The Effect of Legal Status on Immigrant Wages and Occupational Skills

Native and foreign-born workers with a high school degree or less educational attainment provide unique occupational skills to the US labor force. This regularity might be driven, in part, by limited access to occupations for immigrants lacking legal rights to work in the US. This paper exploits exogenous policy change induced by the 1986 Immigration Reform and Control Act (IRCA) to perform triple-difference estimation examining whether legal status causes immigrants to work in occupations that use skills more similar to those of native-born workers. We find that legal status decreases the manual skill intensity of Mexican immigrants by two percentiles. It increases communication skill intensity by an equivalent amount. This effect reduces the skill gap between Mexican-born and native-born American workers by 13%

The Performance Impact of Advance Reservation Meta-scheduling

Abstract As supercomputing resources become more available, users will require resources man-aged by several local schedulers. For example, a user may request 100 processors, a telescope, network bandwidth and a graphics display in order to perform an experiment. In order to gain access to all of these resources (some of which may be in dierent geographical and administrative domains), current systems require meta-jobs like this to run during locked down periods when the resources are only available for meta job use. It is more convenient and eÆcient if the user is able to make a reservation at the soonest time when all of these resources are available. Low utiliza-tion during lock down periods can also be eliminated when meta-jobs are interleaved with existing local usage. System administrators are reluctant to allow reservations external to locked down pe-riods because of the impact reservations may have on utilization and the Quality of Service that the center is able to provide to its normal users. This research quanties the impact of advance reservations on supercomputing center metrics. It also outlines the algorithms that must be used to schedule meta-jobs. The Maui scheduler is used to examine metascheduling using trace les from existing supercomputing centers. These results indicate that advance reservations can improve the response time of supercomputing centers for meta-jobs, while not signicantly impacting overall sys-tem performance. The appropriate balance between meta-jobs and local jobs is also specied using experimental results

Mechanism of imidazolium ionic liquids toxicity in Saccharomyces cerevisiae and rational engineering of a tolerant, xylose-fermenting strain

Additional file 3. Fermentation profiles of Y133 and Y133-IIL in the presence of 1 % [BMIM]Cl at pH 6.5 and pH 5.0, and either aerobic or anaerobic conditions (n = 3, Mean ± S.E, except n = 2 for Y133 pH 6.5 anaerobic 72 h)

PhotoAffinity bits : a photoaffinity-based fragment screening platform for efficient identification of protein ligands

Advances in genomic analyses enable the identification of new proteins that are associated with disease. To validate these targets, tool molecules are required to demonstrate that a ligand can have a disease-modifying effect. Currently, as tools are reported for only a fraction of the proteome, platforms for ligand discovery are essential to leverage insights from genomic analyses. Fragment screening offers an efficient approach to explore chemical space, however, it remains challenging to develop techniques that are both sufficiently high-throughput and sensitive. We present a fragment screening platform, termed PhABits (PhotoAffinity Bits), which utilises a library of photoreactive fragments to covalently capture fragment-protein interactions. Hits can be profiled to determine potency and site of crosslinking, and subsequently developed as reporters in a competitive displacement assay to identify novel hit matter. We envision that the PhABits will be widely applicable to novel protein targets, identifying starting points in the development of therapeutic

RetroSnake: A modular pipeline to detect human endogenous retroviruses in genome sequencing data

Human endogenous retroviruses (HERVs) integrated into the human genome as a result of ancient exogenous infections and currently comprise ∼8% of our genome. The members of the most recently acquired HERV family, HERV-Ks, still retain the potential to produce viral molecules and have been linked to a wide range of diseases including cancer and neurodegeneration. Although a range of tools for HERV detection in NGS data exist, most of them lack wet lab validation and they do not cover all steps of the analysis. Here, we describe RetroSnake, an end-to-end, modular, computationally efficient, and customizable pipeline for the discovery of HERVs in short-read NGS data. RetroSnake is based on an extensively wet-lab validated protocol, it covers all steps of the analysis from raw data to the generation of annotated results presented as an interactive html file, and it is easy to use by life scientists without substantial computational training. Availability and implementation: The Pipeline and an extensive documentation are available on GitHub

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 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

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

Detection of Epileptogenic Cortical Malformations with Surface-Based MRI Morphometry

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery