A SURVEY ON HORSE USE AND MANAGEMENT IN HORSE CLUBS IN CHINA: A PILOT STUDY

During the past three decades, significant development of the Chinese equine industry has resulted in a rapid increase in the number of horses in the country. At the same time, many horse clubs face shortages of trained management personnel or specialists in equine science and technology. The objective of this survey was to document and evaluate horse management and use in Chinese horse clubs. A 29-question survey was developed using SurveyMonkey© and tested on a small pilot group before being revised and released to a general pool of participants. The questionnaire link was distributed through WeChat to specific personnel in selected Chinese horse clubs known to researchers. These personnel were asked to help distribute the questionnaire to other horse clubs. The survey was open for 8 wk, and reminders were sent out regularly. Twenty clubs completed the survey, with 11 (57.9%) representing eastern and northern parts of China. The oldest reporting club was established in 2002, while the newest opened in 2018. Fourteen of the clubs were membership based and reported providing services for 40-1000 members (mean 260) and 200-10,000 visitors in a typical year. A total of 1703 horses were reported, and horses under 15 yrs of age represented 84.6% (n=1449) of total horses. Only 1.8% of horses were older than 20 yrs. The ages of the oldest horses in a given club ranged from 10 to 25 years. Warmbloods made up the greatest number of imported breeds (30.8%), with Thoroughbreds and Arabians being the next most popular (17.9% and 15.4%, respectively). Mongolian horses (29.4%) were the most common indigenous breed. Major horse health problems included hoof-related issues (31.6%) and injuries (31.6%). Colic was reported as an issue in 23.7% of horse clubs. Four clubs reported no turnout space available, and only 5 clubs (20%) noted having access to turnout areas with grass. Turnout size ranged from 50 to 30,000 m2. Horses were turned out for 0-6 hr/d and 0-7 d/wk. Hay constituted the major volume of feed for horses, with concentrates and supplements being fed for multiple reasons. As in the United States, the majority of horses were used for recreation (20.45%). Other uses included breeding (17.46%), dressage (15.25%), jumping (10.29%), endurance (1.64%) and barrel racing (1.18%). Veterinarians, farriers, and nutritionists were the most needed skills, with training, stable management, coaches and riders following. Veterinaries and farriers were more often employed full-time by clubs, while dentists were usually part-time employees. Most (55%) respondents reported having nearly 40% of riders with certificate or degrees associated with equine science. The most common reason for a horse to leave a club was due to being sold, though no reason for the sale was collected. The months horses were most often used was between July and August, which corresponds with summer holidays. Horses were used between 4-7 d/wk during heaviest use. Daily logs were used in 40% of clubs to track horse use, and 36.7% reported having a single person in charge to prevent overuse of horses. Interestingly, 3 clubs (10%) reported having “no such situation” relative to overuse of horses. Although 38.8% of employees reportedly held a certificate or degree associated with equine science, foreign specialists were often employed to support club activities, including teaching general riding (42.9%) and dressage (21.4%). Over 26% came from France. Nearly 53% of respondents took part in events from 0-5 times a year. Data from this study can serve as a platform for future surveys and to begin development of education and training programs to improve horse management in China

Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks

Protein secondary structure prediction is an important problem in bioinformatics. Inspired by the recent successes of deep neural networks, in this paper, we propose an end-to-end deep network that predicts protein secondary structures from integrated local and global contextual features. Our deep architecture leverages convolutional neural networks with different kernel sizes to extract multiscale local contextual features. In addition, considering long-range dependencies existing in amino acid sequences, we set up a bidirectional neural network consisting of gated recurrent unit to capture global contextual features. Furthermore, multi-task learning is utilized to predict secondary structure labels and amino-acid solvent accessibility simultaneously. Our proposed deep network demonstrates its effectiveness by achieving state-of-the-art performance, i.e., 69.7% Q8 accuracy on the public benchmark CB513, 76.9% Q8 accuracy on CASP10 and 73.1% Q8 accuracy on CASP11. Our model and results are publicly available.Comment: 8 pages, 3 figures, Accepted by International Joint Conferences on Artificial Intelligence (IJCAI

Computational Discovery of A New Rhombohedral Diamond Phase

We identify by first-principles calculations a new diamond phase in R¯3c (D63d) symmetry, which has a 16-atom rhombohedral primitive cell, thus termed R16 carbon. This rhombohedral diamond comprises a characteristic all-sp3 six-membered-ring bonding network, and it is energetically more stable than previously identified diamondlike six-membered-ring bonded BC8 and BC12 carbon phases. A phonon mode analysis verifies the dynamic structural stability of R16 carbon, and electronic band calculations reveal that it is an insulator with a direct band gap of 4.45 eV. Simulated x-ray diffraction patterns provide an excellent match to recently reported distinct diffraction peaks found in milled fullerene soot, suggesting a viable experimental synthesis route. These findings pave the way for further exploration of this new diamond phase and its outstanding properties