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When Nicholas Shakespeare stumbled across a box of documents belonging to his late aunt, Priscilla, he was completely unaware of where this discovery would take him and what he would learn about her hidden past. The glamorous, mysterious figure he remembered from his childhood was very different from the morally ambiguous young woman who emerged from the trove of love letters, photographs, and journals, surrounded by suitors and living the dangerous existence of a British woman in a country controlled by the enemy. He had heard rumors that Priscilla had fought in the Resistance, but the truth turned out to be far more complicated. As he investigated his aunt's life, dark secrets emerged, and Nicholas discovered the answers to the questions over which he'd been puzzling: What caused the breakdown of Priscilla's marriage to a French aristocrat? Why had she been interned in a prisoner-of-war camp, and how had she escaped? And who was the \"Otto\" with whom she was having a relationship as Paris was liberated? Piecing together fragments of one woman's remarkable and tragic life, Priscilla is at once a stunning story of detection, a loving portrait of a flawed woman trying to survive in terrible times, and a spellbinding slice of history.

For the research on the recognition and classification of maize leaf disease pictures, this paper proposes a method of fine-tuning model parameters based on transfer learning EfficientNet, which can improve the accuracy and speed of network recognition for a small sample of maize disease dataset. First of all, perform data cleaning and data augmentation on the dataset to obtain richer image data; then, transfer the pre-trained model obtained by EfficientNet training on ImageNet to this model method; finally, the last layer of EfficientNet classifier replace with 8 classes of softmax classifier, and train the entire network to obtain a training model for maize disease prediction. In order to verify the robustness and accuracy of the method proposed in this paper, test verification was carried out in the test dataset with VGG-16, Inception-v3 and Resnet-50, respectively. The experimental results show that the training speed of the network model proposed in this paper has been significantly improved, and its recognition accuracy is far better than other networks with a maximum of 98.52%, which can realize agricultural production applications.

Maize is a sensitive crop to drought and heat stresses, particularly at the reproductive stages of development. The present study investigated the individual and interactive effects of drought (50% field capacity) and heat (38 °C/30 °C) stresses on morpho-physiological growth, yield, nutrient uptake and oxidative metabolism in two maize hybrids i.e., ‘Xida 889’ and ‘Xida 319’. The stress treatments were applied at tasseling stage for 15 days. Drought, heat and drought + heat stress caused oxidative stress by the over-production of ROS (O 2− , H 2 O 2 , OH − ) and enhanced malondialdehyde contents, which led to reduced photosynthetic components, nutrients uptake and yield attributes. The concurrent occurrence of drought and heat was more severe for maize growth than the single stress. However, both stresses induced the metabolites accumulation and enzymatic and non-enzymatic antioxidants to prevent the oxidative damage. The performance of Xida 899 was more prominent than the Xida 319. The greater tolerance of Xida 889 to heat and drought stresses was attributed to strong antioxidant defense system, higher osmolyte accumulation, and maintenance of photosynthetic pigments and nutrient balance compared with Xida 319.

A cloudy day or an overshadowing tree causes fluctuations in light that can throw off the balance of energy flow in plant photosystems I and II (PSI and PSII). Pan et al. solved structures of PSI bound to two light-harvesting complexes (LHCs). One LHC is permanently associated with PSI. The other LHC delivers light energy to PSII under optimal conditions but can switch to a PSI-associated state after phosphorylation by a kinase that senses the redox environment of the chloroplast. The movement of LHCs between the photosystems helps maintain even energy flux. Two chlorophyll-containing subunits are visible in the structure that connect the PSI core to each LHC. Science , this issue p. 1109 Antenna proteins rearrange to balance energy flow to photosystems in fluctuating-light environments. Plants regulate photosynthetic light harvesting to maintain balanced energy flux into photosystems I and II (PSI and PSII). Under light conditions favoring PSII excitation, the PSII antenna, light-harvesting complex II (LHCII), is phosphorylated and forms a supercomplex with PSI core and the PSI antenna, light-harvesting complex I (LHCI). Both LHCI and LHCII then transfer excitation energy to the PSI core. We report the structure of maize PSI-LHCI-LHCII solved by cryo–electron microscopy, revealing the recognition site between LHCII and PSI. The PSI subunits PsaN and PsaO are observed at the PSI-LHCI interface and the PSI-LHCII interface, respectively. Each subunit relays excitation to PSI core through a pair of chlorophyll molecules, thus revealing previously unseen paths for energy transfer between the antennas and the PSI core.

Maize originated in what is now central Mexico about 9000 years ago and spread throughout the Americas before European contact. Kistler et al. applied genomic analysis to ancient and extant South American maize lineages to investigate the genetic changes that accompanied domestication (see the Perspective by Zeder). The origin of modern maize cultivars likely involved a “semidomesticated” lineage that moved out of Mexico. Later improvements then occurred among multiple South American populations, including those in southwestern Amazonia. Science , this issue p. 1309 ; see also p. 1246 A complex history of maize domestication arose from a secondary improvement center in the southwest Amazon. Domesticated maize evolved from wild teosinte under human influences in Mexico beginning around 9000 years before the present (yr B.P.), traversed Central America by ~7500 yr B.P., and spread into South America by ~6500 yr B.P. Landrace and archaeological maize genomes from South America suggest that the ancestral population to South American maize was brought out of the domestication center in Mexico and became isolated from the wild teosinte gene pool before traits of domesticated maize were fixed. Deeply structured lineages then evolved within South America out of this partially domesticated progenitor population. Genomic, linguistic, archaeological, and paleoecological data suggest that the southwestern Amazon was a secondary improvement center for partially domesticated maize. Multiple waves of human-mediated dispersal are responsible for the diversity and biogeography of modern South American maize.

Land-use changes are critical for climate policy because native vegetation and soils store abundant carbon and their losses from agricultural expansion, together with emissions from agricultural production, contribute about 20 to 25 per cent of greenhouse gas emissions 1 , 2 . Most climate strategies require maintaining or increasing land-based carbon 3 while meeting food demands, which are expected to grow by more than 50 per cent by 2050 1 , 2 , 4 . A finite global land area implies that fulfilling these strategies requires increasing global land-use efficiency of both storing carbon and producing food. Yet measuring the efficiency of land-use changes from the perspective of greenhouse gas emissions is challenging, particularly when land outputs change, for example, from one food to another or from food to carbon storage in forests. Intuitively, if a hectare of land produces maize well and forest poorly, maize should be the more efficient use of land, and vice versa. However, quantifying this difference and the yields at which the balance changes requires a common metric that factors in different outputs, emissions from different agricultural inputs (such as fertilizer) and the different productive potentials of land due to physical factors such as rainfall or soils. Here we propose a carbon benefits index that measures how changes in the output types, output quantities and production processes of a hectare of land contribute to the global capacity to store carbon and to reduce total greenhouse gas emissions. This index does not evaluate biodiversity or other ecosystem values, which must be analysed separately. We apply the index to a range of land-use and consumption choices relevant to climate policy, such as reforesting pastures, biofuel production and diet changes. We find that these choices can have much greater implications for the climate than previously understood because standard methods for evaluating the effects of land use 4 – 11 on greenhouse gas emissions systematically underestimate the opportunity of land to store carbon if it is not used for agriculture. Evaluation of the efficiency of land-use changes and their effect on global carbon storage shows that several land-use and consumption choices relevant to climate policy have greater implications than previously thought.

An improved reference genome for maize, using single-molecule sequencing and high-resolution optical mapping, enables characterization of structural variation and repetitive regions, and identifies lineage expansions of transposable elements that are unique to maize. A better map of the maize genome The maize genome was initially reported in 2009 but with some accuracy limitations. Doreen Ware and colleagues report a new reference genome for maize using single-molecule sequencing and high-resolution optical mapping. The technique shows improvements in the gene space including resolution of gaps and misassemblies and correction of order and orientation of genes. The authors characterize structural variation and repetitive regions, and identify transposable element lineage expansions unique to maize. Complete and accurate reference genomes and annotations provide fundamental tools for characterization of genetic and functional variation 1 . These resources facilitate the determination of biological processes and support translation of research findings into improved and sustainable agricultural technologies. Many reference genomes for crop plants have been generated over the past decade, but these genomes are often fragmented and missing complex repeat regions 2 . Here we report the assembly and annotation of a reference genome of maize, a genetic and agricultural model species, using single-molecule real-time sequencing and high-resolution optical mapping. Relative to the previous reference genome 3 , our assembly features a 52-fold increase in contig length and notable improvements in the assembly of intergenic spaces and centromeres. Characterization of the repetitive portion of the genome revealed more than 130,000 intact transposable elements, allowing us to identify transposable element lineage expansions that are unique to maize. Gene annotations were updated using 111,000 full-length transcripts obtained by single-molecule real-time sequencing 4 . In addition, comparative optical mapping of two other inbred maize lines revealed a prevalence of deletions in regions of low gene density and maize lineage-specific genes.

Coexpression networks and gene regulatory networks (GRNs) are emerging as important tools for predicting functional roles of individual genes at a system-wide scale. To enable network reconstructions, we built a large-scale gene expression atlas composed of 62,547 messenger RNAs (mRNAs), 17,862 nonmodified proteins, and 6227 phosphoproteins harboring 31,595 phosphorylation sites quantified across maize development. Networks in which nodes are genes connected on the basis of highly correlated expression patterns of mRNAs were very different from networks that were based on coexpression of proteins. Roughly 85% of highly interconnected hubs were not conserved in expression between RNA and protein networks. However, networks from either data type were enriched in similar ontological categories and were effective in predicting known regulatory relationships. Integration of mRNA, protein, and phosphoprotein data sets greatly improved the predictive power of GRNs.

Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors-including mean climate as well as climate extremes-explain 20%-49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%-43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.

Biochar is widely used as a soil amendment. Enzyme activity is an important factor that reflects soil metabolic activity, and is involved in biochemical processes such as organic matter decomposition and nutrient cycling in soils. However, the effects of biochar prepared for different straw materials on soil enzyme activity and soil nutrients are rarely studied. Through pot experiments, the effects of different straw (wheat, rice, maize) biochars (obtained by pyrolysis at 500 °C) on soil organic carbon, nitrogen, available phosphorus, and enzyme activity were studied in paddy soil. The results showed that the addition of biochar increased the soil organic carbon content, which gradually decreased with the extension of the rice growth period. The soil ammonium nitrogen content gradually decreased as the rice growth period continued; however, the soil nitrate nitrogen content first decreased and then increased over the rice growth period. Soil invertase, phosphatase, and urease activity first increased and then decreased, and the enzyme activity was the highest at the heading stage of rice. At this time, there were also significant correlations between enzyme activity and carbon, nitrogen, and phosphorus levels, except in the case of soil urease activity. The geometric mean of the investigated enzyme activities was the highest after amendment with rice straw biochar. These results indicate that the response of enzyme activity to biochar depends on the biochar feedstock and the rice growth stage.