Energy Efficient Error Resilient Multiplier Using Low-power Compressors

The approximate hardware design can save huge energy at the cost of errors incurred in the design. This article proposes the approximate algorithm for low-power compressors, utilized to build approximate multiplier with low energy and acceptable error profiles. This article presents two design approaches (DA1 and DA2) for higher bit size approximate multipliers. The proposed multiplier of DA1 have no propagation of carry signal from LSB to MSB, resulted in a very high-speed design. The increment in delay, power, and energy are not exponential with increment of multiplier size ( n ) for DA1 multiplier. It can be observed that the maximum combinations lie in the threshold Error Distance of 5% of the maximum value possible for any particular multiplier of size n . The proposed 4-bit DA1 multiplier consumes only 1.3 fJ of energy, which is 87.9%, 78%, 94%, 67.5%, and 58.9% less when compared to M1, M2, LxA, MxA, accurate designs respectively. The DA2 approach is recursive method, i.e., n -bit multiplier built with n/2-bit sub-multipliers. The proposed 8-bit multiplication has 92% energy savings with Mean Relative Error Distance (MRED) of 0.3 for the DA1 approach and at least 11% to 40% of energy savings with MRED of 0.08 for the DA2 approach. The proposed multipliers are employed in the image processing algorithm of DCT, and the quality is evaluated. The standard PSNR metric is 55 dB for less approximation and 35 dB for maximum approximation.

Verbal Working Memory Error Patterns and Speech-Language Outcomes in Youth With Cochlear Implants

Purpose: Verbal working memory (VWM) delays are commonly found in prelingually deaf youth with cochlear implants (CIs), albeit with considerable interindividual variability. However, little is known about the neurocognitive information-processing mechanisms underlying these delays and how these mechanisms relate to spoken language outcomes. The goal of this study was to use error analysis of the letter–number sequencing (LNS) task to test the hypothesis that VWM delays in CI users are due, in part, to fragile, underspecified phonological representations in short-term memory. Method: Fifty-one CI users aged 7–22 years and 53 normal hearing (NH) peers completed a battery of speech, language, and neurocognitive tests. LNS raw scores and error profiles were compared between samples, and a hierarchical regression model was used to test for associations with measures of speech, language, and hearing. Results: Youth with CIs scored lower on the LNS test than NH peers and committed a significantly higher number of errors involving phonological confusions (recalling an incorrect letter/digit in place of a phonologically similar one). More phonological errors were associated with poorer performance on measures of nonword repetition and following spoken directions but not with hearing quality. Conclusions: Study findings support the hypothesis that poorer VWM in deaf children with CIs is due, in part, to fragile, underspecified phonological representations in short-term/working memory, which underlie spoken language delays. Programs aimed at strengthening phonological representations may improve VWM and spoken language outcomes in CI users.

Parameter exploration improves the accuracy of long-read genome assembly

Long-molecule sequencing is now routinely applied to generate high-quality reference genome assemblies. However, datasets differ in repeat composition, heterozygosity, read lengths and error profiles. The assembly parameters that provide the best results could thus differ across datasets. By integrating four complementary and biologically meaningful metrics, we show that simple fine-tuning of assembly parameters can substantially improve the quality of long-read genome assemblies. In particular, modifying estimates of sequencing error rates improves some metrics more than two-fold. We provide a flexible software, CompareGenomeQualities, that automates comparisons of assembly qualities for researchers wanting a straightforward mechanism for choosing among multiple assemblies.

Sequencing error profiles of Illumina sequencing instruments

Sequencing technology has achieved great advances in the past decade. Studies have previously shown the quality of specific instruments in controlled conditions. Here, we developed a method able to retroactively determine the error rate of most public sequencing datasets. To do this, we utilized the overlaps between reads that are a feature of many sequencing libraries. With this method, we surveyed 1943 different datasets from seven different sequencing instruments produced by Illumina. We show that among public datasets, the more expensive platforms like HiSeq and NovaSeq have a lower error rate and less variation. But we also discovered that there is great variation within each platform, with the accuracy of a sequencing experiment depending greatly on the experimenter. We show the importance of sequence context, especially the phenomenon where preceding bases bias the following bases toward the same identity. We also show the difference in patterns of sequence bias between instruments. Contrary to expectations based on the underlying chemistry, HiSeq X Ten and NovaSeq 6000 share notable exceptions to the preceding-base bias. Our results demonstrate the importance of the specific circumstances of every sequencing experiment, and the importance of evaluating the quality of each one.

Kinetics and Fidelity of Psychrophilic DNA Polymerases

The discovery of extremophiles has enabled development of groundbreaking biotechnology. While most of the extremophile research is focused on thermophiles, organisms that adapt to living in cold temperature, known as psychrophiles, remain under-studied. We expressed and purified DNA polymerases PIPI and PIPB from Psychromonas ingrahamii, a psychrophile that can grow below water’s freezing temperature. We demonstrate that they have in vitro DNA replication activity at temperatures as low as −19°C. To our knowledge, this is the coolest DNA polymerization reaction ever carried out in a laboratory. In exploring the behavior of a variety of polymerases as a function of temperature, we found that reaction temperature substantially increases substitution and deletion error rates of both psychrophilic and mesophilic DNA polymerases. Motif analysis further reveals that the substitution error profiles cluster according to phylogenetic similarity of polymerases. Our results provide a useful reference for how reaction temperature, a crucial parameter of biochemistry, can affect the fidelity of DNA polymerases adapted to a wide range of environment.

VISOR: a versatile haplotype-aware structural variant simulator for short- and long-read sequencing

Summary VISOR is a tool for haplotype-specific simulations of simple and complex structural variants (SVs). The method is applicable to haploid, diploid or higher ploidy simulations for bulk or single-cell sequencing data. SVs are implanted into FASTA haplotypes at single-basepair resolution, optionally with nearby single-nucleotide variants. Short or long reads are drawn at random from these haplotypes using standard error profiles. Double- or single-stranded data can be simulated and VISOR supports the generation of haplotype-tagged BAM files. The tool further includes methods to interactively visualize simulated variants in single-stranded data. The versatility of VISOR is unmet by comparable tools and it lays the foundation to simulate haplotype-resolved cancer heterogeneity data in bulk or at single-cell resolution. Availability and implementation VISOR is implemented in python 3.6, open-source and freely available at https://github.com/davidebolo1993/VISOR. Documentation is available at https://davidebolo1993.github.io/visordoc/. Supplementary information Supplementary data are available at Bioinformatics online.

Fraction errors in a digital mathematics environment: Latent class and transition analysis

Student struggles with fractions are well documented, and due to fractions’ importance to later mathematics achievement, identification of the errors students make when solving fraction problems is an area of interest for both researchers and teachers. Within this study, we examine data on student fraction problem errors in pre- and post-quizzes in a digital mathematics environment. Students (n = 1,431) were grouped by prevalence of error types using latent class analysis. Three different classes of error profiles were identified in the pre-quiz data. A latent transition analysis was then used to determine if class membership and class structure changed from pre- to post-quiz. In both pre- and post-quiz, there was a class of students who appeared to be guessing and a class of students who performed well. One class structure was consistent with the idea that early fraction learners rely heavily on whole number principles. Identification of co-occurrence of and changes to fraction errors has implications for curricular design and pedagogical decisions, especially in light of movements toward personalized learning systems.

Long-read amplicon denoising

Long-read next-generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies from PacBio reads. Called ‘amplicon denoising’, this problem has been extensively studied for short-read sequencing technologies, but current solutions do not always successfully generalize to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower. On two Mock Virus Community datasets with ground truth, each sequenced on a different PacBio instrument, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method, successfully discriminating templates that differ by a just single nucleotide. Julia implementations of Fast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD), and a webserver interface, are freely available.