With all the advances in both the science of aging and artificial intelligence (AI), we are in a propitious position to accurately and precisely determine who is at high risk of developing Alzheimer's disease years before signs of even mild cognitive deficit. It takes at least 20 years for aggregates of misfolded β-amyloid and tau proteins to accumulate in the brain along with neuroinflammation that they incite. This provides a long window of opportunity to get ahead of the pathobiological process, both for prediction and prevention. A family history of Alzheimer's and the presence of genetic variants in the APOE4 (apolipoprotein E4) allele indicate an increased risk, as does a polygenic risk score that is based on the combined influence of many genetic variants. However, each of these clues provides little insight about when initial symptoms would likely present.

Proponents of the use of artificial intelligence (AI) in drug discovery predict that it is likely to make drug discovery and development faster and cheaper, particularly in preclinical stages before patents are filed (1). But AI use may also create tendencies to file "compound" patents on molecules that disclose little evidence of real-world testing, exacerbating an issue already of concern in more traditional (even if also computer-aided) drug development and patenting. Our preliminary analyses of an original dataset of compound patents on small-molecule drugs suggest that, compared with more traditional drug developers, "AI-native" developers perform less in vivo, in-depth testing before patenting. In light of the potential for such early patenting to dampen overall drug research and development, it is worth considering policies that encourage disclosure of more evidence for securing a patent and provide incentives for research on disclosed but unpatented structures.

Following its successes retrieving lunar samples from both the near and far sides of the Moon, China is planning an encore, sending a probe to snatch material from a near-Earth asteroid. The target of the Tianwen-2 mission, which is expected to launch by the end of the month, is a chunk of rock named 469219 Kamo'oalewa. It is one of just seven asteroids that fall into a little-understood class known as quasi-satellites of Earth--and it could also be the first known asteroid comprised of lunar material. That hypothesis could be confirmed by laboratory studies of fragments collected by Tianwen-2, which are due to be returned to Earth about 2.5 years after launch. "This is an ambitious mission to explore a fascinating object," says astrophysicist Amy Mainzer of the University of California, Los Angeles.

The World Health Organization (WHO) has sent an internal memo about potential problems with a major company's malaria tests after scientists reported issues with test sensitivity and warned it could delay patients' access to critical treatment. Abbott's Bioline rapid diagnostic tests (RDTs) for malaria are used by health workers around the world, particularly in remote areas where lab techniques such as microscopy and DNA detection aren't available. Investigations at several institutions in Southeast Asia suggest at least some of these RDTs fail to detect infections or show faint test lines for some positive cases. Daniel Ngamije Madandi, director of WHO's Global Malaria Programme (GMP), issued the memo to WHO's six regional offices on 30 April. It lists 11 "affected" lots from two Abbott RDTs--Pf/Pv and Pf/Pan--that were associated with "faint lines and false negative results" in reports from "multiple research groups." The memo follows a public notice by WHO in March that warned of reports of faint lines in malaria RDTs without mentioning particular brands or products.

A study that used artificial intelligence–generated content to "participate" in online discussions and test whether AI was more successful at changing people's minds than human-generated content has caused an uproar because of ethical concerns about the work. This week some of the unwitting research participants publicly asked the University of Zürich (UZH), where the researchers behind the experiment hold positions, to investigate and apologize. "I think people have a reasonable expectation to not be in scientific experiments without their consent," says Casey Fiesler, an expert on internet research ethics at the University of Colorado Boulder. A university statement emailed to Science says the researchers--who remain anonymous--have decided not to publish their results. The university will investigate the incident, the statement says.

On 28 March, Briony Swire-Thompson began seeing reports online that the National Institutes of Health (NIH) might cancel grants for research on misinformation. At first, she didn't think she would be affected. Swire-Thompson, a psychologist at Northeastern University, studies misinformation--but not the political lies that get most of the attention. She's interested in false information about cancer, and why people fall for it. "There's a lot of people online trying to sell their snake oil," she says.

Gene-regulatory networks govern the development of organs. Sarropoulos et al. analyzed mouse cerebellar development in the context of gene-regulatory networks. Single nuclear profiles analyzing chromatin accessibility in about 90,000 cells revealed diversity in progenitor cells and genetic programs guiding cellular differentiation. The footsteps of evolution were apparent in varying constraints on different cell types. Science , abg4696, this issue p. [eabg4696][1] ### INTRODUCTION The cerebellum contributes to many complex brain functions, including motor control, language, and memory. During development, distinct neural cells are generated at cerebellar germinal zones in a spatiotemporally restricted manner. Cis-regulatory elements (CREs), such as enhancers and promoters, and the transcription factors that bind to them are central to cell fate specification and differentiation. Although most CREs undergo rapid turnover during evolution, a few are conserved across vertebrates. ### RATIONALE Bulk measurements of CRE activity have provided insights into gene regulation in the cerebellum, as well as into the evolutionary dynamics of CREs during organ development. However, they lack the cellular resolution required to assess cell-type differences in regulatory constraint and unravel the regulatory programs associated with the specification and differentiation of cell types. ### RESULTS Here, we generated a single-cell atlas of gene regulation in the mouse cerebellum spanning 11 developmental stages, from the beginning of neurogenesis to adulthood. By acquiring snATAC-seq (single-nucleus assay for transposase accessible chromatin using sequencing) profiles for ~90,000 cells, we mapped all major cerebellar cell types and identified candidate CREs. Characterization of CRE activity across the cerebellum development highlights the cell- and time-specificity of gene regulation. Many of the differentially accessible CREs are specific to a single cell type and state, but we also identified a fraction of CREs with pleiotropic (shared) activity. At early developmental stages, temporal changes in CRE activity are shared between progenitor cells from different germinal zones, supporting a model of cell fate induction through common temporal cues. Pleiotropic CREs in major cerebellar neuron types (granule cells, Purkinje cells, and inhibitory interneurons) are more active at early differentiation states, and the regulatory programs gradually diverge as differentiation proceeds. Based on comparisons to vertebrate genomes, we observed a decrease in CRE sequence conservation during development for all cerebellar cell types, a pattern that is largely explained by differentiation as well as by additional temporal differences between cells from matched differentiation states. Across cell types, differences in regulatory conservation are most pronounced in the adult, where microglia—the immune cells of the brain—show the fastest evolutionary turnover. By contrast, mature astrocytes harbor the most conserved intergenic CREs, not only in the cerebellum but also across a wide range of cell types in adult mouse organs. To evaluate the conservation of CRE activity, we acquired snATAC-seq profiles for ~20,000 cerebellar cells from the gray short-tailed opossum, a marsupial separated from mouse by ~160 million years of evolution. Our comparative analysis of CRE activity in the two therian species reinforced our sequence-based conclusions regarding differences in CRE constraint across cell types and developmental stages and also revealed that despite the overall high turnover of CREs, radical repurposing of spatiotemporal CRE activity is rare, at least between cell types in the same tissue. ### CONCLUSION This study reveals extensive temporal differences in CRE activity across cerebellar cell types and a shared decrease in CRE conservation during development and differentiation. Given that the cerebellum has been successfully used as a model system to study cell fate specification, neurogenesis, and other developmental processes, we expect that our observations regarding the developmental and evolutionary dynamics of regulatory elements, and their interplay, are also applicable to mammalian organs in general. ![Figure][2] Cis-regulatory elements in cerebellar cells. snATAC-seq delineates cell- and time-specific CRE activity in the developing mouse cerebellum (left). The chromatin accessibility profiles of cerebellar neuron types gradually diverge during differentiation as the activity of pleiotropic (shared) CREs decreases (top right). The evolutionary conservation of CRE sequences in vertebrates and activity in therian mammals decreases across development and differs between cell types (bottom right). mRNA, messenger RNA; PCA, principal components analysis; TF, transcription factor. Organ development is orchestrated by cell- and time-specific gene regulatory networks. In this study, we investigated the regulatory basis of mouse cerebellum development from early neurogenesis to adulthood. By acquiring snATAC-seq (single-nucleus assay for transposase accessible chromatin using sequencing) profiles for ~90,000 cells spanning 11 stages, we mapped cerebellar cell types and identified candidate cis - regulatory elements (CREs). We detected extensive spatiotemporal heterogeneity among progenitor cells and a gradual divergence in the regulatory programs of cerebellar neurons during differentiation. Comparisons to vertebrate genomes and snATAC-seq profiles for ∼20,000 cerebellar cells from the marsupial opossum revealed a shared decrease in CRE conservation during development and differentiation as well as differences in constraint between cell types. Our work delineates the developmental and evolutionary dynamics of gene regulation in cerebellar cells and provides insights into mammalian organ development. [1]: /lookup/doi/10.1126/science.abg4696 [2]: pending:yes

It has become increasing clear that mutation affects phenotypic variation and disease risk across humans. However, there are many different types of mutation. Seplyarskiy et al. applied a matrix factorization method to large human genomic datasets to identify germline mutational processes in an unsupervised manner. From this survey, nine robust mutational components were identified and specific mechanisms generating seven of these processes were proposed from correlations with genomic features. These results confirm and improve upon our understanding of mutational processes and reveal likely mechanisms of mutation in the human genome. Science , aba7408, this issue p. [1030][1] Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric. [1]: /lookup/doi/10.1126/science.aba7408

RNA is distinct among large biomolecules in that it has both informational coding ability, carried in its sequence, and the ability to form complex three-dimensional structures that can have catalytic and regulatory roles. The information-carrying component is widely appreciated. The pattern of base pairing—the first level of RNA structure—can be experimentally assessed and modeled with impressive accuracy ([ 1 ][1], [ 2 ][2]). By contrast, our understanding of the extent and roles of complex three-dimensional RNA structures remains rudimentary. RNA viral genomes are rich in motifs with complex three-dimensional structures with regulatory functions ([ 3 ][3]), and evidence increasingly supports the hypothesis that functional RNA structures are ubiquitous in organisms ranging from bacteria to humans. However, developing and testing hypotheses about the roles of RNA structure have been hindered by the inability to identify and model these structures. On page 1047 of this issue, Townshend et al. ([ 4 ][4]) report a machine-learning strategy for identifying native-like RNA folds. Nearly all RNAs that form well-understood complex structures fall into a small number of classes: the ribosomal RNAs, the large and small ribozymes that catalyze RNA cleavage, bacterial riboswitches, and regulatory elements encoded by RNA viruses. Thus, there are limited examples for guiding identification and modeling of RNAs with complex three-dimensional structures. There are only four major RNA nucleotides, and the interactions that govern base pairing and simple helix formation are well understood. Once formed, RNA helices (secondary structure) often assemble as fairly rigid elements that interact hierarchically to form more complicated structures (tertiary structure) (see the figure). Despite these simplifying features, the modeling of complex RNA structures has proven to be difficult. The RNA-Puzzles community exercise ([ 5 ][5], [ 6 ][6]) has been instrumental in illuminating the challenges involved: Groups try to predict an RNA structure from its sequence before learning the solved structure. Several rounds of RNA-Puzzles have revealed important themes. No single method consistently yields the best models, although certain approaches have better records than others, and most approaches are getting better. The best agreement tends to result when experimental or homology-based information is incorporated into the computational modeling. However, the median accuracy for small RNAs, with complex tertiary folds but without a close known homolog, has stayed stubbornly stuck in a range of ∼15- to 20-Å root mean square deviation [(RMSD) a measure of the similarity between known and modeled structures]. This agreement is much poorer than that now achieved for protein structures by machine learning ([ 7 ][7]), where native-like folds (∼2-Å RMSD or less) are achieved. Modeled RNA structures thus often recapitulate the overall fold of a target RNA but do not consistently reveal details of the tertiary structure. Current methods are not likely to be useful for applications such as understanding the biological mechanism of a structure or for designing ligands (or drugs) that modulate RNA function. ![Figure][8] RNA structure RNA molecules have multiple levels of structure and ability to encode information. The sequence of RNA is readily determined. RNA secondary structure can now be elucidated with high levels of accuracy using approaches that meld computational energy minimization with experimental per-nucleotide chemical probing information. Townshend et al. developed a deep neural network that can identify models that best represent the native tertiary state, taking a step toward modeling three-dimensional RNA structure. GRAPHIC: C. BICKEL/ SCIENCE The Atomic Rotationally Equivalent Scorer (ARES) approach of Townshend et al. is a deep neural network, a form of machine learning, and did not initially include preconceived notions of RNA structure. Indeed, the ARES framework is not specific to RNA and can be applied to other problems in molecular structure. Instead, ARES was given a small set of motifs with known RNA structure plus a large number of alternative (incorrect) variations of these same structures. ARES parameters were adjusted so that the program learned the functional and geometric arrangements of each atom and how these elements are positioned relative to each other. Layers in the neural network compute features from finer to coarser scales to recognize base pairs, helices, and more-complex structures. For example, ARES learned patterns of base pairing, the optimal geometry for RNA helices, and a subset of noncanonical tertiary motifs without being provided explicit information about these features of RNA structure. Although ARES was trained on very simple RNA systems, the resulting ARES scoring function was able to predict structures of more complex RNAs, on average, to roughly a 12-Å RMSD. This degree of accuracy represents an overall improvement of ∼4 Å over prior scoring methods. ARES is still short of the level consistent with atomic resolution or sufficient to guide identification of key functional sites or drug discovery efforts, but Townshend et al. have achieved notable progress in a field that has proven recalcitrant to transformative advances. There are three fundamental challenges for modeling complex RNA three-dimensional structures: generating reasonable structures that may represent a biological state, accurately scoring or identifying models that best represent the correct native state, and using these hopefully accurate models to discover new functional motifs and to develop hypotheses regarding the mechanisms by which RNAs with complex three-dimensional structures regulate biological processes. The ARES machine-learning approach addressed the second of these three challenges: Candidate structures still need to be generated for evaluation by ARES. With further development, deep learning strategies hold promise for creating new scoring functions that can guide structure generation in ways that might yield near-native structures. Another important goal is to use a machine-learning strategy to identify regions in large RNAs most likely to fold into three-dimensional structures. Current computational-only algorithms are not able to predict the pattern of base pairing in large RNAs accurately, even though base pairs are simpler to predict than tertiary structure. However, secondary structures for large RNAs are routinely modeled to high accuracies by incorporating experimental information. New, efficiently executed experiments are now being developed that measure features of RNA tertiary structures. Another frontier, analogous to recent advances in secondary structure modeling, would thus be to incorporate experimental information into machine-learning strategies for modeling RNA tertiary structure. Large-scale investigation of RNA structure to date, primarily focused on RNA secondary structure, has revealed several core principles. One is that the existence of regions within large RNAs with complex, higher-order structure is unremarkable. When these base pairing and tertiary structures affect biological functions, they create “an RNA structure code” with pervasive effects on gene regulatory circuits. Additionally, every RNA likely has a distinct structural personality, which implies that there are numerous ways by which RNA structure tunes the underlying function of an RNA. At the level of secondary structure, such tuning RNA structures tend to function like switches and attenuators that modulate binding by RNA and protein ligands ([ 8 ][9]–[ 11 ][10]). Finally, characterization of well-determined RNA secondary structures often leads to identification of centers of new biology. As it becomes possible to measure, (deeply) learn, and predict the details of the tertiary RNA structure-ome, diverse new discoveries in biological mechanisms await. 1. [↵][11]1. E. J. Strobel et al ., Nat. Rev. Genet. 19, 615 (2018). [OpenUrl][12][CrossRef][13][PubMed][14] 2. [↵][15]1. K. M. Weeks , Acc. Chem. Res. 54, 2502 (2021). 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[OpenUrl][46][CrossRef][47] Acknowledgments: The author’s laboratory is supported by the US National Institutes of Health and National Science Foundation. The author is an advisor to and holds equity in Ribometrix. 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In handling computer hardware, the last thing anyone would like to do is expose electronic components to electrostatic discharges. Nevertheless, this is exactly an approach that researchers are taking toward faster and more energy-efficient computing. Inspired by the functions of neurons and synapses in the brain, resistive switching devices or “memristors” are being explored as building blocks for neuromorphic circuitry. In such devices, the resistance properties are durably altered by applying voltage pulses. On page 907 of this issue, del Valle et al. ([ 1 ][1]) have imaged the early stages of electric field–induced electronic breakdown and formation of a conducting filament in vanadium oxide. By doing this in a space- and time-resolved manner, the authors provide useful insight into the characteristic length and time scales involved. ![Figure][2] Moving toward neural networksGRAPHIC: KELLIE HOLOSKI/ SCIENCE Computing systems are commonly based on the Von Neumann architecture, in which the memory is physically separated from the logic circuitry. Data are continuously shuttled between these units. This process is time consuming and presents an important cause of energy dissipation. Both aspects become very noticeable in data-intensive applications, like training deep neural networks. Neural networks are composed of layers of neuron-like devices connected through synapses. The latter comprise weight factors that are adjusted in the training process. In conventional complementary metal-oxide semiconductor (CMOS)–based technology, the weights need to be fetched, adjusted, and put back into the memory in every learning step. In an alternative and ultimately more efficient approach, the weights are embodied in the hardware itself, and training implies an alteration of the physical properties of the synapse, similar to what happens in the brain. In a fully electronic implementation, this requires the ability to controllably adjust the electrical resistance of a material. This is achieved using the electric field–driven motion of defect states, such as oxygen vacancies and impurity atoms ([ 2 ][3]), which are resistive switching concepts used also in binary resistive random access memory (ReRAM). Alternatives involve thermally induced alterations of the crystallinity of the material ([ 3 ][4]) and organic memristors ([ 4 ][5]). A complication in many techniques is that they involve atomic displacements and reconfigurations, which can lead to a spread in device properties and fatigue. This problem is circumvented by exploiting tunable electronic and/or magnetic ordering phenomena. The Mott insulator VO2 is an attractive example, exhibiting a hysteretic resistive transition just above room temperature ([ 5 ][6]). Applying electric field pulses to the material in the high-resistive state creates a metallic filament with a conductance that depends on the pulse intensity and duration. Notably, the resistance can be programmed over several orders of magnitude. By studying thin film microdevices with various vanadium oxide stoichiometries, del Valle et al. found that the transition starts with resistance fluctuations and nucleation of the conducting filament in hotspots on a hundreds-of-nanoseconds time scale (see the figure). In an avalanche-like process, the filament subsequently grows, as a result of Joule heating, over a time scale of microseconds. The authors investigated the growth dynamics and the final width of the conducting filament, which depends on both the characteristics of the voltage pulse and the resistivities of the material in the insulating and conducting states. Inhomogeneities play an important role in triggering the transition and in the filament formation by focusing the current. These findings can help to optimize the switching processes—e.g., by deliberately incorporating nanoscopic elements that act as optimized hotspots. The storing of synaptic weights in the neural network hardware is an example of the upcoming in-memory computing paradigm, which aims to circumvent the Von Neumann bottleneck. The practical implementation of this is typically in the form of cross-bar arrays ([ 6 ][7]), with the current lines acting as the pre- and postsynaptic connections to the neurons. The variable conductance properties of the barrier materials encode for the synaptic weight. Using this setup, Ohm's law and Kirchhoff's circuit law are used for matrix-vector multiplications, which are a key processing step in neural network operation. Also, other data-intensive applications can benefit from outsourcing data processing from the logic units to the memory—large-scale database queries being one example ([ 7 ][8]). In addition to storing information, the switching of VO2 when exceeding a certain threshold voltage can also be used for the realization of the artificial neurons. Using a negative differential resistance that can be invoked in the resistive transition, Yi et al. have even demonstrated 23 different neuronal functionalities with VO2-based memristors ([ 8 ][9]). Spiking modes of neural network operation are facilitated by this, with further expected enhancements in energy efficiency. The optical reflectivity modulation, as studied by del Valle et al. , presents a coupling between the electronic and photonic domains. This allows, for example, for the storing of synaptic weights in a photonic processor—a principle recently used in a photonic tensor core accelerator using phase change materials ([ 9 ][10]). Future computer systems will likely comprise a heterogeneous mix of electronic, optical, and spintronic components, and efficient coupling between these domains will then be indispensable. The next stage in vanadium oxide memristor research will be to make the step from single resistive switching devices to functional network structures, like multilayer artificial neural networks, and to explore their operation. In this endeavor, other more exotic post–Von Neumann information processing concepts are also of interest ([ 10 ][11], [ 11 ][12]). The space- and time-resolved optical reflectometry technique as demonstrated by del Valle et al. will enable current pulses and associated resistance modulations passing through such networks to be monitored without interference—tracing, so to say, the path of the variable resistance. 1. [↵][13]1. J. del Valle et al ., Science 373, 907 (2021). [OpenUrl][14][Abstract/FREE Full Text][15] 2. [↵][16]1. R. Waser, 2. R. Dittmann, 3. G. Staikov, 4. K. Szot , Adv. Mater. 21, 2632 (2009). [OpenUrl][17] 3. [↵][18]1. I. Boybat et al ., Nat. Commun. 9, 2514 (2018). [OpenUrl][19][CrossRef][20][PubMed][21] 4. [↵][22]1. S. Goswami, 2. S. Goswami, 3. T. Venkatesan , Appl. Phys. Rev. 7, 021303 (2020). [OpenUrl][23] 5. [↵][24]1. T. Driscoll, 2. H.-T. Kim, 3. B.-G. Chae, 4. M. Di Ventra, 5. D. N. Basov , Appl. Phys. Lett. 95, 043503 (2009). [OpenUrl][25][CrossRef][26] 6. [↵][27]1. Q. Xia, 2. J. J. Yang , Nat. Mater. 18, 309 (2019). [OpenUrl][28][CrossRef][29][PubMed][30] 7. [↵][31]1. I. Giannopoulos et al ., Adv. Intell. Syst. 2, 2000141 (2020). [OpenUrl][32] 8. [↵][33]1. W. Yi et al ., Nat. Commun. 9, 4661 (2018). [OpenUrl][34][CrossRef][35][PubMed][36] 9. [↵][37]1. J. Feldmann et al ., Nature 589, 52 (2021). [OpenUrl][38] 10. [↵][39]1. M. Di Ventra, 2. F. L. Traversa , J. Appl. Phys. 123, 180901 (2018). [OpenUrl][40] 11. [↵][41]1. M. A. Nugent, 2. T. W. Molter , PLOS ONE 9, e85175 (2014). 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