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Machines that simultaneously process and store multistate data at one and the same location can provide a new class of fast, powerful and efficient general-purpose computers. We demonstrate the central element of an all-optical calculator, a photonic abacus, which provides multistate compute-and-store operation by integrating functional phase-change materials with nanophotonic chips. With picosecond optical pulses we perform the fundamental arithmetic operations of addition, subtraction, multiplication, and division, including a carryover into multiple cells. This basic processing unit is embedded into a scalable phase-change photonic network and addressed optically through a two-pulse random access scheme. Our framework provides first steps towards light-based non-von Neumann arithmetic. Computing approaches in the optical domain would allow for ultra-fast signaling and ultra-high bandwidth capabilities. Here, Feldmann et al. demonstrate a photonic abacus, which provides multistate compute-and store operation by integrating phase-change materials with nanophotonic chips.

Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data. An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.

With the proliferation of ultrahigh-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence (AI) 1 , the world is generating exponentially increasing amounts of data that need to be processed in a fast and efficient way. Highly parallelized, fast and scalable hardware is therefore becoming progressively more important 2 . Here we demonstrate a computationally specific integrated photonic hardware accelerator (tensor core) that is capable of operating at speeds of trillions of multiply-accumulate operations per second (10 12 MAC operations per second or tera-MACs per second). The tensor core can be considered as the optical analogue of an application-specific integrated circuit (ASIC). It achieves parallelized photonic in-memory computing using phase-change-material memory arrays and photonic chip-based optical frequency combs (soliton microcombs 3 ). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 gigahertz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates 3 – 5 , ultralow-loss silicon nitride waveguides 6 , 7 , and high-speed on-chip detectors and modulators, our approach provides a path towards full complementary metal–oxide–semiconductor (CMOS) wafer-scale integration of the photonic tensor core. Although we focus on convolutional processing, more generally our results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services. An integrated photonic processor, based on phase-change-material memory arrays and chip-based optical frequency combs, which can operate at speeds of trillions of multiply-accumulate (MAC) operations per second, is demonstrated.

Cultivated strawberry (Fragaria × ananassa) is one of our youngest domesticates, originating in early eighteenth-century Europe from spontaneous hybrids between wild allo-octoploid species (Fragaria chiloensis and Fragaria virginiana). The improvement of horticultural traits by 300 years of breeding has enabled the global expansion of strawberry production. Here, we describe the genomic history of strawberry domestication from the earliest hybrids to modern cultivars. We observed a significant increase in heterozygosity among interspecific hybrids and a decrease in heterozygosity among domesticated descendants of those hybrids. Selective sweeps were found across the genome in early and modern phases of domestication—59–76% of the selectively swept genes originated in the three less dominant ancestral subgenomes. Contrary to the tenet that genetic diversity is limited in cultivated strawberry, we found that the octoploid species harbor massive allelic diversity and that F. × ananassa harbors as much allelic diversity as either wild founder. We identified 41.8 M subgenome-specific DNA variants among resequenced wild and domesticated individuals. Strikingly, 98% of common alleles and 73% of total alleles were shared between wild and domesticated populations. Moreover, genome-wide estimates of nucleotide diversity were virtually identical in F. chiloensis,F. virginiana, and F. × ananassa (π = 0.0059–0.0060). We found, however, that nucleotide diversity and heterozygosity were significantly lower in modern F. × ananassa populations that have experienced significant genetic gains and have produced numerous agriculturally important cultivars.

The annual production of strawberry has increased by one million tonnes in the US and 8.4 million tonnes worldwide since 1960. Here we show that the US expansion was driven by genetic gains from Green Revolution breeding and production advances that increased yields by 2,755%. Using a California population with a century-long breeding history and phenotypes of hybrids observed in coastal California environments, we estimate that breeding has increased fruit yields by 2,974-6,636%, counts by 1,454-3,940%, weights by 228-504%, and firmness by 239-769%. Using genomic prediction approaches, we pinpoint the origin of the Green Revolution to the early 1950s and uncover significant increases in additive genetic variation caused by transgressive segregation and phenotypic diversification. Lastly, we show that the most consequential Green Revolution breeding breakthrough was the introduction of photoperiod-insensitive, PERPETUAL FLOWERING hybrids in the 1970s that doubled yields and drove the dramatic expansion of strawberry production in California. Cultivated strawberry is a hybrid species with a 250-year domestication history. Here, the authors use genomic prediction and a historically important breeding population to show that the introduction of photoperiod-insensitive hybrids and genetic gains from breeding have been catalysts for a strawberry Green Revolution.

Allo-octoploid cultivated strawberry ( × ) originated through a combination of polyploid and homoploid hybridization, domestication of an interspecific hybrid lineage, and continued admixture of wild species over the last 300 years. While genes appear to flow freely between the octoploid progenitors, the genome structures and diversity of the octoploid species remain poorly understood. The complexity and absence of an octoploid genome frustrated early efforts to study chromosome evolution, resolve subgenomic structure, and develop a single coherent linkage group nomenclature. Here, we show that octoploid species harbor millions of subgenome-specific DNA variants. Their diversity was sufficient to distinguish duplicated (homoeologous and paralogous) DNA sequences and develop 50K and 850K SNP genotyping arrays populated with co-dominant, disomic SNP markers distributed throughout the octoploid genome. Whole-genome shotgun genotyping of an interspecific segregating population yielded 1.9M genetically mapped subgenome variants in 5,521 haploblocks spanning 3,394 cM in . subsp. , and 1.6M genetically mapped subgenome variants in 3,179 haploblocks spanning 2,017 cM in . × . These studies provide a dense genomic framework of subgenome-specific DNA markers for seamlessly cross-referencing genetic and physical mapping information and unifying existing chromosome nomenclatures. Using comparative genomics, we show that geographically diverse wild octoploids are effectively diploidized, nearly completely collinear, and retain strong macro-synteny with diploid progenitor species. The preservation of genome structure among allo-octoploid taxa is a critical factor in the unique history of garden strawberry, where unimpeded gene flow supported its origin and domestication through repeated cycles of interspecific hybridization.

Crop production is becoming an increasing challenge as the global population grows and the climate changes. Modern cultivated crop species are selected for productivity under optimal growth environments and have often lost genetic variants that could allow them to adapt to diverse, and now rapidly changing, environments. These genetic variants are often present in their closest wild relatives, but so are less desirable traits. How to preserve and effectively utilize the rich genetic resources that crop wild relatives offer while avoiding detrimental variants and maladaptive genetic contributions is a central challenge for ongoing crop improvement. This Essay explores this challenge and potential paths that could lead to a solution.

The development of genome-informed methods for identifying quantitative trait loci (QTL) and studying the genetic basis of quantitative variation in natural and experimental populations has been driven by advances in high-throughput genotyping. For many complex traits, the underlying genetic variation is caused by the segregation of one or more ‘large-effect’ loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance ( p ) and heritability ( H M 2 ) for large-effect loci underlying complex phenotypes. We found that commonly used statistical methods overestimate p and H M 2 . The source of the upward bias was traced to inequalities between the expected values of variance components in the numerators and denominators of these parameters. Algebraic solutions for bias-correcting estimates of p and H M 2 were found that only depend on the degrees of freedom and are constant for a given study design. We discovered that average semivariance methods, which have heretofore not been used in complex trait analyses, yielded unbiased estimates of p and H M 2 , in addition to best linear unbiased predictors of the additive and dominance effects of the underlying loci. The cryptic bias problem described here is unrelated to selection bias, although both cause the overestimation of p and H M 2 . The solutions we described are predicted to more accurately describe the contributions of large-effect loci to the genetic variation underlying complex traits of medical, biological, and agricultural importance.

The development of strawberry (Fragaria × ananassa Duchesne ex Rozier) cultivars resistant to Phytophthora crown rot (PhCR), a devastating disease caused by the soil‐borne pathogen Phytophthora cactorum (Lebert & Cohn) J. Schröt., has been challenging partly because the resistance phenotypes are quantitative and only moderately heritable. To develop deeper insights into the genetics of resistance and build the foundation for applying genomic selection, a genetically diverse training population was screened for resistance to California isolates of the pathogen. Here we show that genetic gains in breeding for resistance to PhCR have been negligible (3% of the cultivars tested were highly resistant and none surpassed early 20th century cultivars). Narrow‐sense genomic heritability for PhCR resistance ranged from 0.41 to 0.75 among training population individuals. Using multivariate genome‐wide association studies (GWAS), we identified a large‐effect locus (predicted to be RPc2) that explained 43.6–51.6% of the genetic variance, was necessary but not sufficient for resistance, and was associated with calcium channel and other candidate genes with known plant defense functions. The addition of underutilized gene bank resources to our training population doubled additive genetic variance, increased the accuracy of genomic selection, and enabled the discovery of individuals carrying favorable alleles that are either rare or not present in modern cultivars. The incorporation of an RPc2‐associated single‐nucleotide polymorphism (SNP) as a fixed effect increased genomic prediction accuracy from 0.40 to 0.55. Finally, we show that parent selection using genomic‐estimated breeding values, genetic variances, and cross usefulness holds promise for enhancing resistance to PhCR in strawberry. Core Ideas The strongest sources of resistance were heirloom cultivars developed before the advent of soil fumigation. The addition of exotic genetic resources to an elite training population doubled genetic variation. Although resistance is genetically complex, a single locus accounted for 44–52% of the genetic variance. Genetic gains can be accelerated by genomic prediction of breeding values and cross usefulness. A global effort is needed to improve resistance to PhCR, which appears to be inadequate among modern cultivars.

Reliable phenotyping methods that are simple to operate and inexpensive to deploy are critical for studying quantitative traits in plants. Traditional fruit shape phenotyping relies on human raters or 2D analyses to assess form, e.g., size and shape. Systems for 3D imaging using multi‐view stereo have been implemented, but frequently rely on commercial software and/or specialized hardware, which can lead to limitations in accessibility and scalability. We present a complete system constructed of consumer‐grade components for capturing, calibrating, and reconstructing the 3D form of small‐to‐moderate sized fruits and tubers. Data acquisition and image capture sessions are 9 seconds to capture 60 images. The initial prototype cost was $1600 USD. We measured accuracy by comparing reconstructed models of 3D printed ground truth objects to the original digital files of those same ground truth objects. The R2 between length of the primary, secondary, and tertiary axes, volume, and surface area of the ground‐truth object and the reconstructed models was >0.97 and root‐mean square error (RMSE) was < 3 mm for objects without locally concave regions. Measurements from 1 mm and 2 mm resolution reconstructions were consistent (R2 > 0.99). Qualitative assessments were performed on 48 fruit and tubers, including 18 strawberries, 12 potatoes, five grapes, seven peppers, and four Bosc and two red Anjou pears. Our proposed phenotyping system is fast, relatively low cost, and has demonstrated accuracy for certain shape classes, and could be used for the 3D analysis of fruit form. Core Ideas A low‐cost 3D fruit phenotyping system is presented. Image capture using the proposed approach lasts for only 9 s. Accuracy is measured against 3D printed ground‐truth objects. Camera calibration, background segmentation, and reconstruction does not rely on commercial software. An RMSE less than 3 mm was obtained for ground truth objects without locally concave regions.