Georgia Tech Team Entry for the 2013 AUVSI International Aerial Robotics Competition

Presented at the Fifth International Aerial Robotics Competition (IARC) Symposium on Indoor Flight Issues, Grand Forks, ND, August, 201

Effect of Citalopram on Emotion Processing in Humans:A Combined 5-HT [C]CUMI-101 PET and Functional MRI Study

A subset of patients started on a selective serotonin reuptake inhibitor (SSRI) initially experience increased anxiety, which can lead to early discontinuation before therapeutic effects are manifest. The neural basis of this early SSRI effect is not known. Presynaptic dorsal raphe neuron (DRN) 5-HT1A receptors are known to play a critical role in affect processing. Thus we investigated the effect of acute citalopram on emotional processing and the relationship between DRN 5-HT1A receptor availability and amygdala reactivity. Thirteen (mean age 48±9 years) healthy male subjects received either a saline or citalopram infusion intravenously (10 mg over 30 min) on separate occasions in a single-blind, random order, cross-over design. On each occasion, participants underwent a block design face-emotion processing task during fMRI known to activate the amygdala. Ten subjects also completed a positron emission tomography (PET) scan to quantify DRN 5-HT1A availability using [(11)C]CUMI-101.Citalopram infusion when compared to saline resulted in a significantly increased bilateral amygdala responses to fearful vs. neutral faces (Left p=0.025; Right p=0.038 FWE-corrected). DRN [(11)C]CUMI-101availability significantly positively correlated with the effect of citalopram on the left amygdala response to fearful faces (Z=2.51, p=0.027) and right amygdala response to happy faces (Z=2.33, p=0.032). Our findings indicate that the initial effect of SSRI treatment is to alter processing of aversive stimuli, and that this is linked to DRN 5-HT1A receptors in line with evidence that 5-HT1A receptors have a role in mediating emotional processing

Gleaning Racial Justice Futures: Confronting the past and incorporating plural everydays

Visions of the future world we want to create help align people toward change. Such concepts are present within some racial justice advocacy groups. Still, we propose that the work of attaining equity might benefit from more use of future visions as an additional tool toward creating systems change. To understand how visions of possible futures show up in current racial justice work, we analysed the communications of fifteen organizations. We used website content to discern how these organizations describe the worlds they want to build—a technique to gather information without requiring any additional effort on their part. The collect future visions were a small portion of the online material, but they provided rich depictions of systems change. From looking at how organizations described possible futures, we identified themes about future objectives. We found making freedom, health, and safety more accessible for all people to be the most common intention for these futures. This analysis helps us begin to imagine how tools of futures studies might evolve to accommodate justice-oriented world-making. We found that such tools would need to account for the complexity of imagining futures from an inequitable present day: taking account of historic structures and acknowledging the plurality of present-day experiences

Multi-omics data integration for the identification of biomarkers for bull fertility.

Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify infertile bulls, no tools are currently available to enable precise predictions and prevent the commercialization of subfertile semen. Because male fertility is a multifactorial phenotype that is dependent on genetic, epigenetic, physiological and environmental factors, we hypothesized that an integrative analysis might help to refine our knowledge and understanding of bull fertility. We combined -omics data (genotypes, sperm DNA methylation at CpGs and sperm small non-coding RNAs) and semen parameters measured on a large cohort of 98 Montbéliarde bulls with contrasting fertility levels. Multiple Factor Analysis was conducted to study the links between the datasets and fertility. Four methodologies were then considered to identify the features linked to bull fertility variation: Logistic Lasso, Random Forest, Gradient Boosting and Neural Networks. Finally, the features selected by these methods were annotated in terms of genes, to conduct functional enrichment analyses. The less relevant features in -omics data were filtered out, and MFA was run on the remaining 12,006 features, including the 11 semen parameters and a balanced proportion of each type of-omics data. The results showed that unlike the semen parameters studied the-omics datasets were related to fertility. Biomarkers related to bull fertility were selected using the four methodologies mentioned above. The most contributory CpGs, SNPs and miRNAs targeted genes were all found to be involved in development. Interestingly, fragments derived from ribosomal RNAs were overrepresented among the selected features, suggesting roles in male fertility. These markers could be used in the future to identify subfertile bulls in order to increase the global efficiency of the breeding sector

Multiple factor analysis highlights the contributions of SNPs, CpGs and sncRNAs to bull fertility.

MFA was run on the 12,006 selected features belonging to the CpG, sncRNA, SNP and SP tables that actively contributed to the results. Furthermore, fertility, the origins of bulls and the semen extraction batch were set as illustrative features, meaning that they did not participate in MFA construction. a: A global variable plot with active features shown in red and illustrative features in green. b: Individual factor map where each dot corresponds to a bull and coloured depending on its fertility class. C, D: Variable factor maps for quantitative features (CpGs and sncRNAs). The first and second dimensions [c] and the first and third dimensions [d] are represented. Each arrowhead corresponds to a feature and was coloured depending on its dataset of origin, with CpGs, sncRNAs and SPs shown in blue, yellow and grey, respectively. Furthermore, the intensity of the colour of arrowheads indicated the cos2, reflecting the strength of the correlation between a feature and dimension 1. In C, two clusters are represented, gathering the features with the most important positive (>0.55, cluster 1) or negative (<0.4, cluster 2) coordinates along dimension 1.</p

Functional analyses of selected features.

a: Global strategy for functional analysis. The combination of SNP, CpG and sncRNA features selected by the three unbiased methods (Cforest, Gradient Boosting, Neural Networks) was considered and referred to as “Selected features”. Genes including the selected CpGs and SNPs were subjected directly to enrichment analysis. The distribution of different sncRNAs families highlighted an overrepresentation of miRNAs and rRFs among the selected features when compared to the background, which included the 413,952 sncRNAs that were initially represented in the sncRNA dataset (lower left panel). The analysis therefore focused on the miRNA target genes that were subjected to functional enrichment analysis. b: The genes containing selected SNP and CpG features underwent enrichment analysis using DAVID. Three clusters of terms were significantly enriched (EASE score higher than 1.3; left-hand panel). The proportions of genes targeted by selected CpGs only, selected SNPs only, or by both CpGs and SNPs, varied in the three clusters (pie charts, right-hand panel). c: Genes identified as putative targets of selected miRNAs by Targetscan underwent an overrepresentation analysis using Webgestalt. The top 10 overrepresented GO terms are listed, with the corresponding adjusted p-values.</p

Data filtering strategy.

The four different tables included a heterogeneous number of features [a]. Because the CpGs, SNPs and sncRNAs constituted huge data tables, features that could be considered as noise and features that did not display significant variations among the bulls were filtered out [b]. Because the remaining sncRNAs and SPs were impacted by the extraction batch of the semen, they were next corrected for this batch effect [c]. Finally, because the CpG, SNP and sncRNA tables still included an important number of features, the most relevant were selected using a supervised method, Random Forest [d]. At the end of these three filtering steps, 12,006 relevant features originating from four data tables were retained for further analysis.</p