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Colleges and universities are complex spaces of both lesbian, gay, bisexual, transgender, and queer (LGBTQ+) liberation and oppression. College students experience LGBTQ+ bias-motivated incidents of harassment, assault, or other violence. Previous researchers have found that these incidents are severely underreported, which may be related to a range of factors, including campus policies, campus climates, and administrator incompetence. These campus characteristics are upheld through systems of anti-LGBTQ+ surveillance and oppression, including compulsory heterogenderism, which normalize anti-LGBTQ+ violence. In this study, we used a survey of 143 LGBTQ+ college students in the Southeastern U.S. to examine their experiences of violence on campus. Using queer theory, we analyzed student responses about bias-motivated incidents and whether they chose to report the incidents to campus or legal authorities via convergent mixed methods. In this analysis, LGBTQ+ students' decisions to report a bias incident are moderated by considerations of the significance of the incidents, unclear and prohibitive reporting options, and the risks associated with reporting. Additionally, students regularly minimized the seriousness of violence against themselves even when describing incidents that commonly meet college and university definitions of harassment and/or assault. This study shows LGBTQ+ students' experiences of bias-motivated violence and (non)reporting are complex and affected by many facets of college life. Therefore, colleges and universities looking to address violence on campus need to improve not only policy but also communication and other efforts that demonstrate students experiencing violence will be heard, supported, and affirmed.

This qualitative study explored administrative responses to local and sociopolitical events challenging campus climates at public research universities. Using critical discourse analysis, we examined the use of language as a form of power in publicly available documents addressing campus climate for diversity and inclusion at 31 U.S. institutions. Findings center on 3 themes: underlying power in determining what to address; the power of language in perpetuating or deconstructing power, privilege, and oppression; and the distinction between espousing and enacting commitments to diversity, equity, and inclusion. Implications for research and practice are discussed.

A need exists to examine the strengths and limitations of literature on LGBTQ + student mental health since higher education and helping professionals use it to support LGBTQ + college students. A summative content analysis of LGBTQ + student mental health research from 2009 to 2019 yielded three themes: (1) how authors frame research, including decisions on methodology, frameworks, and samples; (2) mental health issues explored as involving descriptions and attributions of mental distress, together with the (lack of) systemic analysis; (3) implications provided to enhance LGBTQ + student well-being, such as more resources, structural changes, and training. Implications for practice and research are provided. [ABSTRACT FROM AUTHOR]

The National Research Council recommends that genetic differentiation among subgroups of ethnic samples be lower than 3% of the total genetic differentiation within the ethnic sample to be used for estimating reliable random match probabilities for forensic use. Native American samples in the United States' Combined DNA Index System (CODIS) database represent four language families: Algonquian, Na-Dene, Eskimo-Aleut, and Salishan. However, a minimum of 27 Native American language families exists in the US, not including language isolates. Our goal was to ascertain whether genetic differences are correlated with language groupings and, if so, whether additional language families would provide a more accurate representation of current genetic diversity among tribal populations. The 21 short tandem repeat (STR) loci included in the Globalfiler® PCR Amplification Kit were used to characterize six indigenous language families, including three of the four represented in the CODIS database (i.e. Algonquian, Na-Dene, and Eskimo-Aleut), and two language isolates (Miwok and Seri) using major population genetic diversity metrics such as F statistics and Bayesian clustering analysis of genotype frequencies. Most of the genetic variation (97%) was found to be within language families instead of among them (3%). In contrast, when only the three of the four language families represented in both the CODIS database and the present study were considered, 4% of the genetic variation occurred among the language groups. Bayesian clustering resulted in a maximum posterior probability indicating three genetically distinct groups among the eight language families and isolates: (1) Eskimo, (2) Seri, and (3) all other language groups and isolates, thus confirming genetic subdivision among subgroups of the CODIS Native American database. This genetic structure indicates the need for an increased number of Native American populations based on language affiliation in the CODIS database as well as more robust sample sets for those language families. Supplemental data for this article is available online at https://doi.org/10.1080/20961790.2021.1963088. [ABSTRACT FROM AUTHOR]

We compare the effectiveness of short tandem repeat (STR) and single nucleotide polymorphism (SNP) genotypes for estimating pairwise relatedness, using molecular data and pedigree records from a captive Chinese rhesus macaque population at the California National Primate Research Center. We find that a panel of 81 SNPs is as effective at estimating first-order kin relationships as a panel of 14 highly polymorphic STRs. We note, however, that the selected STRs provide more precise predictions of relatedness than the selected SNPs, and may be preferred in contexts that require the discrimination of kin related more distantly than first-order relatives. Additionally, we compare the performance of three commonly used relatedness estimation algorithms, and find that the Wang [2002] algorithm outperforms other algorithms when analyzing STR data, while the Queller & Goodnight [1989] algorithm outperforms other algorithms when analyzing SNP data. Future research is needed to address the number of SNPs required to reach the discriminatory power of a standard STR panel in relatedness estimation for primate colony management. Am. J. Primatol. 76:313-324, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]