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Metabolic-digestive clinical disorders of lactating dairy cows were associated with alterations of rumination, physical activity, and lying behavior monitored by an ear-attached sensor
Rial, CLara (Journal of Dairy Science, 2023-06-28)
The objective of this observational cohort study was to characterize the pattern of rumination time (RT), physical activity (PA), and lying time (LT) monitored by an automated health monitoring system, based on an ear-attached sensor, immediately before, during, and after clinical diagnosis (CD) of metabolic-digestive disorders. Sensor data were collected from 820 lactating Holstein cows monitored daily from calving up to 21 DIM for detection of health disorders (HD). Cows were grouped retrospectively in the no-clinical health disorder group (NCHD; n = 616) if no HD were diagnosed, or the metabolic-digestive group (METB-DIG; n = 58) if diagnosed with clinical ketosis or indigestion only. Cows with another clinical health disorder within -7 to +7 d of CD of displaced abomasum, clinical ketosis, or indigestion were included in the metabolic-digestive plus one group (METB-DIG+1; n = 25). Daily RT, PA, and LT, and absolute and relative changes within -7 to +7 d of CD were analyzed with linear mixed models with or without repeated measures. Rumination time and PA were smaller, and LT was greater for the METB-DIG and METB-DIG+1 group than for cows in the NCHD group for most days from -7 to +7 d of CD of HD. In general, daily RT, PA, and LT differences were larger between the METB-DIG+1 and NCHD groups than between the METB-DIG and NCHD groups. In most cases, RT and PA decreased to a nadir and LT increased to a peak immediately before or after CD of HD, with a return to levels similar to the NCHD group within 7 d of CD. Absolute values and relative changes from 5 d before CD to the day of the nadir for RT and PA or peak for LT were different for cows in the METB-DIG and METB-DIG+1 group than for the NCHD group. For PA, the METB-DIG+1 group had greater changes than the METB-DIG group. For cows affected by metabolic-digestive disorders, RT, PA, and LT on the day of CD and resolution of clinical signs were different than for cows in the NCHD group, but an increase in RT and PA or a decrease in LT was observed from the day of CD to the day of resolution of clinical signs. We conclude that dairy cows diagnosed with metabolic-digestive disorders including displaced abomasum, clinical ketosis, and indigestion presented substantial alterations in the pattern of RT, PA, and LT captured by an ear-attached sensor. Thus, automated health monitoring systems based on ear-attached sensors might be used as an aid for identifying cows with metabolic-digestive disorders. Moreover, RT, PA, and LT changes after CD might be positive indicators of recovery from metabolic-digestive disorders.
Chronic Wasting Disease Surveillance Optimization Software (n x 2)
Hanley, Brenda J.; Mitchell, Corey I.; Them, Cara E.; Walter, W. David; Walsh, Daniel P.,; Jennelle, Christopher S.; Hollingshead, Nicholas A.; Abbott, Rachel C.; Kelly, James D.; Grove, Daniel M.; Williams, David; Christensen, Sonja A.; Ahmed, Md Sohel; Booth, James G.; Guinness, Joseph; Gagne, Roderick B.; DiSalvo, Andrew R.; Fleegle, Jeannine T.; Rosenberry, Christopher S.; Miller, Landon A.; Schuler, Krysten L. (2023-09-22)
The Chronic Wasting Disease Surveillance Optimization Software (n x 2) computes sampling recommendations for state, tribal, or provincial wildlife management agencies when the goal of the disease surveillance program is to detect chronic wasting disease (CWD) in white-tailed deer (Odocoileus virginianus). Driven by a combinatorial optimization algorithm, the Software pinpoints the number of surveillance points that should be evaluated in each county (or administrative area) to maximize the return-on-investment of sampling to the agency, while staying within the predetermined annual CWD surveillance budget. Agency representatives parameterize their Software with their total annual budget, weightings for specific management objectives, a summary of the historical sampling data, per-deer sampling costs, benefits of first and early detections, risk of spread, and benefits of an ad hoc sampling strategy. Outputs include the set of counties that should be sampled in the upcoming surveillance season. We designed the Software for use in Tennessee, US, but included directions for other states or provinces.
Surveillance Benefit Components for Chronic Wasting Disease in White-Tailed Deer
Them, Cara E.; Mitchell, Corey I.; Hollingshead, Nicholas A.; Abbott, Rachel C.; Hanley, Brenda J.; Ahmed, Md Sohel; Booth, James G.; Jennelle, Chris S.; Hodel, Florian H.; Guinness, Joe; Ballard, Jennifer R.; Riggs, A. J.; Middaugh, Christopher R.; Cunningham, Mark; Clemons, Bambi; Sayler, Katherine; Killmaster, Charlie H.; Harms, Tyler M.; Ruden, Rachel M.; Caudell, Joe; Westrich, Michelle Benavidez; McCallen, Emily; Casey, Christine; O’Brien, Lindsey M.; Trudeau, Jonathan K.; Straka, Kelly; Stewart, Chad; Carstensen, Michelle; McKinley, William T.; Hynes, Kevin P.; Ableman, Ashley; Miller, Lauren A.; Cook, Merril; Meyers, Ryan; Shaw, Jonathan; Van de Berg, Sarah; Tonkovich, Michael J.; Nituch, Larissa; Kelly, James D.; Grove, Daniel M.; Storm, Daniel J.; Schuler, Krysten L. (2023-09-22)
The Surveillance Benefit Components for Chronic Wasting Disease in White-Tailed Deer is multivariable data representing epidemiological, population, ecological, and anthropogenic attributes of chronic wasting disease (CWD) in wild, white-tailed deer (Odocoileus virginianus) in the region of the United States (US) containing the states of Arkansas, Florida, Georgia, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, New York, North Carolina, Ohio, Tennessee, and Wisconsin, and in the region of Canada containing the province of Ontario. The data was made available through state and provincial wildlife agencies in partnership with the Surveillance Optimization Project for Chronic Wasting Disease (SOP4CWD), administered by the Cornell Wildlife Health Lab (CWHL) at Cornell University and Boone and Crockett Quantitative Wildlife Center at Michigan State University. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Apply Immuno-FISH in Zea Maize
Wen, Xin (2023)
The process of meiosis, a fundamental mechanism in plant sexual reproduction, orchestrates the creation of germ cells with genetically diverse homologous chromosomes, primarily through the intricate event of crossing-over (CO). CO plays a pivotal role in promoting genetic variation by enabling the exchange of genetic material among chromosomes. Despite DNA Double Strand Breaks (DSBs) initiating chromosome recombination, not all DSB events culminate in COs. This study seeks to revolutionize the CO process in plants to expedite plant breeding and produce novel genotypes harboring traits unattainable through traditional methods. In pursuit of this goal, transgenic Zea mays lines have been developed, introducing a bespoke recombinant protein, Cas9:SPO11. This engineered protein is adept at guiding DSBs to precise chromosome regions during meiosis, imparting a degree of control over the recombination landscape. Our hypothesis posits that these targeted regions, under the influence of Cas9:SPO11, experience heightened recombination rates compared to their wild-type counterparts. The innovation lies in the potential to channel recombination events towards desired genomic loci, thereby creating new avenues for directed trait incorporation. The present study is poised to investigate the anticipated increase in CO events at the designated target loci in the transgenic lines when contrasted with the natural recombination pattern of wild-type maize. This investigation hinges on a multimodal approach, combining the power of Immuno-Fluorescent in situ Hybridization (Immuno-FISH) with protein immunolabeling. These techniques afford visualization of chromosome dynamics, elucidating the spatial and temporal aspects of the recombination process. The research aligns with a significant body of literature emphasizing the indispensability of CO in generating genetic diversity and facilitating adaptive evolution. Moreover, the role of DSBs as initiators of recombination events has been underscored by studies in various organisms, including model plant species. The exploration of CRISPR-Cas9 technology in the context of enhancing recombination dynamics is an emerging avenue, with diverse applications ranging from basic research to agricultural innovation. As the scope of plant breeding broadens to address contemporary challenges, such as climate resilience and nutritional enhancement, the ability to strategically modulate recombination events offers an enticing proposition. The outcomes of this study could revolutionize crop improvement strategies, allowing for the accelerated development of plant varieties with desirable traits. Ultimately, this research underscores the potential of leveraging genetic recombination mechanisms to usher in a new era of precision agriculture.
Automatic Segmentation of Crops in UAV Images
Zheng, Yuanyuan (2023)
Remote sensing imagery has been increasingly utilized in agricultural production due to its convenience and cost-effectiveness. However, traditional methods for crop segmentation require significant time and manual effort. Therefore, this research proposed the use of threshold segmentation and deep learning techniques to achieve automatic crop segmentation in UAV images and evaluated their performance. Specifically, this research utilized image threshold segmentation, a custom UNet network, Deeplabv3+ and segment anything model(SAM) with multiple prompts. The results showed that the Intersection over Union (IoU) for threshold segmentation was 0.58. The IoU for UNet was 0.70, and for DeepLabV3+ it was 0.76. The IoU achieved by SAM with points prompt was 0.89, demonstrating superior crop segmentation performance. However, the masks generated using SAM automatic mask generation and a bounding box with a point prompt couldn’t segment crops effectively.