Quantum computers have been proposed to solve a number of important problems such as discovering new drugs, new catalysts for fertilizer production, breaking encryption protocols, optimizing financial portfolios, or implementing new artificial intelligence applications. Yet, to date, a simple task such as multiplying 3 by 5 is beyond existing quantum hardware. This article examines the difficulties that would need to be solved for quantum computers to live up to their promises. I discuss the whole stack of technologies that has been envisioned to build a quantum computer from the top layers (the actual algorithms and associated applications) down to the very bottom ones (the quantum hardware, its control electronics, cryogeny, etc.) while not forgetting the crucial intermediate layer of quantum error correction. <<<

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features. <<<

Recent events have pushed RNA research into the spotlight. Continued discoveries of RNA with unexpected diverse functions in healthy and diseased cells, such as the role of RNA as both the source and countermeasure to a severe acute respiratory syndrome coronavirus 2 infection, are igniting a new passion for understanding this functionally and structurally versatile molecule. Although RNA structure is key to function, many foundational characteristics of RNA structure are misunderstood, and the default state of RNA is often thought of and depicted as a single floppy strand. The purpose of this perspective is to help adjust mental models, equipping the community to better use the fundamental aspects of RNA structural information in new mechanistic models, enhance experimental design to test these models, and refine data interpretation. We discuss six core observations focused on the inherent nature of RNA structure and how to incorporate these characteristics to better understand RNA structure. We also offer some ideas for future efforts to make validated RNA structural information available and readily used by all researchers. <<<

DNA methylation is a major epigenetic modification found across species and has a profound impact on many biological processes. However, its influence on chromatin accessibility and higher-order genome organization remains unclear, particularly in plants. Here, we present genome-wide chromatin accessibility profiles of 18 mutants that are deficient in CG, CHG, or CHH DNA methylation. We find that DNA methylation in all three sequence contexts impacts chromatin accessibility in heterochromatin. Many chromatin regions maintain inaccessibility when DNA methylation is lost in only one or two sequence contexts, and signatures of accessibility are particularly affected when DNA methylation is reduced in all contexts, suggesting an interplay between different types of DNA methylation. In addition, we found that increased chromatin accessibility was not always accompanied by increased transcription, suggesting that DNA methylation can directly impact chromatin structure by other mechanisms. We also observed that an increase in chromatin accessibility was accompanied by enhanced long-range chromatin interactions. Together, these results provide a valuable resource for chromatin architecture and DNA methylation analyses and uncover a pivotal role for methylation in the maintenance of heterochromatin inaccessibility. <<<

Prolonged detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA and recurrence of PCR-positive tests have been widely reported in patients after recovery from COVID-19, but some of these patients do not appear to shed infectious virus. We investigated the possibility that SARS-CoV-2 RNAs can be reverse-transcribed and integrated into the DNA of human cells in culture and that transcription of the integrated sequences might account for some of the positive PCR tests seen in patients. In support of this hypothesis, we found that DNA copies of SARS-CoV-2 sequences can be integrated into the genome of infected human cells. We found target site duplications flanking the viral sequences and consensus LINE1 endonuclease recognition sequences at the integration sites, consistent with a LINE1 retrotransposon-mediated, target-primed reverse transcription and retroposition mechanism. We also found, in some patient-derived tissues, evidence suggesting that a large fraction of the viral sequences is transcribed from integrated DNA copies of viral sequences, generating viral-host chimeric transcripts. The integration and transcription of viral sequences may thus contribute to the detection of viral RNA by PCR in patients after infection and clinical recovery. Because we have detected only subgenomic sequences derived mainly from the 3' end of the viral genome integrated into the DNA of the host cell, infectious virus cannot be produced from the integrated subgenomic SARS-CoV-2 sequences. <<<

Katharine C Simon, Gregory D Clemenson, Jing Zhang, Negin Sattari, Alessandra E Shuster, Brandon Clayton, Elisabet Alzueta, Teji Dulai, Massimiliano de Zambotti, Craig Stark, Fiona C Baker, Sara C Mednick <<<

Sleep facilitates hippocampal-dependent memories, supporting the acquisition and maintenance of internal representation of spatial relations within an environment. In humans, however, findings have been mixed regarding sleep's contribution to spatial memory and navigation, which may be due to task designs or outcome measurements. We developed the Minecraft Memory and Navigation (MMN) task for the purpose of disentangling how spatial memory accuracy and navigation change over time, and to study sleep's independent contributions to each. In the MMN task, participants learned the locations of objects through free exploration of an open field computerized environment. At test, they were teleported to random positions around the environment and required to navigate to the remembered location of each object. In study 1, we developed and validated four unique MMN environments with the goal of equating baseline learning and immediate test performance. A total of 86 participants were administered the training phases and immediate test. Participants' baseline performance was equivalent across all four environments, supporting the use of the MMN task. In study 2, 29 participants were trained, tested immediately, and again 12 h later after a period of sleep or wake. We found that the metric accuracy of object locations, i.e., spatial memory, was maintained over a night of sleep, while after wake, metric accuracy declined. In contrast, spatial navigation improved over both sleep and wake delays. Our findings support the role of sleep in retaining the precise spatial relationships within a cognitive map; however, they do not support a specific role of sleep in navigation. <<<

Sheila Shanmugan, Jakob Seidlitz, Zaixu Cui, Azeez Adebimpe, Danielle S Bassett, Maxwell A Bertolero, Christos Davatzikos, Damien A Fair, Raquel E Gur, Ruben C Gur, Bart Larsen, Hongming Li, Adam Pines, Armin Raznahan, David R Roalf, Russell T Shinohara, Jacob Vogel, Daniel H Wolf, Yong Fan, Aaron Alexander-Bloch, Theodore D Satterthwaite <<<

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy ( < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography. <<<

Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing. <<<

Maternal brain adaptations have been found across pregnancy and postpartum, but little is known about the long-term effects of parity on the maternal brain. Using neuroimaging and machine learning, we investigated structural brain characteristics in 12,021 middle-aged women from the UK Biobank, demonstrating that parous women showed less evidence of brain aging compared to their nulliparous peers. The relationship between childbirths and a "younger-looking" brain could not be explained by common genetic variation or relevant confounders. Although prospective longitudinal studies are needed, the results suggest that parity may involve neural changes that could influence women's brain aging later in life. <<<

Human object-selective cortex shows a large-scale organization characterized by the high-level properties of both animacy and object size. To what extent are these neural responses explained by primitive perceptual features that distinguish animals from objects and big objects from small objects? To address this question, we used a texture synthesis algorithm to create a class of stimuli-texforms-which preserve some mid-level texture and form information from objects while rendering them unrecognizable. We found that unrecognizable texforms were sufficient to elicit the large-scale organizations of object-selective cortex along the entire ventral pathway. Further, the structure in the neural patterns elicited by texforms was well predicted by curvature features and by intermediate layers of a deep convolutional neural network, supporting the mid-level nature of the representations. These results provide clear evidence that a substantial portion of ventral stream organization can be accounted for by coarse texture and form information without requiring explicit recognition of intact objects. <<<

Antonella Amore, Brandon C Knott, Nitin T Supekar, Asif Shajahan, Parastoo Azadi, Peng Zhao, Lance Wells, Jeffrey G Linger, Sarah E Hobdey, Todd A Vander Wall, Todd Shollenberger, John M Yarbrough, Zhongping Tan, Michael F Crowley, Michael E Himmel, Stephen R Decker, Gregg T Beckham, Larry E Taylor <<<

In nature, many microbes secrete mixtures of glycoside hydrolases, oxidoreductases, and accessory enzymes to deconstruct polysaccharides and lignin in plants. These enzymes are often decorated with N- and O-glycosylation, the roles of which have been broadly attributed to protection from proteolysis, as the extracellular milieu is an aggressive environment. Glycosylation has been shown to sometimes affect activity, but these effects are not fully understood. Here, we examine N- and O-glycosylation on a model, multimodular glycoside hydrolase family 7 cellobiohydrolase (Cel7A), which exhibits an O-glycosylated carbohydrate-binding module (CBM) and an O-glycosylated linker connected to an N- and O-glycosylated catalytic domain (CD)-a domain architecture common to many biomass-degrading enzymes. We report consensus maps for Cel7A glycosylation that include glycan sites and motifs. Additionally, we examine the roles of glycans on activity, substrate binding, and thermal and proteolytic stability. N-glycan knockouts on the CD demonstrate that N-glycosylation has little impact on cellulose conversion or binding, but does have major stability impacts. O-glycans on the CBM have little impact on binding, proteolysis, or activity in the whole-enzyme context. However, linker O-glycans greatly impact cellulose conversion via their contribution to proteolysis resistance. Molecular simulations predict an additional role for linker O-glycans, namely that they are responsible for maintaining separation between ordered domains when Cel7A is engaged on cellulose, as models predict α-helix formation and decreased cellulose interaction for the nonglycosylated linker. Overall, this study reveals key roles for N- and O-glycosylation that are likely broadly applicable to other plant cell-wall-degrading enzymes. <<<

Eicosapentaenoic acid (EPA), an omega-3 (ω-3) polyunsaturated fatty acid, is an essential nutrient that exhibits antiinflammatory, neuroprotective, and cardiovascular-protective activities. Although EPA is used as a nutrient-based pharmaceutical agent or dietary supplement, its molecular target(s) is debatable. Here, we showed that EPA and its metabolites strongly and reversibly inhibit vesicular nucleotide transporter (VNUT), a key molecule for vesicular storage and release of adenosine triphosphate (ATP) in purinergic chemical transmission. In vitro analysis showed that EPA inhibits human VNUT-mediated ATP uptake at a half-maximal inhibitory concentration (IC) of 67 nM, acting as an allosteric modulator through competition with Cl. EPA impaired vesicular ATP release from neurons without affecting the vesicular release of other neurotransmitters. In vivo, mice showed a delay in the onset of neuropathic pain and resistance to both neuropathic and inflammatory pain. EPA potently attenuated neuropathic and inflammatory pain in wild-type mice but not in mice without affecting the basal nociception. The analgesic effect of EPA was canceled by the intrathecal injection of purinoceptor agonists and was stronger than that of existing drugs used for neuropathic pain treatment, with few side effects. Neuropathic pain impaired insulin sensitivity in previous studies, which was improved by EPA in the wild-type mice but not in the mice. Our results showed that VNUT is a molecular target of EPA that attenuates neuropathic and inflammatory pain and insulin resistance. EPA may represent a unique nutrient-based treatment and prevention strategy for neurological, immunological, and metabolic diseases by targeting purinergic chemical transmission. <<<

O Y Olivia Tse, Peiyong Jiang, Suk Hang Cheng, Wenlei Peng, Huimin Shang, John Wong, Stephen L Chan, Liona C Y Poon, Tak Y Leung, K C Allen Chan, Rossa W K Chiu, Y M Dennis Lo <<<

5-Methylcytosine (5mC) is an important type of epigenetic modification. Bisulfite sequencing (BS-seq) has limitations, such as severe DNA degradation. Using single molecule real-time sequencing, we developed a methodology to directly examine 5mC. This approach holistically examined kinetic signals of a DNA polymerase (including interpulse duration and pulse width) and sequence context for every nucleotide within a measurement window, termed the holistic kinetic (HK) model. The measurement window of each analyzed double-stranded DNA molecule comprised 21 nucleotides with a cytosine in a CpG site in the center. We used amplified DNA (unmethylated) and M.SssI-treated DNA (methylated) (M.SssI being a CpG methyltransferase) to train a convolutional neural network. The area under the curve for differentiating methylation states using such samples was up to 0.97. The sensitivity and specificity for genome-wide 5mC detection at single-base resolution reached 90% and 94%, respectively. The HK model was then tested on human-mouse hybrid fragments in which each member of the hybrid had a different methylation status. The model was also tested on human genomic DNA molecules extracted from various biological samples, such as buffy coat, placental, and tumoral tissues. The overall methylation levels deduced by the HK model were well correlated with those by BS-seq ( = 0.99; < 0.0001) and allowed the measurement of allele-specific methylation patterns in imprinted genes. Taken together, this methodology has provided a system for simultaneous genome-wide genetic and epigenetic analyses. <<<

During the first 2 postnatal years, cortical thickness of the human brain develops dynamically and spatially heterogeneously and likely peaks between 1 and 2 y of age. The striking development renders this period critical for later cognitive outcomes and vulnerable to early neurodevelopmental disorders. However, due to the difficulties in longitudinal infant brain MRI acquisition and processing, our knowledge still remains limited on the dynamic changes, peak age, and spatial heterogeneities of cortical thickness during infancy. To fill this knowledge gap, in this study, we discover the developmental regionalization of cortical thickness, i.e., developmentally distinct regions, each of which is composed of a set of codeveloping cortical vertices, for better understanding of the spatiotemporal heterogeneities of cortical thickness development. We leverage an infant-dedicated computational pipeline, an advanced multivariate analysis method (i.e., nonnegative matrix factorization), and a densely sampled longitudinal dataset with 210 serial MRI scans from 43 healthy infants, with each infant being scheduled to have 7 longitudinal scans at around 1, 3, 6, 9, 12, 18, and 24 mo of age. Our results suggest that, during the first 2 y, the whole-brain average cortical thickness increases rapidly and reaches a plateau at about 14 mo of age and then decreases at a slow pace thereafter. More importantly, each discovered region is structurally and functionally meaningful and exhibits a distinctive developmental pattern, with several regions peaking at varied ages while others keep increasing in the first 2 postnatal years. Our findings provide valuable references and insights for early brain development. <<<