Neural and Behavioral Effects of Social Exclusion on Self-Regulation

Poster presentation abstract

HLAProfiler utilizes k-mer profiles to improve HLA calling accuracy for rare and common alleles in RNA-seq data

BACKGROUND: The human leukocyte antigen (HLA) system is a genomic region involved in regulating the human immune system by encoding cell membrane major histocompatibility complex (MHC) proteins that are responsible for self-recognition. Understanding the variation in this region provides important insights into autoimmune disorders, disease susceptibility, oncological immunotherapy, regenerative medicine, transplant rejection, and toxicogenomics. Traditional approaches to HLA typing are low throughput, target only a few genes, are labor intensive and costly, or require specialized protocols. RNA sequencing promises a relatively inexpensive, high-throughput solution for HLA calling across all genes, with the bonus of complete transcriptome information and widespread availability of historical data. Existing tools have been limited in their ability to accurately and comprehensively call HLA genes from RNA-seq data. RESULTS: We created HLAProfiler ( https://github.com/ExpressionAnalysis/HLAProfiler ), a k-mer profile-based method for HLA calling in RNA-seq data which can identify rare and common HLA alleles with > 99% accuracy at two-field precision in both biological and simulated data. For 68% of novel alleles not present in the reference database, HLAProfiler can correctly identify the two-field precision or exact coding sequence, a significant advance over existing algorithms. CONCLUSIONS: HLAProfiler allows for accurate HLA calls in RNA-seq data, reliably expanding the utility of these data in HLA-related research and enabling advances across a broad range of disciplines. Additionally, by using the observed data to identify potential novel alleles and update partial alleles, HLAProfiler will facilitate further improvements to the existing database of reference HLA alleles. HLAProfiler is available at https://expressionanalysis.github.io/HLAProfiler/

Acute and Chronic B Cell Depletion Disrupts CD4 + and CD8 + T Cell Homeostasis and Expansion during Acute Viral Infection in Mice

B cells provide humoral protection against pathogens and promote cellular immunity through diverse nonclassical effector functions. To assess B cell function in promoting T cell homeostasis, mature B cells were either acutely or chronically depleted in mice using CD20 mAb. Acute B cell depletion in either 2- or 4-mo-old mice significantly reduced spleen and lymph node CD4+ and CD8+ T cell numbers, including naive, activated, and Foxp3+CD25+CD4+ regulatory T cell subsets. The numbers of IFN-γ– and TNF-α–producing T cells were also significantly reduced. Chronic B cell depletion for 6 mo in aged naive mice resulted in a 40–70% reduction in activated CD4+ and CD8+ T cell numbers and 20–50% reductions in IFN-γ–producing T cells. Therefore, B cells were necessary for maintaining naive CD4+ and CD8+ T cell homeostasis for subsequent optimal T cell expansion in young and old mice. To determine the significance of this finding, a week of B cell depletion in 4-mo-old mice was followed by acute viral infection with lymphocytic choriomeningitis virus Armstrong. Despite their expansion, activated and cytokine-producing CD4+ and CD8+ T cell numbers were still significantly reduced 1 wk later. Moreover, viral peptide-specific CD4+ and CD8+ T cell numbers and effector cell development were significantly reduced in mice lacking B cells, whereas lymphocytic choriomeningitis virus titers were dramatically increased. Thus, T cell function is maintained in B cell–depleted mice, but B cells are required for optimal CD4+ and CD8+ T cell homeostasis, activation, and effector development in vivo, particularly during responses to acute viral infection

MLSys: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two

Microbial Fuel Cells and Microbial Ecology: Applications in Ruminant Health and Production Research

Microbial fuel cell (MFC) systems employ the catalytic activity of microbes to produce electricity from the oxidation of organic, and in some cases inorganic, substrates. MFC systems have been primarily explored for their use in bioremediation and bioenergy applications; however, these systems also offer a unique strategy for the cultivation of synergistic microbial communities. It has been hypothesized that the mechanism(s) of microbial electron transfer that enable electricity production in MFCs may be a cooperative strategy within mixed microbial consortia that is associated with, or is an alternative to, interspecies hydrogen (H2) transfer. Microbial fermentation processes and methanogenesis in ruminant animals are highly dependent on the consumption and production of H2in the rumen. Given the crucial role that H2 plays in ruminant digestion, it is desirable to understand the microbial relationships that control H2 partial pressures within the rumen; MFCs may serve as unique tools for studying this complex ecological system. Further, MFC systems offer a novel approach to studying biofilms that form under different redox conditions and may be applied to achieve a greater understanding of how microbial biofilms impact animal health. Here, we present a brief summary of the efforts made towards understanding rumen microbial ecology, microbial biofilms related to animal health, and how MFCs may be further applied in ruminant research

Enhancing Criminal Sentencing Options in Wisconsin: The State and County Correctional Partnership

The State and County Correctional Partnership proposes providing Wisconsin counties with annual block grants and imposing fees for state prison time served by felons who commit less serious crimes. This policy change would seek to create an environment in which counties can find innovative ways to divert felons safely and cost-effectively from incarceration in state prisons

genBRDF: Discovering New Analytic BRDFs with Genetic Programming

Figure 1: A comparison between Cook-Torrance [1982], Löw et al.’s smooth surface model [2012], and a new analytic BRDF model found by genBRDF, all fit with Löw et al.’s E2 metric to three materials from the MERL-MIT BRDF database [Matusik et al. 2013]. We present a framework for learning new analytic BRDF models through Genetic Programming that we call genBRDF. This approach to reflectance modeling can be seen as an extension of traditional methods that rely either on a phenomenological or empirical process. Our technique augments the human effort involved in deriving mathematical expressions that accurately characterize complex high-dimensional reflectance functions through a large-scale optimization. We present a number of analysis tools and data visualization techniques that are crucial to sifting through the large result sets produced by genBRDF in order to identify fruitful expressions. Additionally, we highlight several new models found by genBRDF that have not previously appeared in the BRDF literature. These new BRDF models are compact, and more accurate than current state-of-the-art alternatives