Continuous Prediction with Experts' Advice

Prediction with experts' advice is one of the most fundamental problems in online learning and captures many of its technical challenges. A recent line of work has looked at online learning through the lens of differential equations and continuous-time analysis. This viewpoint has yielded optimal results for several problems in online learning. In this paper, we employ continuous-time stochastic calculus in order to study the discrete-time experts' problem. We use these tools to design a continuous-time, parameter-free algorithm with improved guarantees for the quantile regret. We then develop an analogous discrete-time algorithm with a very similar analysis and identical quantile regret bounds. Finally, we design an anytime continuous-time algorithm with regret matching the optimal fixed-time rate when the gains are independent Brownian Motions; in many settings, this is the most difficult case. This gives some evidence that, even with adversarial gains, the optimal anytime and fixed-time regrets may coincide.Comment: 30 pages, 1 figure. Version 2 diff: minor edits, reorganization for a journal submission, correct statement of Lemma 5.1 and a better formatted proof of the same lemm

Triple Helix, Fall 2018

Table of Contents: Science Agenda: The Politics of Grant Writing / by Kavya Rajesh (p. 4) -- From the Experts / by Katherine Bruner (p. 5) -- 3D Printed Drugs: The Future of Pharmaceuticals / by Ethan Wang (p. 6) -- Computerized Markets: Wall Street Takeover / by James Kiraly (p. 10) -- The Evolution of Fear / by Alisha Ahmed (p. 14) -- ADDing Up / by Victor Liaw (p. 18) -- The Clone Wars / by Jina Zhou (p. 22) -- Physician-Assisted Suicide: Drawing the Line / by Haley Wolf (p. 26) -- Supervised Injection Sites / by Alex Gajewski (p. 30) -- On Emerging Medicalization and Health Care / by Patrick Lee (p. 33) -- The Future of Human Gene Modifications / by Elizabeth Robinson (p. 36)College of Natural SciencesUT LibrariesLiberal Art

Epigenomic regulation of human T-cell leukemia virus by chromatin-insulator CTCF

Human T-cell leukemia virus type 1 (HTLV-1) is a retrovirus that causes an aggressive T-cell malignancy and a variety of inflammatory conditions. The integrated provirus includes a single binding site for the epigenomic insulator, CCCTC-binding protein (CTCF), but its function remains unclear. In the current study, a mutant virus was examined that eliminates the CTCF-binding site. The mutation did not disrupt the kinetics and levels of virus gene expression, or establishment of or reactivation from latency. However, the mutation disrupted the epigenetic barrier function, resulting in enhanced DNA CpG methylation downstream of the CTCF binding site on both strands of the integrated provirus and H3K4Me3, H3K36Me3, and H3K27Me3 chromatin modifications both up- and downstream of the site. A majority of clonal cell lines infected with wild type HTLV-1 exhibited increased plus strand gene expression with CTCF knockdown, while expression in mutant HTLV-1 clonal lines was unaffected. These findings indicate that CTCF binding regulates HTLV-1 gene expression, DNA and histone methylation in an integration site dependent fashion

Local Chromatin Features Including PU.1 and IKAROS Binding and H3K4 Methylation Shape the Repertoire of Immunoglobulin Kappa Genes Chosen for V(D)J Recombination.

V(D)J recombination is essential for the generation of diverse antigen receptor (AgR) repertoires. In B cells, immunoglobulin kappa (Igκ) light chain recombination follows immunoglobulin heavy chain (Igh) recombination. We recently developed the DNA-based VDJ-seq assay for the unbiased quantitation of Igh VH and DH repertoires. Integration of VDJ-seq data with genome-wide datasets revealed that two chromatin states at the recombination signal sequence (RSS) of VH genes are highly predictive of recombination in mouse pro-B cells. It is unknown whether local chromatin states contribute to Vκ gene choice during Igκ recombination. Here we adapt VDJ-seq to profile the Igκ VκJκ repertoire and present a comprehensive readout in mouse pre-B cells, revealing highly variable Vκ gene usage. Integration with genome-wide datasets for histone modifications, DNase hypersensitivity, transcription factor binding and germline transcription identified PU.1 binding at the RSS, which was unimportant for Igh, as highly predictive of whether a Vκ gene will recombine or not, suggesting that it plays a binary, all-or-nothing role, priming genes for recombination. Thereafter, the frequency with which these genes recombine was shaped both by the presence and level of enrichment of several other chromatin features, including H3K4 methylation and IKAROS binding. Moreover, in contrast to the Igh locus, the chromatin landscape of the promoter, as well as of the RSS, contributes to Vκ gene recombination. Thus, multiple facets of local chromatin features explain much of the variation in Vκ gene usage. Together, these findings reveal shared and divergent roles for epigenetic features and transcription factors in AgR V(D)J recombination and provide avenues for further investigation of chromatin signatures that may underpin V(D)J-mediated chromosomal translocations

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Investigating PKCε as a Target for Opioid Withdrawal and Pain

Opioid use disorder (OUD) has become much more prevalent in the United States over the past two decades. Withdrawal symptoms from both opioids and OUD medications used as replacement therapies can perpetuate the addiction cycle via negative reinforcement. Thus, there is a need to discover new, non-addictive medications for treatment. Protein kinase C epsilon (PKCε) has emerged as a target for treating both nicotine and alcohol use disorders. More recently, our group found that inhibiting PKCε reduces conditioned place aversion in morphine-dependent mice following naloxone-precipitated withdrawal, suggesting that PKCε is important for negative emotional signs of opioid withdrawal. Our current goal is to investigate whether other behavioral aspects of opioid withdrawal are associated with PKCε and determine which brain regions are important for these behaviors. This was accomplished by identifying the neuronal ensembles that modulate opioid withdrawal via PKCε using Fos immunohistochemistry. I found reductions in Fos expression between mice treated with the PKCε inhibitor in brain regions previously associated with negative emotional signs of withdrawal. However, inherent flaws with immunohistochemistry, which include asymmetrical sectioning, mechanical distortion, and random sub-sampling, may have confounded these findings. Recently, novel tissue-clearing methods have been developed which render millimeter-thick sections transparent and preserve the integrity of the brain’s natural cytoarchitecture, allowing analysis of Fos counts across brain regions in an unbiased fashion. I was able to successfully clear brains using FACT and FDISCO, which are techniques that can clear brains while preserving endogenous fluorescence. These can be used in future experiments investigating brain regions important for PKCε modulation of opioid withdrawal.Biochemistr

Triple Helix, Fall 2019

Table of Contents: From the Expert / by Nathaniel Woodward (p. 2) -- Lab-Cultured Meat / by Victor Liaw (p. 4) -- Tooth Regenerative Therapy / by Omkar Patel (p. 6) -- Voluntourism / by Sofia Ferguson (p. 8) -- The Art [or Science] of Pedagogy / by Alisha Ahmed (p. 10) -- Reminiscing in Real Time / by Rahul Pentaparthy (p. 12) -- Sexual Assault's Attack on the Mind / by Emmilyne Thomas (p. 14) -- Population Control / by Amy Pham (p. 16) -- Free Will / by Aaron Luna (p. 18) -- The Development and Ethics of AI / by Ethan Wang (p. 20) -- Direct to Consumer Testing / by Jina Zhou (p. 22) -- Is That a Disease? / by Montavius Coleman (p. 23)College of Natural SciencesLiberal ArtsUT Librarie

Estimating near-surface air temperature across Israel using a machine learning based hybrid approach

Rising global temperatures over the last decades have increased heat exposure among populations worldwide. An accurate estimate of the resulting impacts on human health demands temporally explicit and spatially resolved monitoring of near-surface air temperature (Ta). Neither ground-based nor satellite-borne observations can achieve this individually, but the combination of the two provides synergistic opportunities. In this study, we propose a two-stage machine learning-based hybrid model to estimate 1 × 1 km2 gridded intra-daily Ta from surface skin temperature (Ts) across the complex terrain of Israel during 2004–2016. We first applied a random forest (RF) regression model to impute missing Ts from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites, integrating Ts from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite and synoptic variables from European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. The imputed Ts are in turn fed into the Stage 2 RF-based model to estimate Ta at the satellite overpass hours of each day. We evaluated the model's performance applying out-of-sample fivefold cross validation. Both stages of the hybrid model perform very well with out-of-sample fivefold cross validated R2 of 0.99 and 0.96, MAE of 0.42°C and 1.12°C, and RMSE of 0.65°C and 1.58°C (Stage 1: imputation of Ts, and Stage 2: estimation of Ta from Ts, respectively). The newly proposed model provides excellent computationally efficient estimation of near-surface air temperature at high resolution in both space and time, which helps further minimize exposure misclassification in epidemiological studies