Effects of hydraulic pressure on cardiomyoblasts in a microfluidic device

We employed a microfluidic device to study the effects of hydraulic pressure on cardiomyoblast H9c2. The 170 mm Hg pressure increased the cellular area and the expression of atrial natriuretic peptide. With the same device, we demonstrated that the effects of hydraulic pressure on the cardiomyoblast could be reduced by the inhibitor of focal adhesion kinase. This mechanical–chemical antagonism could lead to a potential therapeutic strategy of hypertension-induced cardiac hypertrophy.     

An integrated microfluidic system for screening of phage-displayed peptides specific to colon cancer cells and colon cancer stem cells

Affinity reagents recognizing biomarkers specifically are essential components of clinical diagnostics and target therapeutics. However, conventional methods for screening of these reagents often have drawbacks such as large reagent consumption, the labor-intensive or time-consuming procedures, and the involvement of bulky or expensive equipment. Alternatively, microfluidic platforms could potentially automate the screening process within a shorter period of time and reduce reagent and sample consumption dramatically. It has been demonstrated recently that a subpopulation of tumor cells known as cancer stem cells possess high drug resistance and proliferation potential and are regarded as the main cause of metastasis. Therefore, a peptide that recognizes cancer stem cells and differentiates them from other cancer cells will be extremely useful in early diagnosis and target therapy. This study utilized M13 phage display technology to identify peptides that bind, respectively, to colon cancer cells and colon cancer stem cells using an integrated microfluidic system. In addition to positive selection, a negative selection process was integrated on the chip to achieve the selection of peptides of high affinity and specificity. We successfully screened three peptides specific to colon cancer cells and colon cancer stem cells, namely, HOLC-1, HOLC-2, and COLC-1, respectively, and their specificity was measured by the capture rate between target, control, and other cell lines. The capture rates are 43.40 ± 7.23%, 45.16 ± 7.12%, and 49.79 ± 5.34% for colon cancer cells and colon cancer stem cells, respectively, showing a higher specificity on target cells than on control and other cell lines. The developed technique may be promising for early diagnosis of cancer cells and target therapeutics.     

“What would you like me to do? Lie to you?” Teacher education responsibilities to LGBTI students

The experiences of lesbian, gay, trans (The use of trans with an asterisk avoids the use of transsexual or transgender and promotes recognition of the inadequacy of such labels), bisexual and intersex (LGBTI) student teachers were recently investigated at a New Zealand faculty of education. Student teachers studying in early childhood education and care, primary and secondary initial teacher education (ITE) were asked about their perceptions of LGBTI visibility and inclusion. Methods used were online questionnaires, focus groups and individual interviews. While the study encompassed all aspects of the ITE programme, this work uses one specific question about practicum from the questionnaire, and findings related to practicum and teaching from the focus groups and individual interviews. In this article, the experiences of LGBTI (those who identified as non-heterosexual) student teachers and “straight” (those who identified as heterosexual) are discussed. Findings suggest that both faculty and practicum settings are heteronormative and indicate that LGBTI student teachers felt uncertain about their safety. Both LGBTI and straight students felt they had not been given adequate preparation to manage the complexities of diverse sexualities on practicum or in their future teaching. We argue that addressing heteronormativity in ITE will better prepare student teachers for the rich diversity of students and families they will encounter in their teaching.     

DISTRIBUTIVE PARTITIONING OPERATION IN MATHEMATICAL SITUATIONS INVOLVING FRACTIONAL QUANTITIES

In the present study, we describe a participating student’s (Carol’s) distributive partitioning scheme and operations along with Steffe’s and his colleagues’ studies about children’s constructions of fraction knowledge as a particular model of mathematical learning. Analysis of Carol’s mathematical behaviors indicates that an operationally common mathematical behavior (distributive partitioning operation) was revealed in various mathematical problem situations such as fraction multiplication, fraction division, and multiplicative transformation between fractional quantities. It both provides a rationale for why becoming versed in one mathematical subject could facilitate working with another mathematical subject and also implies the necessity of describing and defining students’ mathematical behaviors from an operational view of knowledge, which might lead to building foundations of a substantial cognitive map for students’ mathematical development.     

Global pedigree and national imperative: Hong Kong universities’ response to China’s grand strategies

Higher Education - Literature offers a theoretical framework exemplifying the inherent tensions between “becoming Chinese” and “remaining global” in the evolution of the...     

The Effects of Perfectionism and Dispositional Self-focused Attention of Novice Counselor on Psychological Burnout: Moderated Mediating Effects of Cognitive Emotion Regulation Strategies

International Journal for the Advancement of Counselling - The current study investigated the mediation effect of self-focused attention on the relationship between South Korean novice...     

Cheating among elementary school children: A machine learning approach

Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean accuracy of 81.43%. Cheating was most strongly predicted by children's beliefs about the acceptability of cheating and the observed prevalence and frequency of peer cheating at school. These findings provide important insights about the early development of academic cheating, and how to promote academic integrity and limit cheating before it becomes entrenched. The present research demonstrates that machine learning can be effectively used to analyze developmental data.     

Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010)

Research into invention, innovation policy, and technology strategy can greatly benefit from an accurate understanding of inventor careers. The United States Patent and Trademark Office does not provide unique inventor identifiers, however, making large-scale studies challenging. Many scholars of innovation have implemented ad-hoc disambiguation methods based on string similarity thresholds and string comparison matching; such methods have been shown to be vulnerable to a number of problems that can adversely affect research results. The authors address this issue contributing (1) an application of the Author-ity disambiguation approach (0170 and 0175) to the US utility patent database, (2) a new iterative blocking scheme that expands the match space of this algorithm while maintaining scalability, (3) a public posting of the algorithm and code, and (4) a public posting of the results of the algorithm in the form of a database of inventors and their associated patents. The paper provides an overview of the disambiguation method, assesses its accuracy, and calculates network measures based on co-authorship and collaboration variables. It illustrates the potential for large-scale innovation studies across time and space with visualizations of inventor mobility across the United States. The complete input and results data from the original disambiguation are available at (http://dvn.iq.harvard.edu/dvn/dv/patent); revised data described here are at (http://funglab.berkeley.edu/pub/disamb_no_postpolishing.csv); original and revised code is available at (https://github.com/funginstitute/disambiguator); visualizations of inventor mobility are at (http://funglab.berkeley.edu/mobility/).