Comparing the Accuracy of Automatic Scoring Solutions for a Text Comprehension Diagramming Intervention

Students typically have great difficulty monitoring their comprehension of textual materials. Completing diagrams about causal relations in expository texts has been a successful intervention to enhance the accuracy of students’ reading comprehension judgments (ie, monitoring accuracy), although there is still room for improvement. Such judgments play a role in crucial self-regulated learning decisions that students make such as allocating time and effort, selecting content for restudy, and/or consulting additional sources. The automated scoring of students’ diagram content can provide a basis for strengthening the diagramming intervention with individual and simultaneous feedback to a high number of students. Leveraging an existing human-coded (correct and incorrect) dataset of 6000+ diagram answers (completed in Dutch by 700+ secondary students), we compared different automatic scoring solutions in terms of classification accuracy. Four computational linguistic models for Dutch were identified and tested in combination with four popular machine learning classification algorithms. The best solution reached 81% accuracy (ie, four out of five answers matched the human coding). Depending on the accuracy required for different applications, these results could be used for fully-or semiautomated scorings of students’ answers to generative activities used in reading comprehension interventions

Gene expression analysis of cell death induction by Taurolidine in different malignant cell lines

<p>Abstract</p> <p>Background</p> <p>The anti-infective agent Taurolidine (TRD) has been shown to have cell death inducing properties, but the mechanism of its action is largely unknown. The aim of this study was to identify potential common target genes modulated at the transcriptional level following TRD treatment in tumour cell lines originating from different cancer types.</p> <p>Methods</p> <p>Five different malignant cell lines (HT29, Chang Liver, HT1080, AsPC-1 and BxPC-3) were incubated with TRD (100 μM, 250 μM and 1000 μM). Proliferation after 8 h and cell viability after 24 h were analyzed by BrdU assay and FACS analysis, respectively. Gene expression analyses were carried out using the <it>Agilent </it>-microarray platform to indentify genes which displayed conjoint regulation following the addition of TRD in all cell lines. Candidate genes were subjected to <it>Ingenuity Pathways Analysis </it>and selected genes were validated by qRT-PCR and Western Blot.</p> <p>Results</p> <p>TRD 250 μM caused a significant inhibition of proliferation as well as apoptotic cell death in all cell lines. Among cell death associated genes with the strongest regulation in gene expression, we identified pro-apoptotic transcription factors (EGR1, ATF3) as well as genes involved in the ER stress response (PPP1R15A), in ubiquitination (TRAF6) and mitochondrial apoptotic pathways (PMAIP1).</p> <p>Conclusions</p> <p>This is the first conjoint analysis of potential target genes of TRD which was performed simultaneously in different malignant cell lines. The results indicate that TRD might be involved in different signal transduction pathways leading to apoptosis.</p

Automated detection of regions of interest for tissue microarray experiments: an image texture analysis

BACKGROUND: Recent research with tissue microarrays led to a rapid progress toward quantifying the expressions of large sets of biomarkers in normal and diseased tissue. However, standard procedures for sampling tissue for molecular profiling have not yet been established. METHODS: This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B), percentage occupied by stroma-like regions (P), and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states. RESULTS: Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies. CONCLUSION: These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists) as hundreds of tumors that are used to develop an array have typically been evaluated (graded) by different pathologists. The region of interest information gathered from the whole section images will guide the excision of tissue for constructing tissue microarrays and for high throughput profiling of global gene expression

De novo Assembly of a 40 Mb Eukaryotic Genome from Short Sequence Reads: Sordaria macrospora, a Model Organism for Fungal Morphogenesis

Filamentous fungi are of great importance in ecology, agriculture, medicine, and biotechnology. Thus, it is not surprising that genomes for more than 100 filamentous fungi have been sequenced, most of them by Sanger sequencing. While next-generation sequencing techniques have revolutionized genome resequencing, e.g. for strain comparisons, genetic mapping, or transcriptome and ChIP analyses, de novo assembly of eukaryotic genomes still presents significant hurdles, because of their large size and stretches of repetitive sequences. Filamentous fungi contain few repetitive regions in their 30–90 Mb genomes and thus are suitable candidates to test de novo genome assembly from short sequence reads. Here, we present a high-quality draft sequence of the Sordaria macrospora genome that was obtained by a combination of Illumina/Solexa and Roche/454 sequencing. Paired-end Solexa sequencing of genomic DNA to 85-fold coverage and an additional 10-fold coverage by single-end 454 sequencing resulted in ∼4 Gb of DNA sequence. Reads were assembled to a 40 Mb draft version (N50 of 117 kb) with the Velvet assembler. Comparative analysis with Neurospora genomes increased the N50 to 498 kb. The S. macrospora genome contains even fewer repeat regions than its closest sequenced relative, Neurospora crassa. Comparison with genomes of other fungi showed that S. macrospora, a model organism for morphogenesis and meiosis, harbors duplications of several genes involved in self/nonself-recognition. Furthermore, S. macrospora contains more polyketide biosynthesis genes than N. crassa. Phylogenetic analyses suggest that some of these genes may have been acquired by horizontal gene transfer from a distantly related ascomycete group. Our study shows that, for typical filamentous fungi, de novo assembly of genomes from short sequence reads alone is feasible, that a mixture of Solexa and 454 sequencing substantially improves the assembly, and that the resulting data can be used for comparative studies to address basic questions of fungal biology

A chromosome conformation capture ordered sequence of the barley genome

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