This research studies Chinese students' choice of transnational higher education in the context of the higher education market. Through a case study of the students in the transnational higher education programs of W University, the research finds that Chinese students' choice of transnational higher education is a complicated decisionmaking that is influenced by push factors related to domestic higher education and overseas higher education, pull factors related to transnational higher education and student's characteristics such as economic condition, academic aptitude, and future plans. Compared with domestic higher education and overseas higher education, transnational higher education is a second choice. It is being used by the majority of students as a tool to regain access to high-quality domestic higher education institutions and to gain access to overseas higher education. Currently, there exist information gaps between students and transnational higher education programs, which prevent students from making an informed decision when they choose a particular program.

Polyphenol oxidase (PPO) from jackfruit bulb was purified through acetone precipitation, ion-exchange column, and gel filtration column. PPO was a dimer with the molecular weight of 130 kDa determined by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and gel filtration. The Km was 8.3 and 18.2 mM using catechol and 4-methylcatechol as substrates, respectively. The optimum pH was 7.0 (catechol as the substrate) or 6.5 (4-methylcatechol as the substrate). The optimum temperature was 8 °C. The enzyme was stable below 40 °C. The activation energy (Ea) of heat inactivation was estimated to be 103.30 kJ/mol. The PPO activity was activated by Mn(2+), SDS, Tween-20, Triton X-100, citric acid, and malic acid but inhibited by K(+), Zn(2+), Mg(2+), Ca(2+), Ba(2+), cetyl trimethyl ammonium bromide (CTAB), kojic acid, tropolone, glutathione (GSH), cysteine (Cys), and ascorbic acid (AA). Cys and AA were effective to reduce browning of jackfruit bulbs during the storage at 8 °C for 15 days.

Detection and characterization of rare circulating tumor cells (ctcs) in patients' blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of ctcs from patient blood samples, by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network, a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and ctcs. it requires minimal data pre-processing and has an easy experimental setup. through our experiments, we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying ctcs. With more data becoming available in the future, the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for ctc analysis. Circulating tumor cells (CTCs) found in peripheral blood are originated from solid tumors. They are cells shed by a primary tumor into the vasculature, circulating through bloodstream of cancer patients, and colonizing at distant sites which may form metastatic tumors 1. CTCs are an important biomarker for early tumor diagnosis and early evaluation of disease recurrence and metastatic spread in various types of cancer 2-6. Early detection of CTCs provides high chances for patients to survive before severe cancer growth occurs 7. The CTC count is also an important prognostic factor for patients with metastatic cancer 8-12. For example, a study has shown that the number of CTCs is an independent predictor of survival in patients for breast cancer and prostate cancer 8-10 , and the changes of the CTC count predict the survival in patients for lung cancer 12. However, the identification of the CTCs population is a challenging problem. Various approaches to identifying and isolating CTCs including antibody-based methods and physical-characteristics-based methods have been developed 13-19. This task is difficult because of the low concentration of CTCs existing in a patient's peripheral blood-a few CTCs out of 10 billion blood cells 20,21 , as well as heterogeneity in the characteristics of CTCs 22,23. For example, the mechanism of CTCs maintaining metastatic potential during circulating is not well understood 24 ; CTCs derived from some patients allow a cell line to be established, but CTCs from some others lose the capability of proliferation after a few hours of blood drawing 13. Therefore, the incapability to draw a large volume of blood from patients leads to the need for improvements of CTC isolation methods so that CTCs can be detected in small sample volumes. Further, the inconsistency in the viability of CTCs hinders further explorati...

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