Abstract:We examined the hypothesis that learning to write Chinese characters influences the brain's reading network for characters. Students from a college Chinese class learned 30 characters in a characterwriting condition and 30 characters in a pinyin-writing condition. After learning, functional magnetic resonance imaging collected during passive viewing showed different networks for reading Chinese characters and English words, suggesting accommodation to the demands of the new writing system through shortterm learning. Beyond these expected differences, we found specific effects of character writing in greater activation (relative to pinyin writing) in bilateral superior parietal lobules and bilateral lingual gyri in both a lexical decision and an implicit writing task. These findings suggest that character writing establishes a higher quality representation of the visual-spatial structure of the character and its orthography. We found a greater involvement of bilateral sensori-motor cortex (SMC) for character-writing trained characters than pinyin-writing trained characters in the lexical decision task, suggesting that learning by doing invokes greater interaction with sensori-motor information during character recognition. Furthermore, we found a correlation of recognition accuracy with activation in right superior parietal lobule, right lingual gyrus, and left SMC, suggesting that these areas support the facilitative effect character writing has on reading. Finally, consistent with previous behavioral studies, we found character-writing training facilitates connections with semantics by producing greater activation in bilateral middle temporal gyri, whereas pinyin-writing training facilitates connections with phonology by producing greater activation in right inferior frontal gyrus.

Genome in a Bottle (GIAB) benchmarks have been widely used to validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here we use accurate long and linked reads to expand the prior benchmark to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). We increase coverage of the GRCh38 assembly from 85 % to 92 %, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and assembly errors) that should not have been in the previous version. Our new benchmark reliably identifies both false positives and false negatives across multiple short-, linked-, and long-read based variant calling methods. As an example of its utility, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark, mostly in difficult-to-map regions. To enable robust small variant benchmarking, we still exclude 3.6% of GRCh37 and 5.0% of GRCh38 in (1) highly repetitive regions such as large, highly similar segmental duplications and the centromere not accessible to our data and (2) regions where our sample is highly divergent from the reference due to large indels, structural variation, copy number variation, and/or errors in the reference (e.g., some KIR genes that have duplications in HG002). We have demonstrated the utility of this benchmark to assess performance in more challenging regions, which enables benchmarking in more difficult genes and continued technology and bioinformatics development. The benchmarks are available at: ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/HG002_NA24385_son/NISTv4.1/ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/NIST_v4.2_SmallVariantDraftBenchmark_07092020/

The repetitive nature and complexity of some medically relevant genes poses a challenge for their accurate analysis in a clinical setting. The Genome in a Bottle Consortium has provided variant benchmark sets, but these exclude nearly four hundred medically relevant genes due to their repetitiveness or polymorphic complexity. Here we characterize 273 of these 395 challenging autosomal genes using a haplotype-resolved whole-genome assembly. This curated benchmark reports over 17,000 single nucleotide variations, 3,600 INDELs, and 200 structural variations each for human genome reference GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically relevant genes including CBS , CRYAA , and KCNE1 . When masking these false duplications, variant recall can improve from 8% to 100%. Forming benchmarks from a haplotype-resolved whole-genome assembly may become a prototype for future benchmarks covering the whole genome.

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Adult learners of Chinese learned new characters through writing, visual chunking or reading-only. Following training, ERPs were recorded during character recognition tasks, first shortly after the training and then three months later. We hypothesized that the character training effects would be seen in ERP components associated with word recognition and episodic memory. Results confirmed a larger N170 for visual chunking training than other training and a larger P600 for learned characters than novel characters. Another result was a training effect on the amplitude of the P100, which was greater following writing training than other training, suggesting that writing training temporarily lead to increased visual attention to the orthographic forms. Furthermore, P100 amplitude at the first post-test was positively correlated with character recall 3 months later. Thus the marker of early visual attention (P100) was predictive of retention of orthographic knowledge acquired in training.

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The repetitive nature and complexity of multiple medically important genes make them intractable to accurate analysis, despite the maturity of short-read sequencing, resulting in a gap in clinical applications of genome sequencing. The Genome in a Bottle Consortium has provided benchmark variant sets, but these excluded some medically relevant genes due to their repetitiveness or polymorphic complexity. In this study, we characterize 273 of these 395 challenging autosomal genes that have multiple implications for medical sequencing. This extended, curated benchmark reports over 17,000 SNVs, 3,600 INDELs, and 200 SVs each for GRCh37 and GRCh38. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically important genes including CBS, CRYAA, and KCNE1. Our proposed solution improves variant recall in these genes from 8% to 100%. This benchmark will significantly improve the comprehensive characterization of these medically relevant genes and guide new method development.

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