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1. To Read or Not to Read: Predicting Student Engagement in Interactive Reading

   https://doi.org/10.1007/978-3-031-64299-9_15  

   Beigman_Klebanov, Beata; Weeks, Jonathan; Sinharay, Sandip (July 2024, Journal of King Saud University Computer and information sciences)

   In this study, upper-elementary-age students used an interactive reading app to read from a classic children’s novel during a summer program. Students took turns reading with an adult virtual narrator (audiobook). We use process and background data to explore factors that could predict whether a reader will read their next turn or skip it. We find that skipping quickly becomes self-perpetuating, underscoring the need to support the teacher in providing just-in-time personalized intervention to help students avoid the disengagement trap. 

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2. Performance-Optimal Read-Only Transactions

   Lu, Haonan; Sen, Siddhartha; Lloyd, Wyatt (November 2020, 14th USENIX Symposium on Operating Systems Design and Implementation)

   null (Ed.)

   Read-only transactions are critical for consistently reading data spread across a distributed storage system but have worse performance than simple, non-transactional reads. We identify three properties of simple reads that are necessary for read-only transactions to be performance-optimal, i.e.,come as close as possible to simple reads. We demonstrate a fundamental tradeoff in the design of read-only transactions by proving that performance optimality is impossible to achieve with strict serializability, the strongest consistency.Guided by this result, we present PORT, a performance-optimal design with the strongest consistency to date. Central to PORT are version clocks, a specialized logical clock that concisely captures the necessary ordering constraints.We show the generality of PORT with two applications.Scylla-PORT provides process-ordered serializability with simple writes and shows performance comparable to its non-transactional base system. Eiger-PORT provides causal consistency with write transactions and significantly improves the performance of its transactional base system. 

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3. Exact Sketch-Based Read Mapping

   Schulz, Tizian; Medvedev, Paul (January 2023, 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023))
4. Metagenome-assembled genomes from Stordalen Mire, Sweden (2019) (MAGs from long-read, short-read, & hybrid assemblies)

   https://doi.org/10.5281/zenodo.14207415  

   Aroney, Samuel; Woodcroft, Ben J (November 2024, Zenodo)

   METHODS: Soil samples (6 total) were collected at the Stordalen Mire site in 2019 from two depths (1-5 & 20-24 cm below ground) across three habitats (Palsa, Bog, and Fen). DNA was extracted based on the protocol described by Li et al. (2024). For short reads, libraries were prepared at the Joint Genome Institute (JGI) with the KAPA Hyperprep kit, and sequenced with Illumina NovaSeq 6000. For long reads, libraries were prepared with the SMRTbell Express Template Prep Kit 2.0 (PacBio), then sequenced using PacBio Sequel IIe at JGI. PacBio data was processed at JGI to form filtered CCS (Circular Consensus Sequencing) reads.  Assemblies were generated with short-only, long-only, and hybrid read sources: Short-only was assembled with metaSPAdes (v3.15.4) using Aviary (v0.5.3) with default parameters. Long-only was assembled with metaFlye (v2.9-b1768) using Aviary (v0.5.3) with default parameters. Hybrid assembly was performed using Aviary v0.5.3 with default parameters. This involved a step-down procedure with long-read assembly through metaFlye (v2.9-b1768), followed by short-read polishing by Racon (v1.4.3), Pilon (v1.24) and then Racon again. Next, reads that didn't map to high-quality metaFlye contigs were hybrid assembled with SPAdes (--meta option) and binned out with MetaBAT2 (v2.1.5). For each bin, the reads within the bin were hybrid assembled using Unicycler (v0.4.8). The high-coverage metaFlye contigs and Unicycler contigs were then combined to form the assembly fasta file. Genome recovery was performed using Aviary v0.5.3 with samples chosen for differential abundance binning by Bin Chicken (v0.4.2) using SingleM metapackage S3.0.5. This involved initial read mapping through CoverM (v0.6.1) using minimap2 (v2.18) and binning by MetaBAT, MetaBAT2 (v2.1.5), VAMB (v3.0.2), SemiBin (v1.3.1), Rosella (v0.4.2), CONCOCT (v1.1.0) and MaxBin2 (v2.2.7). Genomes were analyzed using CheckM2 (v1.0.2) and clustered at 95% ANI using Galah (v0.4.0).   FILES: EMERGE_MAGs_2019_long-short-hybrid.tar.gz - Archive containing the MAG files (.fna). metadata_MAGs_2019_EMERGE.tsv - Table containing source sample names and accessions, GTDB classifications, CheckM2 quality information, NCBI GenomeBatch- and MIMAG(6.0)-formatted attributes, and other metadata for the MAGs.   FUNDING: This research is a contribution of the EMERGE Biology Integration Institute (https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440. We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164. Data from the Joint Genome Institute (JGI) was collected under BER Support Science Proposal 503530 (DOI: 10.46936/10.25585/60001148), conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. 

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5. ESKEMAP: exact sketch-based read mapping

   https://doi.org/10.1186/s13015-024-00261-7  

   Schulz, Tizian; Medvedev, Paul (December 2024, Algorithms for Molecular Biology)

   Abstract BackgroundGiven a sequencing read, the broad goal of read mapping is to find the location(s) in the reference genome that have a “similar sequence”. Traditionally, “similar sequence” was defined as having a high alignment score and read mappers were viewed as heuristic solutions to this well-defined problem. For sketch-based mappers, however, there has not been a problem formulation to capture what problem an exact sketch-based mapping algorithm should solve. Moreover, there is no sketch-based method that can find all possible mapping positions for a read above a certain score threshold. ResultsIn this paper, we formulate the problem of read mapping at the level of sequence sketches. We give an exact dynamic programming algorithm that finds all hits above a given similarity threshold. It runs in$$\mathcal {O} (|t| + |p| + \ell ^2)$$ $O\left(\right|t|+|p|+{\ell }^2)$time and$$\mathcal {O} (\ell \log \ell )$$ $O(\ell log\ell )$space, where |t| is the number of$$k$$ $k$-mers inside the sketch of the reference, |p| is the number of$$k$$ $k$-mers inside the read’s sketch and$$\ell$$ $\ell$is the number of times that$$k$$ $k$-mers from the pattern sketch occur in the sketch of the text. We evaluate our algorithm’s performance in mapping long reads to the T2T assembly of human chromosome Y, where ampliconic regions make it desirable to find all good mapping positions. For an equivalent level of precision as minimap2, the recall of our algorithm is 0.88, compared to only 0.76 of minimap2. 

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