Pig as a Favorable Animal for Taenia Saginata Asiatica Infection

The epidemiology of Taenia saginata in some parts of Asia is confusing, in that beef does not appear to be the source of infection. In some areas, beef is either not available or not eaten raw, whereas pork at times is eaten uncooked. In light of this situation, we have exposed pigs and other animals to infection with strains of T. saginata to establish their ability to serve as intermediate hosts. Eggs of Taiwan Taenia, Korea Taenia, Indonesia Taenia, Thailand Taenia, Philippines Taenia, Ethiopia Taenia, and Madagascar Taenia were fed to 83 pigs of three strains: 43 Small-Ear Miniature (SEM), 34 Landrace Small-Ear Miniature (L-SEM), and 6 Duroc-Yorkshire-Landrace (DYL). We also fed the eggs to 10 Holstein calves, 17 Sannean goats, and 4 monkeys (Macaca cyclopis). We succeeded in infecting SEM (infection rate 88%, cysticercus recovery rate 19.1%), L-SEM (83%, 1.1%), and DYL (100%, 0.3%) pigs with Taiwan Taenia; SEM (100%, 1.7%), L-SEM (100%, 5.6%), and DYL (100%, 0.06%) pigs with Korea Taenia; SEM (100%, 22%) and L-SEM (100%, 1.6%) pigs with Indonesia Taenia; SEM (75%, 0.06%) pigs with Thailand Taenia SEM (100%, 11%) pigs with Philippines Taenia; SEM (80%, 0.005%) pigs with Ethiopia Taenia; SEM (100%, 0.2%) pigs with Madagascar Taenia. Holstein calves became infected with Taenia from Taiwan (100%, 1.1%), Korea (100%, 0.03%), Thailand (100%, 0.2%), and the Philippines (100%, 6%); however, the cysticerci of Taenia from Korea, Thailand, and the Philippines were degenerated and/or calcified. Sannean goats became infected with Taenia from Taiwan (33%, 0.01%) and Korea (50%, 0.02%), while monkeys became infected with Taenia from Taiwan (50%, 0.01%). However, the cysticerci were degenerated and/or calcified. Therefore, these strains of pig seem to be favorable animal models for experimental studies of T. saginata-like tapeworms, with the SEM pig the most favorable

Linear Displacement and Straightness Measurement by Fabry-Perot Interferometer Integrated with an Optoelectronic Module

This research develops a three degrees of freedom (DOF) measurement system by integrating Fabry-Perot interferometer and photoelectronic inspection module to determine linear displacement, horizontal and vertical straightness geometric error parameters simultaneously. The interferometer and the photoelectronic inspection module in a three DOF measurement system share the same light source, and the two structures are used to measure linear displacement and straightness errors. The experimental results are utilized to calculate the relevant error parameters according to ISO standards and numerical analysis. They show that after the machine error compensation, the positioning deviation of the system is reduced from 55 μm to 19 μm, corresponding to the reduction of 65%. The accuracy is promoted from 65 μm to 31 μm, about the improvement of 52%. The horizontal and vertical straightness errors of the machine are 4.30 μm and 5.71 μm respectively

Leveling Maintenance Mechanism by Using the Fabry-Perot Interferometer with Machine Learning Technology

This study proposes a method for maintaining parallelism of the optical cavity of a laser interferometer using machine learning. The Fabry-Perot interferometer is utilized as an experimental optical structure in this research due to its advantage of having a brief optical structure. The supervised machine learning method is used to train algorithms to accurately classify and predict the tilt angle of the plane mirror using labeled interference images. Based on the predicted results, stepper motors are fixed on a plane mirror that can automatically adjust the pitch and yaw angles. According to the experimental results, the average correction error and standard deviation in 17-grid classification experiment are 32.38 and 11.21 arcseconds, respectively. In 25-grid classification experiment, the average correction error and standard deviation are 19.44 and 7.86 arcseconds, respectively. The results show that this parallelism maintenance technology has essential for the semiconductor industry and precision positioning technology

Computing Thresholds of Linguistic Saliency

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

The association between problematic cellular phone use and risky behaviors and low self-esteem among Taiwanese adolescents

<p>Abstract</p> <p>Background</p> <p>Cellular phone use (CPU) is an important part of life for many adolescents. However, problematic CPU may complicate physiological and psychological problems. The aim of our study was to examine the associations between problematic CPU and a series of risky behaviors and low self-esteem in Taiwanese adolescents.</p> <p>Methods</p> <p>A total of 11,111 adolescent students in Southern Taiwan were randomly selected into this study. We used the Problematic Cellular Phone Use Questionnaire to identify the adolescents with problematic CPU. Meanwhile, a series of risky behaviors and self-esteem were evaluated. Multilevel logistic regression analyses were employed to examine the associations between problematic CPU and risky behaviors and low self-esteem regarding gender and age.</p> <p>Results</p> <p>The results indicated that positive associations were found between problematic CPU and aggression, insomnia, smoking cigarettes, suicidal tendencies, and low self-esteem in all groups with different sexes and ages. However, gender and age differences existed in the associations between problematic CPU and suspension from school, criminal records, tattooing, short nocturnal sleep duration, unprotected sex, illicit drugs use, drinking alcohol and chewing betel nuts.</p> <p>Conclusions</p> <p>There were positive associations between problematic CPU and a series of risky behaviors and low self-esteem in Taiwanese adolescents. It is worthy for parents and mental health professionals to pay attention to adolescents' problematic CPU.</p

THE LEARNABILITY OF UNKNOWN QUANTUM MEASUREMENTS

Abstract Machine Learning (ML) , which are randomly and independently drawn from some measure µ on X × Y. The main focuses of ML are: (i) computational complexity which measures the efficiency of a learning algorithm; (ii) sample complexity which determines the number of queries to a membership made by the learning algorithm such that the hypothesis function is Probably Approximately Correct , is one of the most popular figures of merit in ML because it indicates how well the training set can approximate the input space under the function f . Eventually, we are interested in whether a quantity, denoted as m F ( , δ), exists such that given n ≥ m F ( , δ), for every 0 &lt; , δ &lt; 1 and any probability measure µ, This quantity m F ( , δ) is called the sample complexity of the hypothesis set F with accuracy and confidence δ. One of the biggest achievements in ML Quantum Information Processing (QIP) has achieved significant breakthroughs recently Any quantum statistical learning problem can be similarly formulated as that in the first paragraph; namely, the input and output space X and Y could be any subset of C d×d (instead of R d in classical ML), and the hypothesis set F could contain matrix-valued functions. Such a generalization encompasses all system models in previous works . Under this framework, we consider the problem of learning an unknown quantum measurement, and we mainly focus on learning a two-outcome measurement. For multi-outcome POVMs, the results can be easil