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DNA is an ultrahigh-density storage medium that could meet exponentially growing worldwide demand for archival data storage if DNA synthesis costs declined sufficiently and if random access of files within exabyte-to-yottabyte-scale DNA data pools were feasible. Here, we demonstrate a path to overcome the second barrier by encapsulating data-encoding DNA file sequences within impervious silica capsules that are surface labelled with single-stranded DNA barcodes. Barcodes are chosen to represent file metadata, enabling selection of sets of files with Boolean logic directly, without use of amplification. We demonstrate random access of image files from a prototypical 2-kilobyte image database using fluorescence sorting with selection sensitivity of one in 106 files, which thereby enables one in 106N selection capability using N optical channels. Our strategy thereby offers a scalable concept for random access of archival files in large-scale molecular datasets.

DNA is an ultra-high-density storage medium that could meet exponentially growing worldwide demand for archival data storage if DNA synthesis costs declined sufficiently and random access of files within exabyte-to-yottabyte-scale DNA data pools were feasible. To overcome the second barrier, here we encapsulate data-encoding DNA file sequences within impervious silica capsules that are surface-labeled with single-stranded DNA barcodes. Barcodes are chosen to represent file metadata, enabling efficient and direct selection of sets of files with Boolean logic. We demonstrate random access of image files from an image database using fluorescence sorting with selection sensitivity of 1 in 106 files, which thereby enables 1 in 106N per N optical channels. Our strategy thereby offers retrieval of random file subsets from exabyte and larger-scale long-term DNA file storage databases, offering a scalable solution for random-access of archival files in massive molecular datasets.

It has been observed that the performing for high stakes can, paradoxically, lead to uncharacteristically poor performance. Here we investigate a novel approach to attenuating such ‘choking under pressure’ by instructing participants performing a demanding motor task that rewards successful performance with a monetary gain, to reappraise this incentive as a monetary loss for unsuccessful performance. We show that when participants applied this simple strategy, choking was significantly reduced. This strategy also influenced participants’ neural and physiological activity. When participants reappraised the incentive as a potential monetary loss, the representation of the magnitude of the incentive in the ventral striatum Blood Oxygenation Level Dependent (BOLD) signal was attenuated. In addition, individual differences in the degree of attenuation of the neural response to incentive predicted the effectiveness of the reappraisal strategy in reducing choking. Furthermore, participants’ skin conductance changed in proportion to the magnitude of the incentive being played for, and was exaggerated on high incentive trials on which participants failed. Reappraisal of the incentive abolished this exaggerated skin conductance response. This represents the first experimental association of sympathetic arousal with choking. Taken together, these results suggest that reappraisal of the incentive is indeed a promising intervention for attenuating choking under pressure.

Motivation: The study of T cell receptor (TCR) repertoires has generated new insights into immune system recognition. However, the ability to robustly characterize these populations has been limited by technical barriers and an inability to reliably infer heterodimeric chain pairings for TCRs. Results: Here, we describe a novel analytical approach to an emerging immune repertoire sequencing method, improving the resolving power of this low-cost technology. This method relies upon the distribution of a T cell population across a 96-well plate, followed by barcoding and sequencing of the relevant transcripts from each T cell. Multicell Analytical Deconvolution for High Yield Paired-chain Evaluation (MAD-HYPE) uses Bayesian inference to more accurately extract TCR information, improving our ability to study and characterize T cell populations for immunology and immunotherapy applications. Availability and implementation: The MAD-HYPE algorithm is released as an open-source project under the Apache License and is available from https://github.com/birnbaumlab/MAD-HYPE., National Science Foundation (Grant PHY-1305537), National Science Foundation (Grant PHY-1707999), National Institutes of Health (Grant P30-CA14051)

Biochemical circuits made of rationally designed DNA molecules are proofs of concept for embedding control within complex molecular environments. They hold promise for transforming the current technologies in chemistry, biology, medicine and material science by introducing programmable and responsive behaviour to diverse molecular systems. As the transformative power of a technology depends on its accessibility, two main challenges are an automated design process and simple experimental procedures. Here we demonstrate the use of circuit design software, combined with the use of unpurified strands and simplified experimental procedures, for creating a complex DNA strand displacement circuit that consists of 78 distinct species. We develop a systematic procedure for overcoming the challenges involved in using unpurified DNA strands. We also develop a model that takes synthesis errors into consideration and semi-quantitatively reproduces the experimental data. Our methods now enable even novice researchers to successfully design and construct complex DNA strand displacement circuits., DNA circuits hold promise for advancing information-based molecular technologies, yet it is challenging to design and construct them in practice. Thubagere et al. build DNA strand displacement circuits using unpurified strands whose sequences are automatically generated from a user-friendly compiler.

As an engineering material, DNA is well suited for the construction of biochemical circuits and systems, because it is simple enough that its interactions can be rationally designed using Watson–Crick base pairing rules, yet the design space is remarkably rich. When designing DNA systems, this simplicity permits using functional sections of each strand, called domains, without considering particular nucleotide sequences. However, the actual sequences used may have interactions not predicted at the domain-level abstraction, and new rigorous analysis techniques are needed to determine the extent to which the chosen sequences conform to the system’s domain-level description. We have developed a computational method for verifying sequence-level systems by identifying discrepancies between the domain-level and sequence-level behaviour. This method takes a DNA system, as specified using the domain-level tool Peppercorn, and analyses data from the stochastic sequence-level simulator Multistrand and sequence-level thermodynamic analysis tool NUPACK to estimate important aspects of the system, such as reaction rate constants and secondary structure formation. These techniques, implemented as the Python package KinDA, will allow researchers to predict the kinetic and thermodynamic behaviour of domain-level systems after sequence assignment, as well as to detect violations of the intended behaviour.