A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry

Climate change impacts on crop breeding: Targeting interacting biotic and abiotic stresses for wheat improvement

Wheat (Triticum aestivum L.) as a staple crop is closely interwoven into the development of modern society. Its influence on culture and economic development is global. Recent instability in wheat markets has demonstrated its importance in guaranteeing food security across national borders. Climate change threatens food security as it interacts with a multitude of factors impacting wheat production. The challenge needs to be addressed with a multidisciplinary perspective delivered across research, private, and government sectors. Many experimental studies have identified the major biotic and abiotic stresses impacting wheat production, but fewer have addressed the combinations of stresses that occur simultaneously or sequentially during the wheat growth cycle. Here, we argue that biotic and abiotic stress interactions, and the genetics and genomics underlying them, have been insufficiently addressed by the crop science community. We propose this as a reason for the limited transfer of practical and feasible climate adaptation knowledge from research projects into routine farming practice. To address this gap, we propose that novel methodology integration can align large volumes of data available from crop breeding programs with increasingly cheaper omics tools to predict wheat performance under different climate change scenarios. Underlying this is our proposal that breeders design and deliver future wheat ideotypes based on new or enhanced understanding of the genetic and physiological processes that are triggered when wheat is subjected to combinations of stresses. By defining this to a trait and/or genetic level, new insights can be made for yield improvement under future climate conditions

Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck

To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R2 = 0.48, RMSE = 5.24 μmol m–2 s–1 and stomatal conductance: R2 = 0.36, RMSE = 0.14 mol m–2 s–1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R2 = 0.3, p < 0.05; R2 = 0.29, p < 0.05) and at 7 days after anthesis (R2 = 0.15, p < 0.05; R2 = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling

Aroma and metabolite profiling in duckweeds: exploring species and ecotypic variation to enable wider adoption as a food crop

Duckweeds (water lentils) are a nutritious human food source, with Wolffia species consumed traditionally in Eastern Asia. Duckweed contain up to 45 % protein by dry weight, high macronutrients, minerals and carotenoids. However, duckweed are not cultivated at scale and there are circa 35 other species to consider for food potential in other global regions. Here, we measured the suitability of four Lemna species and Spirodela polyrhiza for nutritional assessment, by scaling up growth of 25 ecotypes from the United Kingdom in a glasshouse. Here we showed intra- and inter-species variation of aromatic and metabolic profiles, together with biomass obtained from production. The dominant volatile organic compounds (VOCs) in duckweed are hexanal, 1-penten-3-one, 1-penten-3-ol, cis-2-pentanol and pentadecanal, with variations in amounts of 22 other compounds between species. In comparison with other leafy herbs, duckweed aroma profiles were most similar to spinach and dandelion with high ‘green’ and ‘fresh’ aroma compounds. Spirodela polyrhiza contained high flavonoids including apigenin and luteolin, offering potential benefits for health. Our results demonstrate that Lemna and Spirodela species have suitable flavonoid and amino acid profiles for nutrition. VOCs found here had positive aroma descriptors and can be used as biomarkers of freshness during storage of duckweed foodstuffs

Familial hypercholesterolaemia in children and adolescents from 48 countries: a cross-sectional study

Background: Approximately 450 000 children are born with familial hypercholesterolaemia worldwide every year, yet only 2·1% of adults with familial hypercholesterolaemia were diagnosed before age 18 years via current diagnostic approaches, which are derived from observations in adults. We aimed to characterise children and adolescents with heterozygous familial hypercholesterolaemia (HeFH) and understand current approaches to the identification and management of familial hypercholesterolaemia to inform future public health strategies. Methods: For this cross-sectional study, we assessed children and adolescents younger than 18 years with a clinical or genetic diagnosis of HeFH at the time of entry into the Familial Hypercholesterolaemia Studies Collaboration (FHSC) registry between Oct 1, 2015, and Jan 31, 2021. Data in the registry were collected from 55 regional or national registries in 48 countries. Diagnoses relying on self-reported history of familial hypercholesterolaemia and suspected secondary hypercholesterolaemia were excluded from the registry; people with untreated LDL cholesterol (LDL-C) of at least 13·0 mmol/L were excluded from this study. Data were assessed overall and by WHO region, World Bank country income status, age, diagnostic criteria, and index-case status. The main outcome of this study was to assess current identification and management of children and adolescents with familial hypercholesterolaemia. Findings: Of 63 093 individuals in the FHSC registry, 11 848 (18·8%) were children or adolescents younger than 18 years with HeFH and were included in this study; 5756 (50·2%) of 11 476 included individuals were female and 5720 (49·8%) were male. Sex data were missing for 372 (3·1%) of 11 848 individuals. Median age at registry entry was 9·6 years (IQR 5·8-13·2). 10 099 (89·9%) of 11 235 included individuals had a final genetically confirmed diagnosis of familial hypercholesterolaemia and 1136 (10·1%) had a clinical diagnosis. Genetically confirmed diagnosis data or clinical diagnosis data were missing for 613 (5·2%) of 11 848 individuals. Genetic diagnosis was more common in children and adolescents from high-income countries (9427 [92·4%] of 10 202) than in children and adolescents from non-high-income countries (199 [48·0%] of 415). 3414 (31·6%) of 10 804 children or adolescents were index cases. Familial-hypercholesterolaemia-related physical signs, cardiovascular risk factors, and cardiovascular disease were uncommon, but were more common in non-high-income countries. 7557 (72·4%) of 10 428 included children or adolescents were not taking lipid-lowering medication (LLM) and had a median LDL-C of 5·00 mmol/L (IQR 4·05-6·08). Compared with genetic diagnosis, the use of unadapted clinical criteria intended for use in adults and reliant on more extreme phenotypes could result in 50-75% of children and adolescents with familial hypercholesterolaemia not being identified. Interpretation: Clinical characteristics observed in adults with familial hypercholesterolaemia are uncommon in children and adolescents with familial hypercholesterolaemia, hence detection in this age group relies on measurement of LDL-C and genetic confirmation. Where genetic testing is unavailable, increased availability and use of LDL-C measurements in the first few years of life could help reduce the current gap between prevalence and detection, enabling increased use of combination LLM to reach recommended LDL-C targets early in life

Soil respiration in Mexico: Advances and future directions

Soil respiration (RS) is a CO2 efflux from the soil to the atmosphere defined as the sum of autotrophic (respiration by roots and mycorrhizae), and heterotrophic (respiration of microorganisms that decompose fractions of organic matter and of soil fauna) respiration. Globally, RS is considered to be the second largest flux of C to the atmosphere. From published literature it is clear that its main controls are soil temperature, soil moisture, photosynthesis, organic matter inputs and soil biota composition. Despite its relevance in C cycle science, there have been only twenty eight studies in Mexico in the last decade where direct measurement of gas exchange was conducted in the field. These studies were held mostly in agricultural and forest ecosystems, in Central and Southern Mexico where mild subtropical conditions prevail. However, arid, semi-arid, tropical and wetland ecosystems may have an important role in Mexico’s CO2 emissions because of their extent and extensive land use changes. From the twenty eight studies, only two provided continuous measurements of RS with high temporal resolution, highlighting the need for long-term studies to evaluate the complex biophysical controls of this flux and associated processes over different ecological succession stages. We conclude that Mexico represents an important opportunity to understand its complex dynamics, in national and global context, as ecosystems in the country cover a wide range of climatic conditions. This is particularly important because deforestation and degradation of Mexican ecosystems is rapidly increasing along with expected changes in climate

Base de datos de flujos verticales de dioxido de carbono en ecosistemas terrestres y costeros en México

El dióxido de carbono (CO2) es uno de los principales gases de efecto invernadero (GEI) cuyo incremento en la atmósfera está asociado con el calentamiento global. Con el objetivo de promover estudios de síntesis que lleven a un mejor entendimiento de los procesos relacionados con el ciclo del carbono en ecosistemas terrestres y costeros de México, se construyeron bases de datos de flujos verticales de carbono. Se construyó una base de datos con flujos de CO2 a escala anual, para ocho sitios y 30 años por sitio, de la red MexFlux, cuya información se obtuvo de publicaciones en revistas científicas, memorias de resúmenes en extenso y documentos de tesis. Una segunda base se construyó a partir de datos a escala diaria, de los flujos de CO2 de 14 sitios de monitoreo y 53 años/ sitio, que fueron proporcionados directamente por los investigadores principales (PI) de cada sitio y denominada MexFlux_2019 V1. Esta última base de datos, a diferencia de la primera que es de libre acceso, está restringida. Las bases de datos incluyen información del intercambio neto de carbono a nivel ecosistema, la productividad primaria bruta, respiración del ecosistema y de variables meteorológicas y ambientales complementarias