A Composite of Multiple Signals Distinguishes Causal Variants in Regions of Positive Selection

The human genome contains hundreds of regions whose patterns of genetic variation indicate recent positive natural selection, yet for most the underlying gene and the advantageous mutation remain unknown. We developed a method, composite of multiple signals (CMS), that combines tests for multiple signals of selection and increases resolution by up to 100-fold. By applying CMS to candidate regions from the International Haplotype Map, we localized population-specific selective signals to 55 kilobases (median), identifying known and novel causal variants. CMS can not just identify individual loci but implicates precise variants selected by evolution.Organismic and Evolutionary BiologyOther Research Uni

Genome-wide detection and characterization of positive selection in human populations

With the advent of dense maps of human genetic variation, it is now possible to detect positive natural selection across the human genome. Here we report an analysis of over 3 million polymorphisms from the International HapMap Project Phase 2 (HapMap2). We used ‘long-range haplotype’ methods, which were developed to identify alleles segregating in a population that have undergone recent selection, and we also developed new methods that are based on cross-population comparisons to discover alleles that have swept to near-fixation within a population. The analysis reveals more than 300 strong candidate regions. Focusing on the strongest 22 regions, we develop a heuristic for scrutinizing these regions to identify candidate targets of selection. In a complementary analysis, we identify 26 non-synonymous, coding, single nucleotide polymorphisms showing regional evidence of positive selection. Examination of these candidates highlights three cases in which two genes in a common biological process have apparently undergone positive selection in the same population: LARGE and DMD, both related to infection by the Lassa virus3, in West Africa; SLC24A5 and SLC45A2, both involved in skin pigmentation in Europe; and EDAR and EDA2R, both involved in development of hair follicles, in Asia

Left Hemisphere Specialization for Oro-Facial Movements of Learned Vocal Signals by Captive Chimpanzees

The left hemisphere of the human brain is dominant in the production of speech and signed language. Whether similar lateralization of function for communicative signal production is present in other primates remains a topic of considerable debate. In the current study, we examined whether oro-facial movements associated with the production of learned attention-getting sounds are differentially lateralized compared to facial expressions associated with the production of species-typical emotional vocalizations in chimpanzees.Still images captured from digital video were used to quantify oro-facial asymmetries in the production of two attention-getting sounds and two species-typical vocalizations in a sample of captive chimpanzees. Comparisons of mouth asymmetries during production of these sounds revealed significant rightward biased asymmetries for the attention-getting sounds and significant leftward biased asymmetries for the species-typical sounds.These results suggest that the motor control of oro-facial movements associated with the production of learned sounds is lateralized to the left hemisphere in chimpanzees. Furthermore, the findings suggest that the antecedents for lateralization of human speech may have been present in the common ancestor of chimpanzees and humans approximately 5 mya and are not unique to the human lineage

Negative regulation of NF-κB by the ING4 tumor suppressor in breast cancer

Nuclear Factor kappa B (NF-κB) is a key mediator of normal immune response but contributes to aggressive cancer cell phenotypes when aberrantly activated. Here we present evidence that the Inhibitor of Growth 4 (ING4) tumor suppressor negatively regulates NF-κB in breast cancer. We surveyed primary breast tumor samples for ING4 protein expression using tissue microarrays and a newly generated antibody. We found that 34% of tumors expressed undetectable to low levels of the ING4 protein (n = 227). Tumors with low ING4 expression were frequently large in size, high grade, and lymph node positive, suggesting that down-regulation of ING4 may contribute to breast cancer progression. In the same tumor set, we found that low ING4 expression correlated with high levels of nuclear phosphorylated p65/RelA (p-p65), an activated form of NF-κB (p = 0.018). Fifty seven percent of ING4-low/p-p65-high tumors were lymph node-positive, indicating a high metastatic tendency of these tumors. Conversely, ectopic expression of ING4 inhibited p65/RelA phosphorylation in T47D and MCF7 breast cancer cells. In addition, ING4 suppressed PMA-induced cell invasion and NF-κB-target gene expression in T47D cells, indicating that ING4 inhibited NF-κB activity in breast cancer cells. Supportive of the ING4 function in the regulation of NF-κB-target gene expression, we found that ING4 expression levels inversely correlated with the expression of NF-κB-target genes in primary breast tumors by analyzing public gene expression datasets. Moreover, low ING4 expression or high expression of the gene signature composed of a subset of ING4-repressed NF-κB-target genes was associated with reduced disease-free survival in breast cancer patients. Taken together, we conclude that ING4 negatively regulates NF-κB in breast cancer. Consequently, down-regulation of ING4 leads to activation of NF-κB, contributing to tumor progression and reduced disease-free patient survival in breast cancer

Student and Faculty Perceptions of Artificial Intelligence in Student Writing

Artificial Intelligence (AI) can write poetry and news articles that go undetected by human readers. Can students use AI to write college assignments that go undetected by their professors? Past and current perceptions of AI in education have differed; while some people view AI as a tool, others view it as a threat to learning. We surveyed 83 students and 82 faculty, providing them with 4 writing samples, 3 generated by undergraduate students and 1 generated by the AI-chatbot, ChatGPT-3. We found that neither faculty nor students could detect AI-generated writing at above chance levels. Faculty and students had similar opinions on the ethicality of various uses of AI technology and how much these uses are likely to compromise learning. Faculty reported a high level of concern regarding the potential effects that AI could have on their pedagogical practices. Prior experience with ChatGPT-3 and analyzing the structure and organization of the response was found to improve detection ability in faculty, suggesting that increased exposure and domain-specific analysis may be beneficial in the detection of AI in students’ writing

Disseminated aspergillosis in a dog due to Aspergillus alabamensis

Disseminated aspergillosis is uncommon in dogs and often associated with Aspergillus terreus. A case of disseminated disease in an English springer spaniel is reported from which Aspergillus alabamensis was recovered by culture and identified by molecular means suggesting a potential role for this agent as a primary pathogen of dogs.This article is published as Burrough, Eric, Krysta Deitz, Joann Kinyon, Claire Andreasen, Timothy Frana, Deanna Sutton, Elizabeth Thompson, Jianmin Fu, Brian Wickes, and Jesse Hostetter. "Disseminated aspergillosis in a dog due to Aspergillus alabamensis." Medical Mycology Case Reports 1, no. 1 (2012): 1-4. DOI: 10.1016/j.mmcr.2012.02.002. Copyright 2012 International Society for Human and Animal Mycology. Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) Posted with permission