Sex Differences in Skin Tone Predicting Depressive Symptoms among College Students of Color

Sex Differences in Skin Tone Predicting Depressive Symptoms among College Students of Color Jenifer Rodriguez, Jenna Minter, Depts. of Psychology and Political Science, Eryn DeLaney, Dept. of Psychology Graduate Student, & Chloe Walker, Dept. of Psychology Graduate Student, with Dr. Chelsea D. Williams, Dr. Amy Adkins, Dr. Tricia Smith, & Dr. Danielle Dick, Dept. of Psychology Skin tone, or more specifically the meaning and treatment that society attaches to skin tone, has been found to impact individuals’ outcomes, with those with darker skin tones (who experience more colorism) experiencing more negative outcomes (e.g., Norwood, 2014). However, less research has tested whether there are sex differences in these relations. Intersectionality theory (Crenshaw, 1989) suggests that one’s lived experiences result from their holistic experiences of intersecting aspects of themselves (e.g., skin tone and sex). Thus, to address gaps in research, the current study examined sex as a moderating variable in the relation between skin tone and depressive symptoms among 81 college students of color who were part of a larger study on cultural experiences, genetics, and ancestry. We hypothesized that sex would moderate this relation, such that skin tone would predict greater depressive symptoms, and this association would be weaker among males compared to females (Hunter, 2007). A linear regression was conducted to test our hypothesis. Findings indicated that sex moderates the relation between skin tone and depressive symptoms, however, in a direction contrary to our hypothesis. In particular, there was no relation between skin tone and depressive symptoms among females (B = .08, p = .54), however, for males, those with darker skin tones had lower depressive symptoms (B = -.53, p = .02). In conclusion, this study pushes for more research on the sex differences in how skin tone affects mental health among college students.https://scholarscompass.vcu.edu/uresposters/1332/thumbnail.jp

Regulation, Organization and Incentives: The Political Economy of Potable Water Services in Honduras

This case study of urban water and sanitation in Honduras focuses on the perplexing phenomenon of low level equilibrium (LLE). It first seeks to characterize LLE in terms of sector performance and to show how it is propitiated by flawed arrangements for sectoral governance, organization of service delivery and regulation. It then turns to the question of why it is so difficult to escape from LLE. To this end, it analyzes the failure of recent reform efforts, using political economy techniques.

A Narrative Review of Protective Factors that Predict Enculturation Processes for Latinx Individuals in the U.S.

A Narrative Review of Protective Factors that Predict Enculturation Processes for Latinx Individuals in the U.S. Jane Sun, Dept. of Psychology, Jennifer Rodriguez, Alanna Cason, Yessica Flores, Karl Villareal, Arlenis Santana, Dept. of Psychology Graduate Student, & Chloe Walker, Dept. of Psychology Graduate Student, with Dr. Chelsea D. Williams, Dept. of Psychology According to the 2010 U.S. Census, the rise of immigration led the Latinx community to experience the largest population growth amongst all ethnic-racial groups (Sanchez et al., 2012). Enculturation is the process of preserving heritage cultural values while enduring the influence of the current, surrounding culture (Schwartz et al., 2013). Enculturation is a subcomponent in the broad spectrum of acculturation, the process through which the introduction of two differing cultures induces cultural changes (Rodriguez et al., 2002). While current research has focused on the protective factors involved in the acculturative process, minimal research has centered on the protective factors in enculturation amongst the Latinx community. The aim of the current narrative review was to identify the protective factors (e.g., language, values, generational differences, group membership) associated with enculturation of Latinx U.S. citizens. Implications will discuss the promotion of social awareness within the Latinx community.https://scholarscompass.vcu.edu/uresposters/1333/thumbnail.jp

Large parallel and perpendicular electric fields on electron spatial scales in the terrestrial bow shock

Large parallel ($\leq$ 100 mV/m) and perpendicular ($\leq$ 600 mV/m) electric fields were measured in the Earth's bow shock by the vector electric field experiment on the Polar satellite. These are the first reported direct measurements of parallel electric fields in a collisionless shock. These fields exist on spatial scales comparable to or less than the electron skin depth (a few kilometers) and correspond to magnetic field-aligned potentials of tens of volts and perpendicular potentials up to a kilovolt. The perpendicular fields are amongst the largest ever measured in space, with energy densities of $\epsilon_0 E^2/ n k_b T_e$ of order 10%. The measured parallel electric field implies that the electrons can be demagnetized, which may result in stochastic (rather than coherent) electron heating

Star Formation in the Northern Cloud Complex of NGC 2264

We have made continuum and spectral line observations of several outflow sources in the Mon OB1 dark cloud (NGC 2264) using the Heinrich Hertz Telescope (HHT) and ARO 12m millimeter-wave telescope. This study explores the kinematics and outflow energetics of the young stellar systems observed and assesses the impact star formation is having on the surrounding cloud environment. Our data set incorporates 12CO(3-2), 13CO(3-2), and 12CO(1-0) observations of outflows associated with the sources IRAS 06382+1017 and IRAS 06381+1039, known as IRAS 25 and 27, respectively, in the northern cloud complex. Complementary 870 micron continuum maps were made with the HHT 19 channel bolometer array. Our results indicate that there is a weak (approximately less than 0.5%) coupling between outflow kinetic energy and turbulent energy of the cloud. An analysis of the energy balance in the IRAS 25 and 27 cores suggests they are maintaining their dynamical integrity except where outflowing material directly interacts with the core, such as along the outflow axes.Comment: 28 pages including 6 figures, to be published in ApJ 01 July 2006, v645, 1 issu

Geographical information retrieval with ontologies of place

Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness of place as well as semantic closeness with respect to the information of interest. Here we present an ontology of place that combines limited coordinate data with semantic and qualitative spatial relationships between places. This parsimonious model of geographical place supports maintenance of knowledge of place names that relate to extensive regions of the Earth at multiple levels of granularity. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical spatial distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This is integrated with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects

Evaluation of TypeSeq, a Novel High-Throughput, Low-Cost, Next-Generation Sequencing-Based Assay for Detection of 51 Human Papillomavirus Genotypes.

BackgroundHuman papillomaviruses (HPV) cause over 500 000 cervical cancers each year, most of which occur in low-resource settings. Human papillomavirus genotyping is important to study natural history and vaccine efficacy. We evaluated TypeSeq, a novel, next-generation, sequencing-based assay that detects 51 HPV genotypes, in 2 large international epidemiologic studies.MethodsTypeSeq was evaluated in 2804 cervical specimens from the Study to Understand Cervical Cancer Endpoints and Early Determinants (SUCCEED) and in 2357 specimens from the Costa Rica Vaccine Trial (CVT). Positive agreement and risks of precancer for individual genotypes were calculated for TypeSeq in comparison to Linear Array (SUCCEED). In CVT, positive agreement and vaccine efficacy were calculated for TypeSeq and SPF10-LiPA.ResultsWe observed high overall and positive agreement for most genotypes between TypeSeq and Linear Array in SUCCEED and SPF10-LiPA in CVT. There was no significant difference in risk of precancer between TypeSeq and Linear Array in SUCCEED or in estimates of vaccine efficacy between TypeSeq and SPF10-LiPA in CVT.ConclusionsThe agreement of TypeSeq with Linear Array and SPF10-LiPA, 2 well established standards for HPV genotyping, demonstrates its high accuracy. TypeSeq provides high-throughput, affordable HPV genotyping for world-wide studies of cervical precancer risk and of HPV vaccine efficacy

Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://apc.cs.princeton.edu