 × 1.5 m) contained a 4-hole dry,
self-feeder and a nipple waterer for ad libitum
access to feed and water.
Materials and Methods, Lys Exp.
7. PROC GLIMMIX in SAS (SAS Institute, Inc., Cary, NC)
Experimental unit: pen
Results considered significant at P ≤ 0.05 and
tendencies between P > 0.05 and P ≤ 0.10.
Models evaluated:
Quadratic (QP), broken-line linear (BLL), and
broken-line quadratic (BLQ)
A lower Bayesian Information Criterion (BIC)
indicated a better fit.
A decrease in BIC greater than 2.0 among models
for a particular response criterion was considered
an improved fit.
Statistical Analysis, Lys Exp.
Gonçalves et al. (2016)
8. Lys Results, Experimental Period
SID Lys, % P <
Item 1.10 1.20 1.30 1.40 1.50 1.60 SEM Lin Quad
d 0 to 141
ADG, g 265 263 298 313 319 320 10.1 0.001 0.278
ADFI, g 432 417 446 438 441 442 14.6 0.336 0.835
G:F 0.616 0.631 0.670 0.714 0.725 0.729 0.0173 0.001 0.273
F/G 1.64 1.60 1.50 1.40 1.39 1.38 0.040 0.001 0.136
1BW ≈ 6.7 to 10.8
9. Modeling Results: ADG (d 0 to 14; 6.7 to 10.8 kg BW)
Best Fit: BLL and QP Model
BLL Breakpoint: 1.45% [95% CI:1.31, 1.58% ]
QP Maximum: >1.60%, 95% of Max: 1.43
10. Modeling Results: G:F (d 0 to 14; 6.7 to 10.8 kg BW)
Best Fit: BLL and QP Model
BLL Breakpoint: 1.45% [95% CI:1.35, 1.54% ]
QP Maximum: >1.60%, 95% of Max: 1.43
11. Valine
280 nursery pigs (PIC 327 × 1050, 21 d of age)
5 d on a common starter diet
Pens were allotted to treatments according to BW
in a randomized complete block design.
7 treatment diets increasing in SID Val:Lys
50, 57, 63, 68, 73, 78, 85
1.24% SID Lys
NRC requirement – 64%, PIC – 67% SID Val:Lys
12. Val experimental period performance
SID Val:Lys, % P <
Item 50 57 63 68 73 78 85 SEM Lin Quad
d 0 to 141
ADG, g 190 221 249 249 248 251 238 11.2 0.001 0.001
ADFI, g 331 363 394 388 403 390 386 17.2 0.012 0.030
G:F 0.579 0.612 0.635 0.646 0.614 0.645 0.617 0.0189 0.101 0.039
F/G 1.74 1.65 1.60 1.56 1.63 1.56 1.64 0.050 0.084 0.036
1BW ≈ 6.5 to 9.8
13. Modeling Results: ADG (d 0 to 14; 6.5 to 9.8 kg BW)
Best Fit: Broken-Line Linear Model
Breakpoint: 62.9% SID Val:Lys
14. Modeling Results: ADFI (d 0 to 14; 6.5 to 9.8 kg BW)
Best Fit: QP Model
Maximum: 73.7% SID Val:Lys, 99% of Max: 68.0%
15. Modeling Results: G:F (d 0 to 14; 6.5 to 9.8 kg BW)
Best Fit: Quadratic Polynomial Model
Max Performance: 71.7% SID Val:Lys, 99% of Max: 64.4%
16. Isoleucine
280 nursery pigs (PIC 327 × 1050, 21 d of age)
6 d on a common starter diet
12 d on experimental diets
Pens allotted to treatments according to BW/location in
a randomized complete block design
7 treatment diets increasing in SID Ile:Lys
40, 44, 48, 52, 54, 58, 63
NRC requirement – 52%, PIC – 55%
17. Ile experimental period performance
SID Ile:Lys, % P <
Item 40 44 48 52 54 58 63 SEM Lin Quad
d 0 to 121
ADG, g 330 344 342 388 344 358 375 11.3 0.001 0.446
ADFI, g 495 524 546 601 522 574 555 16.6 0.002 0.017
G:F 0.669 0.657 0.628 0.648 0.658 0.625 0.676 0.0152 0.900 0.041
F/G 1.50 1.53 1.60 1.55 1.52 1.61 1.49 0.037 0.812 0.059
1BW ≈ 6.8 to 11.0
18. Modeling Results: ADG (d 0 to 14; 6.8 to 11.0 kg BW)
Best Fit: QP, BLL and BLQ Models
BLL Breakpoint: 52.0% [95% CI:51.96, 52.04 % ]
BLQ Breakpoint: 52.0%, [95% CI:51.97, 52.03]
QP Maximum: 64.7%, 99% of Max: 57.0
19. Modeling Results: ADFI (d 0 to 14; 6.8 to 11.0 kg BW)
Best Fit: QP and BLL Model
QP Maximum: 56.2 % SID Ile:Lys, 99% of Max: 51.6%
BLL Breakpoint: 50.6% SID Ile:Lys, [41.99, 59.15%]
20. Summary
Results heavily dependent on the model and criteria
evaluated
SID Lys – 1.45 to 1.60%
SID Val:Lys – 62.9 to 71.7%
SID Ile:Lys – 52.0 to 64.7%
Consider maximum of response vs 95 or 99% of max
Generating response surfaces allows producers to
make decisions based on ingredient nutrient
composition, availability, and economics