1,080 research outputs found
A Reductionist Approach in Curricular Planning for Teaching Language Arts
Contemporary education faces multiple challenges that encumber today’s public school teachers, especially those in English Language Arts (ELA). One aspect remaining consistent over the decades is the imbalance between the amount of curricular material teachers are directed to teach and the time school districts allot to do it. It is likely a contributing factor to burnout and attrition in the faculty workforce. This essay presents counterintuitive reasons for proposing the implementation of a “proof of concept” intra-school research project that would demonstrate the potential value of a reductionist approach to the amount of content required in curricular designs. It may have the potential to increase cognitive capability of students along with reducing stress on teachers; not only by curtailing the number of texts for student study, but by incorporating methodologies of how texts are selected, analyzed and taught as well as students’ creation of their own
Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping
Gathering information about forest variables is an expensive and arduous
activity. As such, directly collecting the data required to produce
high-resolution maps over large spatial domains is infeasible. Next generation
collection initiatives of remotely sensed Light Detection and Ranging (LiDAR)
data are specifically aimed at producing complete-coverage maps over large
spatial domains. Given that LiDAR data and forest characteristics are often
strongly correlated, it is possible to make use of the former to model,
predict, and map forest variables over regions of interest. This entails
dealing with the high-dimensional () spatially dependent LiDAR
outcomes over a large number of locations (~10^5-10^6). With this in mind, we
develop the Spatial Factor Nearest Neighbor Gaussian Process (SF-NNGP) model,
and embed it in a two-stage approach that connects the spatial structure found
in LiDAR signals with forest variables. We provide a simulation experiment that
demonstrates inferential and predictive performance of the SF-NNGP, and use the
two-stage modeling strategy to generate complete-coverage maps of forest
variables with associated uncertainty over a large region of boreal forests in
interior Alaska
Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping
Gathering information about forest variables is an expensive and arduous
activity. As such, directly collecting the data required to produce
high-resolution maps over large spatial domains is infeasible. Next generation
collection initiatives of remotely sensed Light Detection and Ranging (LiDAR)
data are specifically aimed at producing complete-coverage maps over large
spatial domains. Given that LiDAR data and forest characteristics are often
strongly correlated, it is possible to make use of the former to model,
predict, and map forest variables over regions of interest. This entails
dealing with the high-dimensional () spatially dependent LiDAR
outcomes over a large number of locations (~10^5-10^6). With this in mind, we
develop the Spatial Factor Nearest Neighbor Gaussian Process (SF-NNGP) model,
and embed it in a two-stage approach that connects the spatial structure found
in LiDAR signals with forest variables. We provide a simulation experiment that
demonstrates inferential and predictive performance of the SF-NNGP, and use the
two-stage modeling strategy to generate complete-coverage maps of forest
variables with associated uncertainty over a large region of boreal forests in
interior Alaska
The Golden Rule:Interfaith Peacemaking and the Charter for Compassion
The Charter for Compassion has been signed by over two million people from around the world and partnered with hundreds of interfaith organizations and cities seeking to put into practice the Golden Rule, common to the main faith traditions, of doing unto others as you would be done by. This article sets the Charter within the context of a post secular international society and faith-based diplomacy, in which religious interreligious initiatives emerge as serious, rather than peripheral, actors in developing sustainable peace making through bottom-up approaches. The article critically engages with the Charter's claim that ‘any interpretation of scripture that breeds violence, hatred or disdain is illegitimate’ while accepting that peaceful interpretations of scriptures are helpful to peace processes where religious actors are involved. The article explores the claims of the Charter for Compassion International as they seek to make peace through compassion, before concluding that the Charter for Compassion is a long-term project aimed at changing hearts and minds but has had limited substantive impact to date
Cosmological parameters from SDSS and WMAP
We measure cosmological parameters using the three-dimensional power spectrum
P(k) from over 200,000 galaxies in the Sloan Digital Sky Survey (SDSS) in
combination with WMAP and other data. Our results are consistent with a
``vanilla'' flat adiabatic Lambda-CDM model without tilt (n=1), running tilt,
tensor modes or massive neutrinos. Adding SDSS information more than halves the
WMAP-only error bars on some parameters, tightening 1 sigma constraints on the
Hubble parameter from h~0.74+0.18-0.07 to h~0.70+0.04-0.03, on the matter
density from Omega_m~0.25+/-0.10 to Omega_m~0.30+/-0.04 (1 sigma) and on
neutrino masses from <11 eV to <0.6 eV (95%). SDSS helps even more when
dropping prior assumptions about curvature, neutrinos, tensor modes and the
equation of state. Our results are in substantial agreement with the joint
analysis of WMAP and the 2dF Galaxy Redshift Survey, which is an impressive
consistency check with independent redshift survey data and analysis
techniques. In this paper, we place particular emphasis on clarifying the
physical origin of the constraints, i.e., what we do and do not know when using
different data sets and prior assumptions. For instance, dropping the
assumption that space is perfectly flat, the WMAP-only constraint on the
measured age of the Universe tightens from t0~16.3+2.3-1.8 Gyr to
t0~14.1+1.0-0.9 Gyr by adding SDSS and SN Ia data. Including tensors, running
tilt, neutrino mass and equation of state in the list of free parameters, many
constraints are still quite weak, but future cosmological measurements from
SDSS and other sources should allow these to be substantially tightened.Comment: Minor revisions to match accepted PRD version. SDSS data and ppt
figures available at http://www.hep.upenn.edu/~max/sdsspars.htm
The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe
The preponderance of matter over antimatter in the early Universe, the
dynamics of the supernova bursts that produced the heavy elements necessary for
life and whether protons eventually decay --- these mysteries at the forefront
of particle physics and astrophysics are key to understanding the early
evolution of our Universe, its current state and its eventual fate. The
Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed
plan for a world-class experiment dedicated to addressing these questions. LBNE
is conceived around three central components: (1) a new, high-intensity
neutrino source generated from a megawatt-class proton accelerator at Fermi
National Accelerator Laboratory, (2) a near neutrino detector just downstream
of the source, and (3) a massive liquid argon time-projection chamber deployed
as a far detector deep underground at the Sanford Underground Research
Facility. This facility, located at the site of the former Homestake Mine in
Lead, South Dakota, is approximately 1,300 km from the neutrino source at
Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino
charge-parity symmetry violation and mass ordering effects. This ambitious yet
cost-effective design incorporates scalability and flexibility and can
accommodate a variety of upgrades and contributions. With its exceptional
combination of experimental configuration, technical capabilities, and
potential for transformative discoveries, LBNE promises to be a vital facility
for the field of particle physics worldwide, providing physicists from around
the globe with opportunities to collaborate in a twenty to thirty year program
of exciting science. In this document we provide a comprehensive overview of
LBNE's scientific objectives, its place in the landscape of neutrino physics
worldwide, the technologies it will incorporate and the capabilities it will
possess.Comment: Major update of previous version. This is the reference document for
LBNE science program and current status. Chapters 1, 3, and 9 provide a
comprehensive overview of LBNE's scientific objectives, its place in the
landscape of neutrino physics worldwide, the technologies it will incorporate
and the capabilities it will possess. 288 pages, 116 figure
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
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