Women’s Soccer Clips South Dakota State, 1-0, For First Win of 2019

first_img Live Stats Story Links Next Game: UMKC 9/8/2019 – 1 PM Preview Watch Live ESPN3center_img Drake’s first goal came courtesy of a well-executed set piece started by junior goalkeeper Kelsie Stone. After a foul by the Jackrabbits, Stone delivered a booming free kick from midfield that Bormann headed far post for the Bulldogs’ first goal of the season coming in the 61st minute (60:15). Box Score (PDF) Full Schedule Roster DES MOINES, Iowa – The Drake University women’s soccer team earned its first win of the 2019 season with a 1-0 victory over South Dakota State Friday, Sept. 6 evening at the Cownie Soccer Complex. Junior Hannah Bormann scored the first goal of the season for Drake (1-4), which hosted its annual Kick Cancer event.”We weren’t great tonight, but we did enough to get the shutout,” Drake head coach Lindsey Horner said. “Vanessa Kavan and Audrey Vidmar were fantastic for us. Hannah Bormann was a spark and great to have back on the field, and Ali Burke gave us a solid shift in the midfield.” Stone notched three saves in the first half, including two in quick succession in the 35th and 37th minutes, to stave off the South Dakota State (2-3) attack. She finished with seven saves in the victory.”We did a good job of keeping South Dakota State’s forward pair in front of us, but when the game became frantic as they pushed numbers forward for the equalizer, we didn’t manage the lead as well as we would have liked,” Horner said. “Kelsie’s worked at her distribution and range, so that was exciting for her to tally an assist.”The night was extra special for the Bulldogs as they honored their special family member Breslyn pregame. Breslyn became a part of the Drake women’s soccer family in 2018 as part of the Friends of Jaclyn Foundation. She was diagnosed with leukemia at age 3, but rang the bell in 2018 signaling her final chemo treatment. Breslyn and her family founded Breslyn’s Blankets of Love to help other children in their fight against cancer. For more information on their foundation, visit breslynsblanketsoflove.com.”I’m really pleased that we earned a result on a night we were playing for Breslyn,” Horner said. Print Friendly Versionlast_img read more

Scientists create new method of particle tracking based on machine learning

first_imgReviewed by James Ives, M.Psych. (Editor)Aug 24 2018Scientists at the University of North Carolina at Chapel Hill have created a new method of particle tracking based on machine learning that is far more accurate and provides better automation than techniques currently in use.Single-particle tracking involves tracking the motion of individual particles, such as viruses, cells and drug-loaded nanoparticles, within fluids and biological samples. The technique is widely used in both physical and life sciences. The team at UNC-Chapel Hill that developed the new tracking method uses particle tracking to develop new ways to treat and prevent infectious diseases. They examine molecular interactions between antibodies and biopolymers and characterize and design nano-sized drug carriers. Their work is published in the Proceedings of the Nationals Academy of Scientists.”In order to derive meaning from videos, you have to convert the videos into quantitative data,” said Sam Lai, Ph.D., an associate professor in the UNC Eshelman School of Pharmacy and one of the creators of the new tracker. “With current software, researchers must carefully supervise the video conversion to ensure accuracy. This often takes many weeks to months, and greatly limit both the throughput and accuracy.”We got tired of the bottleneck.”The root of the problem can be traced to the small number of parameters, such as particle size, brightness and shape, used by current software to identify the full range of particles present in any video. Things get missed because they don’t quite fit the parameters, and the parameters vary as different operators set them, Alison Schaefer, a Ph.D. student in the Lai lab, said. This creates a tremendous challenge with data reproducibility, as two users analyzing the same video frequently obtain different results.”Self-driving cars work because they can see and keep track of many different objects around them in real time,” said M. Gregory Forest, Ph.D., the Grant Dahlstrom Distinguished Professor in the UNC Departments of Mathematics and Applied Physical Sciences, and co-senior author on the project.”We wondered if we could create a version of that kind of artificial intelligence that could track thousands of nanoscale particles at one time and do it automatically.”Related StoriesArtificial intelligence can help accurately predict acute kidney injury in burn patientsAI coach feasible and useful for behavioral counseling of teens in weight-loss programArtificial intelligence can be used to efficiently diagnose rare diseasesAs it turns out, they could and used their discovery to launch Chapel Hill-based AI Tracking Solutions, which is seeking to commercialize the new technology. The company has received a Small Business Technology Transfer award from the National Institutes of Health to commercialize the technology.Lai and his collaborators in the UNC Department of Mathematics designed an artificial neural network to go to work on their problem. Neural networks are loosely based on the human brain but learn by being fed a large number of examples. For example, if a neural network needs to recognize photos of dogs, it is shown lots of photos of dogs. It doesn’t need to know what a dog looks like; it will figure that out from the common elements of the photographs. The better the examples, the better the neural network will be.The UNC team first taught the neural network tracker from a truth set of computer-generated data. They then further refined the tracker using high-quality data from past experiments conducted in Lai’s lab. The result was a new tracker with thousands of well-tuned parameters that can process a highly diverse range of videos fully automatically, is at least 10 times more accurate than systems currently in use, is highly scalable, and possesses perfect reproducibility, Lai said. The team documented their achievement in the Proceedings of the National Academy of Sciences.The new system is ready just in time to support the increasing availability of powerful microscopes capable of collecting terabytes of high resolution 2D and 3D video in a single day, said Jay Newby, Ph.D., lead author of the study and an assistant professor at the University of Alberta.”Tracking the movement of nanometer-scale particles is critical for understanding how pathogens breach mucosal barriers and for the design of new drug therapies,” Newby said. “Our advancement provides, first and foremost, substantially improved automation. Additionally, our method greatly improves accuracy compared with current methods and reproducibility across users and laboratories.” Source:https://pharmacy.unc.edu/news/2018/08/23/unc-builds-better-particle-tracking-software-using-artificial-intelligence/last_img read more