Convolutional neural networks can decode eye movement data: A black box approach to predicting task from eye movements

Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes and/or data that have been processed into aggregate (e.g., fixations, saccades) or statistical (e.g., fixation density) features. Black box convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally processed data and images, but difficulty interpreting these model architectures has contributed to challenges in generalizing lab-trained CNNs to applied contexts. In the current study, a CNN classifier was used to classify task from two eye movement datasets (Exploratory and Confirmatory) in which participants searched, memorized, or rated indoor and outdoor scene images. The Exploratory dataset was used to tune the hyperparameters of the model, and the resulting model architecture was retrained, validated, and tested on the Confirmatory dataset. The data were formatted into timelines (i.e., x-coordinate, y-coordinate, pupil size) and minimally processed images. To further understand the informational value of each component of the eye movement data, the timeline and image datasets were broken down into subsets with one or more components systematically removed. Classification of the timeline data consistently outperformed the image data. The Memorize condition was most often confused with Search and Rate. Pupil size was the least uniquely informative component when compared with the x- and y-coordinates. The general pattern of results for the Exploratory dataset was replicated in the Confirmatory dataset. Overall, the present study provides a practical and reliable black box solution to classifying task from eye movement data

Applicant Reactions to the AAMC Standardized Video Interview During the 2018 Application Cycle

PURPOSE: This study examined applicant reactions to the Association of American Medical Colleges (AAMC) Standardized Video Interview (SVI) during its first year of operational use in emergency medicine (EM) residency program selection in order to identify strategies to improve applicants\u27 SVI experience and attitudes. METHOD: Individuals who self-classified as EM applicants applying in the Electronic Residency Application Service 2018 cycle and completed the SVI in summer 2017 were invited to participate in two surveys. Survey 1, which focused on procedural issues, was administered immediately after SVI completion. Survey 2, which focused on applicants\u27 SVI experience, was administered in fall 2017, after SVI scores were released. RESULTS: The response rates for surveys 1 and 2 were 82.3% (2,906/3,532) and 58.7% (2,074/3,532), respectively. Applicant reactions varied by aspect of the SVI studied and their SVI total scores. Most applicants were satisfied with most procedural aspects of the SVI, but most applicants were not satisfied with the SVI overall or with their total SVI scores. About 20-30% of applicants had neutral opinions about most aspects of the SVI. Negative reactions to the SVI were stronger for applicants who scored lower on the SVI. CONCLUSIONS: Applicants had generally negative reactions to the SVI. Most were skeptical of its ability to assess the target competencies and its potential to add value to the selection process. Applicant acceptance and appreciation of the SVI will be critical to the SVI\u27s acceptance by the graduate medical education community

The WiggleZ Dark Energy Survey: final data release and the metallicity of UV-luminous galaxies

The WiggleZ Dark Energy Survey measured the redshifts of over 200 000 ultraviolet (UV)- selected (NUV < 22.8 mag) galaxies on the Anglo-Australian Telescope. The survey detected the baryon acoustic oscillation signal in the large-scale distribution of galaxies over the redshift range 0.2 < z < 1.0, confirming the acceleration of the expansion of the Universe and measuring the rate of structure growth within it. Here, we present the final data release of the survey: a catalogue of 225 415 galaxies and individual files of the galaxy spectra. We analyse the emission-line properties of these UV-luminous Lyman-break galaxies by stacking the spectra in bins of luminosity, redshift, and stellar mass. The most luminous (-25 mag < MFUV < -22 mag) galaxies have very broad Hβ emission from active nuclei, as well as a broad second component to the [OIII] (495.9 nm, 500.7 nm) doublet lines that is blueshifted by 100 km s-1, indicating the presence of gas outflows in these galaxies. The composite spectra allow us to detect and measure the temperature-sensitive [O III] (436.3 nm) line and obtain metallicities using the direct method. The metallicities of intermediate stellar mass (8.8 < log (M*/M⊙) < 10)WiggleZ galaxies are consistent with normal emission-line galaxies at the samemasses. In contrast, the metallicities of high stellarmass (10 < log (M*/M⊙) < 12) WiggleZ galaxies are significantly lower than for normal emission-line galaxies at the same masses. This is not an effect of evolution as the metallicities do not vary with redshift; it is most likely a property specific to the extremely UV-luminous WiggleZ galaxies

Perturbative Instabilities on the Non-Commutative Torus, Morita Duality and Twisted Boundary Conditions

We study one-loop corrections in scalar and gauge field theories on the non-commutative torus. For rational theta, Morita equivalence allows these theories to be reformulated in terms of ordinary theories on a commutative torus with twisted boundary conditions. UV/IR mixing does not lead to singularities, however there can be large corrections. In particular, gauge theories show tachyonic instabilities for some of the modes. We discuss their relevance to spontaneous Z_N x Z_N symmetry breaking in the Morita dual SU(N) theory due to electric flux condensation.Comment: 30 page

Whole genome sequencing for the genetic diagnosis of heterogenous dystonia phenotypes

Introduction: Dystonia is a clinically and genetically heterogeneous disorder and a genetic cause is often difficult to elucidate. This is the first study to use whole genome sequencing (WGS) to investigate dystonia in a large sample of affected individuals. Methods: WGS was performed on 111 probands with heterogenous dystonia phenotypes. We performed analysis for coding and non-coding variants, copy number variants (CNVs), and structural variants (SVs). We assessed for an association between dystonia and 10 known dystonia risk variants. Results: A genetic diagnosis was obtained for 11.7% (13/111) of individuals. We found that a genetic diagnosis was more likely in those with an earlier age at onset, younger age at testing, and a combined dystonia phenotype. We identified pathogenic/likely-pathogenic variants in ADCY5 (n = 1), ATM (n = 1), GNAL (n = 2), GLB1 (n = 1), KMT2B (n = 2), PRKN (n = 2), PRRT2 (n = 1), SGCE (n = 2), and THAP1 (n = 1). CNVs were detected in 3 individuals. We found an association between the known risk variant ARSG rs11655081 and dystonia (p = 0.003). Conclusion: A genetic diagnosis was found in 11.7% of individuals with dystonia. The diagnostic yield was higher in those with an earlier age of onset, younger age at testing, and a combined dystonia phenotype. WGS may be particularly relevant for dystonia given that it allows for the detection of CNVs, which accounted for 23% of the genetically diagnosed cases. © 2019 The Author

Multiple Levels of Synergistic Collaboration in Termite Lignocellulose Digestion

In addition to evolving eusocial lifestyles, two equally fascinating aspects of termite biology are their mutualistic relationships with gut symbionts and their use of lignocellulose as a primary nutrition source. Termites are also considered excellent model systems for studying the production of bioethanol and renewable bioenergy from 2nd generation (non-food) feedstocks. While the idea that gut symbionts are the sole contributors to termite lignocellulose digestion has remained popular and compelling, in recent years host contributions to the digestion process have become increasingly apparent. However, the degree to which host and symbiont, and host enzymes, collaborate in lignocellulose digestion remain poorly understood. Also, how digestive enzymes specifically collaborate (i.e., in additive or synergistic ways) is largely unknown. In the present study we undertook translational-genomic studies to gain unprecedented insights into digestion by the lower termite Reticulitermes flavipes and its symbiotic gut flora. We used a combination of native gut tissue preparations and recombinant enzymes derived from the host gut transcriptome to identify synergistic collaborations between host and symbiont, and also among enzymes produced exclusively by the host termite. Our findings provide important new evidence of synergistic collaboration among enzymes in the release of fermentable monosaccharides from wood lignocellulose. These monosaccharides (glucose and pentoses) are highly relevant to 2nd-generation bioethanol production. We also show that, although significant digestion capabilities occur in host termite tissues, catalytic tradeoffs exist that apparently favor mutualism with symbiotic lignocellulose-digesting microbes. These findings contribute important new insights towards the development of termite-derived biofuel processing biotechnologies and shed new light on selective forces that likely favored symbiosis and, subsequently, group living in primitive termites and their cockroach ancestors

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment