Supporting teachers’ use of data-based instruction to improve students’ early writing skills

We examined the effects of a professional development (PD) system designed to support teachers’ use of data-based instruction (DBI) to improve early writing outcomes for children with intensive needs. The PD system, called DBI-TLC, provided tools for early writing assessment and intervention, learning modules including face-to-face workshops followed by classroom application, and ongoing coaching to support DBI implementation. Special education teachers in 19 classrooms in 2 Midwestern districts were assigned randomly to receive DBI-TLC or to a business-as-usual control group. All teachers completed pre- and posttests of DBI knowledge and skills and self-efficacy, and DBI-TLC teachers’ fidelity to DBI was assessed. Fifty-three students (2 to 3 from each classroom) completed pre- and posttests of early writing using curriculum-based measures (CBM) and the Test of Early Written Language-3 (TEWL-3). DBI-TLC teachers outperformed controls at posttest on DBI knowledge and skills (Hedge’s g = 2.88) and reported a more explicit writing instruction orientation compared to controls (g = 1.63). DBI fidelity varied (on average, 84% for assessment, 79% for intervention, and 52% for decision-making). Students whose teachers implemented DBI showed a pattern of stronger early writing performance compared to control students on CBM, with effect sizes of 0.23 to 0.40, but not on the TEWL-3 (0.02 to 0.13). We discuss the promise of DBI-TLC to improve teacher practice and student outcomes, as well as the need to continue to explore ways to support teachers’ implementation of DBI with fidelity. (PsycINFO Database Record (c) 2019 APA, all rights reserved

Single-spin Azimuthal Asymmetries in Electroproduction of Neutral Pions in Semi-inclusive Deep-inelastic Scattering

A single-spin asymmetry in the azimuthal distribution of neutral pions relative to the lepton scattering plane has been measured for the first time in deep-inelastic scattering of positrons off longitudinally polarized protons. The analysing power in the sin(phi) moment of the cross section is 0.019 +/- 0.007(stat.) +/- 0.003(syst.). This result is compared to single-spin asymmetries for charged pion production measured in the same kinematic range. The pi^0 asymmetry is of the same size as the pi^+ asymmetry and shows a similar dependence on the relevant kinematic variables. The asymmetry is described by a phenomenological calculation based on a fragmentation function that represents sensitivity to the transverse polarization of the struck quark.Comment: 4 pages, 1 figure, replaced to correct eprint author field, 2nd replacement to correct figure; upper limit of model predictions are corrected. No correction to data or conclusion

Evidence for Quark-Hadron Duality in the Proton Spin Asymmetry A1A_1

Spin-dependent lepton-nucleon scattering data have been used to investigate the validity of the concept of quark-hadron duality for the spin asymmetry $A_1$. Longitudinally polarised positrons were scattered off a longitudinally polarised hydrogen target for values of $Q^2$ between 1.2 and 12 GeV$^2$ and values of $W^2$ between 1 and 4 GeV$^2$. The average double-spin asymmetry in the nucleon resonance region is found to agree with that measured in deep-inelastic scattering at the same values of the Bjorken scaling variable $x$. This finding implies that the description of $A_1$ in terms of quark degrees of freedom is valid also in the nucleon resonance region for values of $Q^2$ above 1.6 GeV$^2$.Comment: 5 pages, 1 eps figure, table added, new references added, in print in Phys. Rev. Let

Double-Spin Asymmetry in the Cross Section for Exclusive rho^0 Production in Lepton-Proton Scattering

Evidence for a positive longitudinal double-spin asymmetry = 0.24 +-0.11 (stat) +-0.02 (syst) in the cross section for exclusive diffractive rho^0(770) vector meson production in polarised lepton-proton scattering was observed by the HERMES experiment. The longitudinally polarised 27.56 GeV HERA positron beam was scattered off a longitudinally polarised pure hydrogen gas target. The average invariant mass of the photon-proton system has a value of = 4.9 GeV, while the average negative squared four-momentum of the virtual photon is = 1.7 GeV^2. The ratio of the present result to the corresponding spin asymmetry in inclusive deep-inelastic scattering is in agreement with an early theoretical prediction based on the generalised vector meson dominance model.Comment: 10 pages, 4 embedded figures, LaTe

Physicochemical property distributions for accurate and rapid pairwise protein homology detection

<p>Abstract</p> <p>Background</p> <p>The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.</p> <p>Results</p> <p>We introduce a new method for feature vector representation based on the physicochemical properties of the primary protein sequence. A distribution of physicochemical property scores are assembled from 4-mers of the sequence and normalized based on the null distribution of the property over all possible 4-mers. With this approach there is little computational cost associated with the transformation of the protein into feature space, and overall performance in terms of remote homology detection is comparable with current state-of-the-art methods. We demonstrate that the features can be used for the task of pairwise remote homology detection with improved accuracy versus sequence-based methods such as BLAST and other feature-based methods of similar computational cost.</p> <p>Conclusions</p> <p>A protein feature method based on physicochemical properties is a viable approach for extracting features in a computationally inexpensive manner while retaining the sensitivity of SVM protein homology detection. Furthermore, identifying features that can be used for generic pairwise homology detection in lieu of family-based homology detection is important for applications such as large database searches and comparative genomics.</p

Synthesis of Highly Functionalized Oligobenzamide Proteomimetic Foldamers by Late Stage Introduction of Sensitive Groups

α-Helix proteomimetics represent an emerging class of ligands that can be used to inhibit an array of helix mediated protein-protein interactions. Within this class of inhibitor, aromatic oligobenzamide foldamers have been widely and succssefuly used. This manuscript describes alternative syntheses of these compounds that can be used to access mimetics that are challenging to synthesize using previously described methodologies, permiting access to compounds functionalized with multiple sensitive side chains and accelerated library assembly through late stage derivatisation

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