Me, Myself, and I: Activating Social Identities to Protect Against Identity Threat

Social identities help foster feelings of belonging and support (Cialdini & Richardson, 1980; Correll & Park, 2005). This is particularly important during college, where the environment is ripe with identity-related threats that communicate a student does not belong, which is associated with decrements in academic well-being and performance (Steele & Aronson, 1995). To combat this threat, I created and tested an identity-based intervention that focused on activating students’ social identities. I predicted that activation of multiple identities could enhance the benefits associated with holding social identities and integrate students’ identities into a cohesive sense of self, buttressing sense of self in the face of threat. I conducted three pilot studies to investigate empirical differences between single and multiple identity activation (Study 1), better understand the experience of holding multiple social identities for college students (Study 2), and examine how a multiple identity manipulation combatted negative feedback directed to the self (Study 3). These findings were then integrated to create a multiple identity intervention (Study 4) delivered to incoming college students (N = 651). Results of Study 4 revealed that the identity intervention did not have an overall effect of enhancing academic well-being and performance. However, moderation analyses indicated that the intervention benefitted students in situations where perceived conflict among identities or lower identity importance diminished well-being and performance. These findings suggest that activation of multiple identities can create a sense of harmony and cohesion among identities, buffering against perceived dissonance among identities that might otherwise be detrimental to well-being and academic performance. Discussion of these findings, potential limitations of the current methodology, and modifications for further examinations of multiple identity activation are included

Unraveling the biological functions of Smad7 with mouse models

Smad7 is a key negative regulator of the transforming growth factor β (TGF-β) signaling and plays an important role in modulating a large array of biological processes. The physiological actions of Smad7 have been extensively investigated by using various mouse models. These studies have pinpointed numerous important in vivo functions of Smad7, including its activity in early embryonic development, fibrosis of many organs, skin cell differentiation, regulation of immune response and inflammation, tumorigenesis, and metabolic control. As most biological activities modulated by Smad7 are closely related to human disorders, it is anticipated that Smad7 will continue to be an intriguing molecule that will be vigorously investigated in the future to strengthen our understanding about the pathogenesis of human diseases

Multiple Identity Activation as Stereotype Threat Protection

People face threats daily –threats to their self-esteem, belonging, and sense of self. The current proposal explored a solution for these threats through utilizing the positive power of social identities. Past work has shown that social identities benefit the individual in a multitude of ways (e.g. increasing self-esteem or sense of belonging). Therefore, I predicted that thinking of multiple social identities important to the self would activate these positive outcomes, which in turn would serve as protection in the face of threat. I first explored the relationship between self-esteem, importance of identities, and number of identities generated, as there is little research investigating the number of identities from which individuals derive benefits. In Study 1, participants were asked to come up with a specific number of identities important to the self, followed by measurements of identity importance, difficulty of listing identities, and self-esteem. Identity importance tapered off after listing five identities, suggesting the presence of diminishing returns for the self after priming more than five identities. Using these results, Study 2 tested the main hypothesis of multiple identity activation on identity threat protection. In this study, female participants listed no identities, a singular identity, or five identities, followed by a gender stereotype threat (i.e. women underperforming in math), and quantitative task. Results of Study 2 did not support the prediction that generating multiple identities would protect against stereotype threat. Unexpectedly, participants who received the stereotype threat performed better on the math task compared to those who did not receive the threat, contradicting the expected effect of the threat. Additional moderation analyses and possible reasons for the observed pattern of analyses are discussed

Oracle Database 10g: a platform for BLAST search and Regular Expression pattern matching in life sciences

As database management systems expand their array of analytical functionality, they become powerful research engines for biomedical data analysis and drug discovery. Databases can hold most of the data types commonly required in life sciences and consequently can be used as flexible platforms for the implementation of knowledgebases. Performing data analysis in the database simplifies data management by minimizing the movement of data from disks to memory, allowing pre-filtering and post-processing of datasets, and enabling data to remain in a secure, highly available environment. This article describes the Oracle Database 10g implementation of BLAST and Regular Expression Searches and provides case studies of their usage in bioinformatics. http://www.oracle.com/technology/software/index.htm

The prion dilemma confounding science educators

In this paper, the issue of the prion hypothesis, a simmering controversy within the scientific community, is addressed. We inquire into the appropriateness of the use of certain augmentations and rhetoric approaches used during scientific debates, as well as the aptness of unequivocal statements in textbooks that indicate “abnormal prions” as a primary cause of Transmissible Spongiform Encephalopathies

xFraud: Explainable Fraud Transaction Detection

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly composed of a detector and an explainer. The xFraud detector can effectively and efficiently predict the legitimacy of incoming transactions. Specifically, it utilizes a heterogeneous graph neural network to learn expressive representations from the informative heterogeneously typed entities in the transaction logs. The explainer in xFraud can generate meaningful and human-understandable explanations from graphs to facilitate further processes in the business unit. In our experiments with xFraud on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud is able to outperform various baseline models in many evaluation metrics while remaining scalable in distributed settings. In addition, we show that xFraud explainer can generate reasonable explanations to significantly assist the business analysis via both quantitative and qualitative evaluations.Comment: This is the extended version of a full paper to appear in PVLDB 15 (3) (VLDB 2022

Introduction to semantic e-Science in biomedicine

The Semantic Web technologies provide enhanced capabilities that allow data and the meaning of the data to be shared and reused across application, enterprise, and community boundaries, better enabling integrative research and more effective knowledge discovery. This special issue is intended to give an introduction of the state-of-the-art of Semantic Web technologies and describe how such technologies would be used to build the e-Science infrastructure for biomedical communities. Six papers have been selected and included, featuring different approaches and experiences in a variety of biomedical domains

The quality of energy- and macronutrient-balanced diets regulates host susceptibility to influenza in mice

Modulation of individual macronutrients or caloric density is known to regulate host resistance to infection in mice. However, the impact of diet composition, independent of macronutrient and energy content, on infection susceptibility is unclear. We show that two laboratory rodent diets, widely used as standard animal feeds and experimental controls, display distinct abilities in supporting mice during influenza infection. Mice placed on the highly processed AIN93G showed increased mortality to infection compared with those on a grain-based chow diet, suggesting a detrimental role for highly processed food in host defense. We further demonstrate that the heightened susceptibility of AIN93G-fed mice was associated with the failure in homeostasis restoration mediated by the cytokine interferon (IFN)-γ. Our findings show that diet composition calibrates host survival threshold by regulating adaptive homeostasis and highlights a pivotal role for extrinsic signals in host phenotype and outcome of host-pathogen interaction

Advancing translational research with the Semantic Web

<p>Abstract</p> <p>Background</p> <p>A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen <it>Translational Research</it>, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature.</p> <p>Results</p> <p>We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine.</p> <p>Conclusion</p> <p>Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.</p

Changing Social Norms to Foster the Benefits of Collaboration in Diverse Workgroups

Although collaboration is essential for advancing science and maximizing human performance, collaboration in demographically diverse groups has often proven ineffective and sometimes harmful for negatively stereotyped group members. Here we report the results of a two randomized field experiments that sought to change social norms in an effort to realize the benefits of demographic diversity in collaborative workgroups. Separate experiments were conducted in undergraduate Biology (N=1215) and Physics (N=607) courses that were already employing small-group collaboration (3-4 students) during weekly study sections. At the beginning of the semester, study sections were assigned to receive either the intervention or business-as-usual control activities. The 30-minute intervention used narrative writing, peer testimonials, and small group discussion to establish a local norm that social and academic struggles are normal, anxiety about belonging is common, and most students eventually overcome these challenges. At the end of the semester, students who worked in diverse groups reported more positive social experiences in the intervention compared to control condition. Behaviorally, average attendance was higher in study sections that received the intervention, as was persistence in college after one year. Finally, students in each context theorized to be high in belonging uncertainty showed performance benefits, as the intervention closed the ethnic group performance gap in Biology classrooms and the gender performance gap in Physics classrooms. The results illustrate how social experiences in collaborative groups can be engineered to help realize the benefits of diversity