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Background: Our understanding of the physiological responses of rice inflorescence (panicle) to environmental stresses is limited by the challenge of accurately determining panicle photosynthetic parameters and their impact on grain yield. This is primarily due to the lack of a suitable gas exchange methodology for panicles and non-destructive methods to accurately determine panicle surface area. Results: To address these challenges, we have developed a custom panicle gas exchange cylinder compatible with the LiCor 6800 Infra-red Gas Analyzer. Accurate surface area measurements were determined using 3D panicle imaging to normalize the panicle-level photosynthetic measurements. We observed differential responses in both panicle and flag leaf for two temperate Japonica rice genotypes (accessions TEJ-1 and TEJ-2) exposed to heat stress during early grain filling. There was a notable divergence in the relative photosynthetic contribution of flag leaf and panicles for the heat-tolerant genotype (TEJ-2) compared to the sensitive genotype (TEJ-1). Conclusion: The novelty of this method is the non-destructive and accurate determination of panicle area and photosynthetic parameters, enabling researchers to monitor temporal changes in panicle physiology during the reproductive development. The method is useful for panicle-level measurements under diverse environmental stresses and is sensitive enough to evaluate genotypic variation for panicle physiology and architecture in cereals with compact inflorescences. [ABSTRACT FROM AUTHOR]

Background: Wheat is an important food crop globally, and timely prediction of wheat yield in breeding efforts can improve selection efficiency. Traditional yield prediction method based on secondary traits is time-consuming, costly, and destructive. It is urgent to develop innovative methods to improve selection efficiency and accelerate genetic gains in the breeding cycle. Results: Crop yield prediction using remote sensing has gained popularity in recent years. This paper proposed a novel ensemble feature selection (EFS) method to improve yield prediction from hyperspectral data. For this, 207 wheat cultivars and breeding lines were grown under full and limited irrigation treatments respectively, and their canopy hyperspectral reflectance was measured at the flowering, early grain filling (EGF), mid grain filling (MGF), and late grain filling (LGF) stages. Then, 115 vegetation indices were extracted from the hyperspectral reflectance and combined with four feature selection methods, i.e., mean decrease impurity (MDI), Boruta, FeaLect, and RReliefF to train deep neural network (DNN) models for yield prediction. Next, a learning framework was developed by combining the predicted values of the selected and the full features using multiple linear regression (MLR). The results show that the selected features contributed to higher yield prediction accuracy than the full features, and the MDI method performed well across growth stages, with a mean R2 ranging from 0.634 to 0.666 (mean RMSE = 0.926–0.967 t ha−1). Also, the proposed EFS method outperformed all the individual feature selection methods across growth stages, with a mean R2 ranging from 0.648 to 0.679 (mean RMSE = 0.911–0.950 t ha−1). Conclusions: The proposed EFS method can improve grain yield prediction from hyperspectral data and can be used to assist wheat breeders in earlier decision-making. [ABSTRACT FROM AUTHOR]

The rhizosphere is a nexus for plant–microbe interactions and, as a host-structured environment, a location of high activity for distinct microbes and plant species. Although our insights into this habitat have exploded in recent years, we are still limited in our ability to answer key questions about the specificity of these root-microbial relationships. In particular, it can be difficult to confirm or reject microbiome heritability in many plant systems and to pinpoint which microbial taxa are key to plant functioning. Like other host-structured environments, the rhizosphere is structurally, chemically, and biologically complex, driven largely by differences in root anatomy, location, and function. In this Correspondence, we describe a review of 377 "rhizosphere microbiome" research papers and demonstrate how matching a sampling method to the biological question can advance our understanding of host-microbe interactions in a functionally heterogeneous environment. We found that the vast majority of studies (92%) pool all roots from a root system during sampling, ignoring variation in microbial composition between roots of different function and limiting insight into key root-microbial relationships. Furthermore, approaches for removing root-associated microbes are highly variable and non-standard, complicating multi-study analyses. Our understanding of the strength and nature of host-microbe relationships in heterogenous host-microbiome environments can be clarified by targeting sampling to locations of high interaction. While the high complexity of the rhizosphere creates logistical challenges, we suggest that unambiguous language and refined approaches will improve our ability to match methods to research questions and advance our understanding of the specificity of plant-microbial interactions. [ABSTRACT FROM AUTHOR]

Background: It is critical to understand the sensitivity, response direction and magnitude of carbohydrates and secondary compounds to warming for predicting the structure and function of the tundra ecosystem towards future climate change. Results: Open-top chambers (OTCs) were used to passively increase air and soil temperatures on Changbai Mountain alpine tundra. After seven years' continuous warming (+ 1.5 °C), the vegetation coverage, nonstructural carbohydrates (soluble sugars and starch) and secondary compounds (total phenols, flavonoids and triterpenes) of leaves and roots in three dominant dwarf shrubs, Dryas octopetala var. asiatica, Rhododendron confertissimum and Vaccinium uliginosum, were investigated during the growing season. Warming did not significantly affect the concentrations of carbohydrates but decreased total phenols for the three species. Carbohydrates and secondary compounds showed significantly seasonal pattern and species-specific variation. No significant trade-off or negative relationship between carbohydrates and secondary compounds was observed. Compared to Dr. octopetala var. asiatica, V. uliginosum allocated more carbon on secondary compounds. Warming significantly increased the coverage of Dr. octopetala var. asiatica, did not change it for V. uliginosum and decreased it for Rh. confertissimum. Rh. confertissimum had significantly lower carbohydrates and invested more carbon on secondary compounds than the other two species. Conclusions: Enhanced dominance and competitiveness of Dr. octopetala var. asiatica was companied by increased trend in carbohydrate concentrations and decreased ratio of secondary compounds to total carbon in the warming OTCs. We, therefore, predict that Dr. octopetala var. asiatica will continue to maintain dominant status, but the competition ability of V. uliginosum could gradually decrease with warming, leading to changes in species composition and community structure of the Changbai tundra ecosystem under future climate warming. [ABSTRACT FROM AUTHOR]

Background: The chance to compare patterns of differential gene expression in related ecologically distinct species can be particularly fruitful to investigate the genetics of adaptation and phenotypic plasticity. In this regard, a powerful technique such as RNA-Seq applied to ecologically amenable taxa allows to address issues that are not possible in classic model species. Here, we study gene expression profiles and larval performance of the cactophilic siblings Drosophila buzzatii and D. koepferae reared in media that approximate natural conditions and evaluate both chemical and nutritional components of the diet. These closely related species are complementary in terms of host-plant use since the primary host of one is the secondary of the other. D. koepferae is mainly a columnar cactus dweller while D. buzzatii prefers Opuntia hosts. Results: Our comparative study shows that D. buzzatii and D. koepferae have different transcriptional strategies to face the challenges posed by their natural resources. The former has greater transcriptional plasticity, and its response is mainly modulated by alkaloids of its secondary host, while the latter has a more canalized genetic response, and its transcriptional plasticity is associated with the cactus species. Conclusions: Our study unveils a complex pleiotropic genetic landscape in both species, with functional links that relate detox responses and redox mechanisms with developmental and neurobiological processes. These results contribute to deepen our understanding of the role of host plant shifts and natural stress driving ecological specialization. [ABSTRACT FROM AUTHOR]

Background: Managed grasslands are global sources of atmospheric methanol, which is one of the most abundant volatile organic compounds in the atmosphere and promotes oxidative capacity for tropospheric and stratospheric ozone depletion. The phyllosphere is a favoured habitat of plant-colonizing methanol-utilizing bacteria. These bacteria also occur in the rhizosphere, but their relevance for methanol consumption and ecosystem fluxes is unclear. Methanol utilizers of the plant-associated microbiota are key for the mitigation of methanol emission through consumption. However, information about grassland plant microbiota members, their biodiversity and metabolic traits, and thus key actors in the global methanol budget is largely lacking. Results: We investigated the methanol utilization and consumption potentials of two common plant species (Festuca arundinacea and Taraxacum officinale) in a temperate grassland. The selected grassland exhibited methanol formation. The detection of 13C derived from 13C-methanol in 16S rRNA of the plant microbiota by stable isotope probing (SIP) revealed distinct methanol utilizer communities in the phyllosphere, roots and rhizosphere but not between plant host species. The phyllosphere was colonized by members of Gamma- and Betaproteobacteria. In the rhizosphere, 13C-labelled Bacteria were affiliated with Deltaproteobacteria, Gemmatimonadates, and Verrucomicrobiae. Less-abundant 13C-labelled Bacteria were affiliated with well-known methylotrophs of Alpha-, Gamma-, and Betaproteobacteria. Additional metagenome analyses of both plants were consistent with the SIP results and revealed Bacteria with methanol dehydrogenases (e.g., MxaF1 and XoxF1-5) of known but also unusual genera (i.e., Methylomirabilis, Methylooceanibacter, Gemmatimonas, Verminephrobacter). 14C-methanol tracing of alive plant material revealed divergent potential methanol consumption rates in both plant species but similarly high rates in the rhizosphere and phyllosphere. Conclusions: Our study revealed the rhizosphere as an overlooked hotspot for methanol consumption in temperate grasslands. We further identified unusual new but potentially relevant methanol utilizers besides well-known methylotrophs in the phyllosphere and rhizosphere. We did not observe a plant host-specific methanol utilizer community. Our results suggest that our approach using quantitative SIP and metagenomics may be useful in future field studies to link gross methanol consumption rates with the rhizosphere and phyllosphere microbiome. [ABSTRACT FROM AUTHOR]

• Context: Biomass prediction is important when dealing for instance with carbon sequestration, wildfire modeling, or bioenergy supply. Although allometric models based on destructive sampling provide accurate estimates, alternative species-specific equations often yield considerably different biomass predictions. An important source of intra-specific variability remains unexplained. • Aims: The aims of the study were to inspect and assess intra-specific differences in aboveground biomass of Pinus brutia Ten. and to fill the gap in knowledge on biomass prediction for this species. • Methods: Two hundred one trees between 2.3 and 55.8 cm in diameter at breast height were sampled throughout the eastern- and southernmost natural distribution area of P. brutia, in Middle East, where it forms different stand structures. Allometric equations were fitted separately for two countries. The differences in biomass prediction at tree, stand, and forest level were analyzed. The effect of stand structure and past forest management was discussed. • Results: Between-country differences in total aboveground biomass were not large. However, differences in biomass stock were large when tree components were analyzed separately. Trees had higher stem biomass and lower crown biomass in dense even-aged stands than in more uneven-aged and sparse stands. • Conclusion: Biomass and carbon predictions could be improved by taking into account stand structure in biomass models. [ABSTRACT FROM AUTHOR]

Background: Jubaea chilensis (Molina) Baillon, is a uniquely large palm species endemic to Chile. It is under threatened status despite its use as an ornamental species throughout the world. This research seeks to identify the phyllotaxis of the species based on an original combination of non-destructive data acquisition technologies, namely Magnetic Resonance Imaging (MRI) in saplings and young individuals and Terrestrial Laser Scanning (TLS) in standing specimens, and a novel analysis methodology. Results: Two phyllotaxis parameters, parastichy pairs and divergence angle, were determined by analyzing specimens at different developmental stages. Spiral phyllotaxis patterns of J. chilensis progressed in complexity from parastichy pairs (3,2) and (3,5) in juvenile specimens and (5,3), (8,5) and (8,13) for adult specimens. Divergence angle was invariable and averaged 136.9°, close to the golden angle. Phyllotactic pattern changes associated with establishment phase, the adult vegetative and the adult reproductive phases were observed. Both technologies, MRI and TLS proved to be adequate for the proposed analysis. Conclusions: Understanding phyllotactic transitions may assist identification of developmental stages of wild J. chilensis specimens. The proposed methodology may also be useful for the study of other palm species. [ABSTRACT FROM AUTHOR]

Key message: This document describes a dataset obtained from a field sampling program conducted in Alberta, Canada. Field data were used to describe the structure and composition of forest stands, including several fuel loads (e.g., surface, understory, canopy fuels). The dataset can be downloaded from https://doi.org/10.17605/OSF.IO/FZ8E4 and metadata is available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/527efb49-43b4-43eb-88b2-70535ff99fc5 We present a quality-checked and curated dataset obtained from a field sampling program conducted in the province of Alberta, Canada. Field data were used to describe the structure and composition of forest stands documented in 476 sampling events. Each sampling event record consists of 42 different variables, including several fuel loads (e.g., surface, understory, canopy fuels). The dataset has been created for operational and research applications including but not limited to fuel classification, estimation of fuel attributes from remote sensing technologies, fuel treatment planning, fire behavior prediction, and use in high resolution fire growth models. [ABSTRACT FROM AUTHOR]

Background: The bermudagrass (Cynodon dactylon (L.) Pers) roots responded differently in terms of morphological and anatomical characteristics under diverse submergence conditions, and they developed aerenchyma under non-flooding condition. In order to understand these mechanisms, bermudagrass cuttings were used as experimental material to examine their biomass, root morphology, and aerenchyma formation under three different water treatments, including control (CK), shallow submergence (SS), and deep submergence (DS). Results: The total root length, root volume, root surface area, and biomass of bermudagrass were largest in CK, followed by SS and DS. However, the average root diameter was greater in each of DS and SS than that in CK. Root aerenchyma formation was observed in CK, and submergence boosted the aerenchyma formation and the root cavity rate. Furthermore, our study found that the process of aerenchyma formation began with the increase of cell volume and cell separation to form a narrow space, and these cells gradually died to form matured aerenchyma cavity, which belongs to schizo-lysigenous aerenchyma. Meanwhile, typical biomarkers of programmed cell death were also observed. Conclusion: Overall, these results suggested that submergence inhibited the accumulation of biomass and root growth, but facilitated aerenchyma formation by increasing root diameter. [ABSTRACT FROM AUTHOR]

Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion. Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression. Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat. Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management. [ABSTRACT FROM AUTHOR]

Key message: The lack of efficient phenotyping capacities has been recognized as a bottleneck in forestry phenotyping and breeding. Modern phenotyping technologies use systems equipped with various imaging sensors to automatically collect high volume phenotypic data that can be used to assess trees' various attributes. Context: Efficient phenotyping has the potential to spark a new Green Revolution, and it would provide an opportunity to acquire growth parameters and dissect the genetic bases of quantitative traits. Phenotyping platforms aim to link information from several sources to derive knowledge about trees' attributes. Aims: Various tree phenotyping techniques were reviewed and analyzed along with their different applications. Methods: This article presents the definition and characteristics of forest tree phenotyping and reviews newly developed imaging-based practices in forest tree phenotyping. Results: This review addressed a wide range of forest trees phenotyping applications, including a survey of actual inter- and intra-specific variability, evaluating genotypes and species response to biotic and abiotic stresses, and phenological measurements. Conclusion: With the support of advanced phenotyping platforms, the efficiency of traits phenotyping in forest tree breeding programs is accelerated. [ABSTRACT FROM AUTHOR]

Background: Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum. [ABSTRACT FROM AUTHOR]

Background: Roots are vital to plant performance because they acquire resources from the soil and provide anchorage. However, it remains difficult to assess root system size and distribution because roots are inaccessible in the soil. Existing methods to phenotype entire root systems range from slow, often destructive, methods applied to relatively small numbers of plants in the field to rapid methods that can be applied to large numbers of plants in controlled environment conditions. Much has been learned recently by extensive sampling of the root crown portion of field-grown plants. But, information on large-scale genetic and environmental variation in the size and distribution of root systems in the field remains a key knowledge gap. Minirhizotrons are the only established, non-destructive technology that can address this need in a standard field trial. Prior experiments have used only modest numbers of minirhizotrons, which has limited testing to small numbers of genotypes or environmental conditions. This study addressed the need for methods to install and collect images from thousands of minirhizotrons and thereby help break the phenotyping bottleneck in the field. Results: Over three growing seasons, methods were developed and refined to install and collect images from up to 3038 minirhizotrons per experiment. Modifications were made to four tractors and hydraulic soil corers mounted to them. High quality installation was achieved at an average rate of up to 84.4 minirhizotron tubes per tractor per day. A set of four commercially available minirhizotron camera systems were each transported by wheelbarrow to allow collection of images of mature maize root systems at an average rate of up to 65.3 tubes per day per camera. This resulted in over 300,000 images being collected in as little as 11 days for a single experiment. Conclusion: The scale of minirhizotron installation was increased by two orders of magnitude by simultaneously using four tractor-mounted, hydraulic soil corers with modifications to ensure high quality, rapid operation. Image collection can be achieved at the corresponding scale using commercially available minirhizotron camera systems. Along with recent advances in image analysis, these advances will allow use of minirhizotrons at unprecedented scale to address key knowledge gaps regarding genetic and environmental effects on root system size and distribution in the field. [ABSTRACT FROM AUTHOR]

Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease. [ABSTRACT FROM AUTHOR]

Background: High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial. Results: The results show that 40–42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of rs = 0.50–0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI). Conclusions: The proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust. [ABSTRACT FROM AUTHOR]

Background: The root system architecture (RSA) of alfalfa (Medicago sativa L.) affects biomass production by influencing water and nutrient uptake, including nitrogen fixation. Further, roots are important for storing carbohydrates that are needed for regrowth in spring and after each harvest. Previous selection for a greater number of branched and fibrous roots significantly increased alfalfa biomass yield. However, phenotyping root systems of mature alfalfa plant is labor-intensive, time-consuming, and subject to environmental variability and human error. High-throughput and detailed phenotyping methods are needed to accelerate the development of alfalfa germplasm with distinct RSAs adapted to specific environmental conditions and for enhancing productivity in elite germplasm. In this study methods were developed for phenotyping 14-day-old alfalfa seedlings to identify measurable root traits that are highly heritable and can differentiate plants with either a branched or a tap rooted phenotype. Plants were grown in a soil-free mixture under controlled conditions, then the root systems were imaged with a flatbed scanner and measured using WinRhizo software. Results: The branched root plants had a significantly greater number of tertiary roots and significantly longer tertiary roots relative to the tap rooted plants. Additionally, the branch rooted population had significantly more secondary roots > 2.5 cm relative to the tap rooted population. These two parameters distinguishing phenotypes were confirmed using two machine learning algorithms, Random Forest and Gradient Boosting Machines. Plants selected as seedlings for the branch rooted or tap rooted phenotypes were used in crossing blocks that resulted in a genetic gain of 10%, consistent with the previous selection strategy that utilized manual root scoring to phenotype 22-week-old-plants. Heritability analysis of various root architecture parameters from selected seedlings showed tertiary root length and number are highly heritable with values of 0.74 and 0.79, respectively. Conclusions: The results show that seedling root phenotyping is a reliable tool that can be used for alfalfa germplasm selection and breeding. Phenotypic selection of RSA in seedlings reduced time for selection by 20 weeks, significantly accelerating the breeding cycle. [ABSTRACT FROM AUTHOR]

Nondestructive testing of plant roots is a hot topic in recent years. The traditional measurement process is time-consuming and laborious, and it is impossible to analyze the state of plant roots without destroying the sample. Recent studies have shown that as an excellent nondestructive measurement method, although electrical impedance spectroscopy (EIS) has made great achievements in many botanical research fields such as plant morphology and stress resistance, there are still limitations. This review summarizes the application of EIS in plant root measurement. The experiment scheme, instrument and electrode, excitation frequency range, root electrical characteristics, equivalent circuit, and combination of EIS and artificial intelligence (AI) are discussed. Furthermore, the review suggests that future research should focus on miniaturization of measurement equipment, standardization of planting environment and intelligentization of root diagnosis, so as to better apply EIS technology to in situ root nondestructive measurement. [ABSTRACT FROM AUTHOR]

Background: Phalaris species (Poaceae) occupy diverse environments throughout all continents except Antarctica. Phalaris arundinacea is an important forage, ornamental, wetland restoration and biofuel crop grown globally as well as being a wetland invasive. The nuclear ribosomal internal transcribed spacer (ITS) region has been used for Phalaris barcoding as a DNA region with high nucleotide diversity for Phalaris species identification. Recent findings that P. arundinacea populations in Minnesota USA are most likely native and not European prompted this analysis to determine whether Eurasian vs. native North American P. arundinacea differed in ITS regions. Our objectives were to amplify and compare ITS regions (ITS1 and ITS2) of historic herbaria (1882–2001) and extant (fresh) Phalaris specimens; analyze ITS regions for species-specific polymorphisms (diagnostic SNPs) and compare ITS regions of historic Phalaris specimens with known, extant Phalaris species. Results: We obtained complete ITS1 and ITS2 sequences from 31 Phalaris historic (herbaria samples, 1908 to 2001) and five extant (fresh) specimens. Herbaria Phalaris specimens did not produce new SNPs (single nucleotide polymorphisms) not present in extant specimens. Diagnostic SNPs were identified in 8/12 (66.6%) Phalaris species. This study demonstrates the use of herbaria tissue for barcoding as a means for improved species identification of Phalaris herbaria specimens. No significant correlation between specimen age and genomic DNA concentration was found. Phalaris arundinacea showed high SNP variation within its clade, with the North American being distinctly different than other USA and most Eurasian types, potentially allowing for future identification of specific SNPs to geographic origin. Conclusions: While not as efficient as extant specimens to obtain DNA, Phalaris herbaria specimens can produce high quality ITS sequences to evaluate historic genetic resources and facilitate identification of new species-specific barcodes. No correlation between DNA concentration and age of historic samples (119 year range) occurred. Considerable polymorphism was exhibited in the P. arundinacea clade with several N. American accessions being distinct from Eurasian types. Further development of within species- and genus-specific barcodes could contribute to designing PCR primers for efficient and accurate identification of N. American P. arundinacea. Our finding of misidentified Phalaris species indicates the need to exercise stringent quality control measures on newly generated sequence data and to approach public sequence databases in a critical way. [ABSTRACT FROM AUTHOR]

One of the most prominent life-history trade-offs involves the cost of reproduction. Oxidative stress has been proposed to be involved in this trade-off and has been associated with reduced life span. There is currently an unclear relationship between oxidative cost and the reproduction-longevity trade-off. The current study, using a non-lethal and minimally invasive (only a single blood sample and no euthanasia) method, investigated whether an oxidative cost (oxidative stress) to reproduction would be apparent in two long-lived eusocial mole-rats, the naked mole-rat (NMR), Heterocephalus glaber, and the Damaraland mole-rat (DMR), Fukomys damarensis, where breeding colony members live longer than non-breeder conspecifics. We measured the direct redox balance in plasma by measuring the oxidative stress index (OSI) based on the ratio of total oxidant status and total antioxidant activity in breeders and non-breeders of both sexes, in the two species. NMR had significantly higher OSI between breeders and non-breeders of each sex, whereas DMR showed no significant differences except for total antioxidant capacity (TAC). The mode of reproductive suppression and the degree of reproductive investment in NMR may explain to some degree the redox balance difference between breeders and non-breeders. DMR show minimal physiological changes between breeders and non-breeders except for the mode of reproduction, which may explain some variations in TAC and TOS values, but similar OSI between breeders and non-breeders. [ABSTRACT FROM AUTHOR]

Key message: Increment cores can provide improved predictive capabilities of the modulus of elasticity (MOE) of sawn boards. Multiple increment cores collected at different heights in a tree provide marginally increased accuracy over a single breast-height core, with higher labour costs. Approximately 50% of the variability of the static bending MOE of individual boards is explained by the predicted MOE obtained from a single increment core taken at breast height. Context: Prediction of individual board MOE can lead to accurate optimisation of the value extracted from forest resources, and enhanced decision-making on the management and allocation of the resource to different processors, and improve the processors ability to optimise grade allocation. Aims: The objective of this study is to predict the MOE of individual sawn boards from the MOE measured from cores collected from standing trees. Methods: A five-parameter logistic (5PL) function and radial basis function interpolants are used to obtain a continuous distribution of MOE throughout a log. By developing a “virtual sawing” methodology, we predict the individual board MOE for sixty-eight trees consisting of locally developed F1 and F2 hybrid pines (Pinuscaribaea var. hondurensis × Pinuselliottii var. elliottii). Results: Moderate correlations for individual board predictions are observed, with R2 values ranging from 0.47 to 0.53. Good correlations between average predicted board MOE and average measured MOE are also observed, with R2 ≈ 0.83. A pseudo-three-dimensional approach, accounting for variation in height in the tree, affords marginally greater accuracy and predictive capability at the cost of increased data collection and processing. By using a single breast-height core, we can obtain a similar level of prediction of individual board MOE. Conclusion: We have presented a novel non-destructive evaluation approach to predict the MOE of individual boards sawn from trees. This approach can be adapted to other wood properties, and other wood products obtained from trees. [ABSTRACT FROM AUTHOR]

Background: ICON® Maxx (Syngenta) is an insecticide treatment kit of pyrethroid and binding agent for long-lasting treatment of mosquito nets. Interim recommendation for use on nets was granted by the World Health Organization (WHO) after successful evaluation in experimental huts following multiple washes. A full WHO recommendation is contingent upon demonstration of continued bio-efficacy after 3 years of use. Methods: A household-randomized prospective study design was used to assess ICON Maxx-treated nets over 3 years in north-eastern Tanzania. Conventional treated nets (with lambda-cyhalothrin, but without binder) served as a positive control. At 6-monthly intervals, cross-sectional household surveys monitored net use and physical integrity, while cone and tunnel tests assessed insecticidal efficacy. Pyrethroid content was determined after 12 and 36 months. A parallel cohort of nets was monitored annually for evidence of net deterioration and attrition. Results: After 12 months' use, 97% of ICON Maxx-treated nets but only 67% of CTN passed the WHO efficacy threshold for insecticidal durability (> 80% mortality in cone or tunnel or 90% feeding inhibition in tunnel). After 24- and 36-months use, 67% and 26% of ICON Maxx treated nets met the cone criteria, respectively, and over 90% met the combined cone and tunnel criteria. Lambda-cyhalothrin content after 36 months was 17% (15.8 ± 4.3 mg/m2) of initial content. ICON Maxx nets were used year-round and washed approximately 4 times per year. In cross-sectional survey after 36 months the average number of holes was 20 and hole index was 740 cm2 per net. Cohort nets had fewer holes and smaller hole index than cross-sectional nets. However, only 15% (40/264) of cohort nets were not lost to follow-up or not worn out after 36 months. Conclusions: Because more than 80% of nets met the WHO efficacy criteria after 36 months use, ICON Maxx was granted WHO full recommendation. Cross-sectional and cohort surveys were complementary and gave a fuller understanding of net durability. To improve net usage and retention, stronger incentives and health messaging should be introduced in WHO LLIN longitudinal trials. Untreated polyester nets may be made long-lastingly insecticidal in Africa through simple household treatment using ICON Maxx pyrethroid-binder kits. [ABSTRACT FROM AUTHOR]

Background: Estimation of nitrate nitrogen (NO3−–N) content in petioles is one of the key approaches for monitoring nitrogen (N) nutrition in crops. Rapid, non-destructive, and accurate evaluation of NO3−–N contents in cotton petioles under drip irrigation is of great significance. Methods: In this study, we discussed the use of hyperspectral data to estimate NO3−–N contents in cotton petioles under drip irrigation at different N treatments and growth stages. The correlations among trilateral parameters and six vegetation indices and petiole NO3−–N contents were first investigated, after which a traditional regression model for petioles NO3−–N content was established. A wavelet neural network (WNN) model for estimating petiole NO3−–N content was also established. In addition, the performance of WNN was compared to those of random forest (RF), radial basis function neural network (RBF) and back propagation neural network (BP). Results: Between the blue edge amplitude (Db) and blue edge area (SDb) of the blue edge parameters was the optimal index for the estimation model of petiole NO3−–N content. We found that the prediction results of the blue edge parameters and WNN were 7.3% higher than the coefficient of determination (R2) of the first derivative vegetation index and WNN. Root mean square error (RMSE) and mean absolute error (MAE) were 25.2% and 30.9% lower than first derivative vegetation, respectively, and the performance was better than that of RF, RBF and BP. Conclusions: An inexpensive approach consisting of the WNN algorithm and blue edge parameters can be used to enhance the accuracy of NO3−–N content estimation in cotton petioles under drip irrigation. [ABSTRACT FROM AUTHOR]

Background: Rice is one of the most important grain crops worldwide. The accurate and dynamic monitoring of Leaf Area Index (LAI) provides important information to evaluate rice growth and production. Methods: This study explores a simple method to remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for a variety of rice cultivars throughout the entire growing season. Forty eight different rice cultivars were planted in the study site and field campaigns were conducted once a week. For each campaign, several widely used vegetation indices (VI) were calculated from canopy reflectance obtained by 12-band UAV images, canopy height was derived from UAV RGB images and LAI was destructively measured by plant sampling. Results: The results showed the correlation of VI and LAI in rice throughout the entire growing season was weak, and for all tested indices there existed significant hysteresis of VI vs. LAI relationship between rice pre-heading and post-heading stages. The model based on the product of VI and canopy height could reduce such hysteresis and estimate rice LAI of the whole season with estimation errors under 24%, not requiring algorithm re-parameterization for different phenology stages. Conclusions: The progressing phenology can affect VI vs. LAI relationship in crops, especially for rice having quite different canopy spectra and structure after its panicle exsertion. Thus the models solely using VI to estimate rice LAI are phenology-specific and have high uncertainties for post-heading stages. The model developed in this study combines both remotely sensed canopy height and VI information, considerably improving rice LAI estimation at both pre- and post-heading stages. This method can be easily and efficiently implemented in UAV platforms for various rice cultivars during the entire growing season with no rice phenology and cultivar pre-knowledge, which has great potential for assisting rice breeding and field management studies at a large scale. [ABSTRACT FROM AUTHOR]

Background: Understanding the determinants of free asparagine concentration in wheat grain is necessary to reduce levels of the processing contaminant acrylamide in baked and toasted wheat products. Although crop management strategies can help reduce asparagine concentrations, breeders have limited options to select for genetic variation underlying this trait. Asparagine synthetase enzymes catalyse a critical step in asparagine biosynthesis in plants and, in wheat, are encoded by five homeologous gene triads that exhibit distinct expression profiles. Within this family, TaASN2 genes are highly expressed during grain development but TaASN-B2 is absent in some varieties. Results: Natural genetic diversity in the asparagine synthetase gene family was assessed in different wheat varieties revealing instances of presence/absence variation and other polymorphisms, including some predicted to affect the function of the encoded protein. The presence and absence of TaASN-B2 was determined across a range of UK and global common wheat varieties and related species, showing that the deletion encompassing this gene was already present in some wild emmer wheat genotypes. Expression profiling confirmed that TaASN2 transcripts were only detectable in the grain, while TaASN3.1 genes were highly expressed during the early stages of grain development. TaASN-A2 was the most highly expressed TaASN2 homeologue in most assayed wheat varieties. TaASN-B2 and TaASN-D2 were expressed at similar, lower levels in varieties possessing TaASN-B2. Expression of TaASN-A2 and TaASN-D2 did not increase to compensate for the absence of TaASN-B2, so total TaASN2 expression was lower in varieties lacking TaASN-B2. Consequently, free asparagine concentrations in field-produced grain were, on average, lower in varieties lacking TaASN-B2, although the effect was lost when free asparagine accumulated to very high concentrations as a result of sulphur deficiency. Conclusions: Selecting wheat genotypes lacking the TaASN-B2 gene may be a simple and rapid way for breeders to reduce free asparagine concentrations in commercial wheat grain. [ABSTRACT FROM AUTHOR]

Background: Tri-axial accelerometers have been used to remotely describe and identify in situ behaviours of a range of animals without requiring direct observations. Datasets collected from these accelerometers (i.e. acceleration, body position) are often large, requiring development of semi-automated analyses to classify behaviours. Marine fishes exhibit many "burst" behaviours with high amplitude accelerations that are difficult to interpret and differentiate. This has constrained the development of accurate automated techniques to identify different "burst" behaviours occurring naturally, where direct observations are not possible. Methods: We trained a random forest machine learning algorithm based on 624 h of accelerometer data from six captive yellowtail kingfish during spawning periods. We identified five distinct behaviours (swim, feed, chafe, escape, and courtship), which were used to train the model based on 58 predictive variables. Results: Overall accuracy of the model was 94%. Classification of each behavioural class was variable; F1 scores ranged from 0.48 (chafe) – 0.99 (swim). The model was subsequently applied to accelerometer data from eight free-ranging kingfish, and all behaviour classes described from captive fish were predicted by the model to occur, including 19 events of courtship behaviours ranging from 3 s to 108 min in duration. Conclusion: Our findings provide a novel approach of applying a supervised machine learning model on free-ranging animals, which has previously been predominantly constrained to direct observations of behaviours and not predicted from an unseen dataset. Additionally, our findings identify typically ambiguous spawning and courtship behaviours of a large pelagic fish as they naturally occur. [ABSTRACT FROM AUTHOR]

Background: Plants influence their root and rhizosphere microbial communities through the secretion of root exudates. However, how specific classes of root exudate compounds impact the assembly of root-associated microbiotas is not well understood, especially not under realistic field conditions. Maize roots secrete benzoxazinoids (BXs), a class of indole-derived defense compounds, and thereby impact the assembly of their microbiota. Here, we investigated the broader impacts of BX exudation on root and rhizosphere microbiotas of adult maize plants grown under natural conditions at different field locations in Europe and the USA. We examined the microbiotas of BX-producing and multiple BX-defective lines in two genetic backgrounds across three soils with different properties. Results: Our analysis showed that BX secretion affected the community composition of the rhizosphere and root microbiota, with the most pronounced effects observed for root fungi. The impact of BX exudation was at least as strong as the genetic background, suggesting that BX exudation is a key trait by which maize structures its associated microbiota. BX-producing plants were not consistently enriching microbial lineages across the three field experiments. However, BX exudation consistently depleted Flavobacteriaceae and Comamonadaceae and enriched various potential plant pathogenic fungi in the roots across the different environments. Conclusions: These findings reveal that BXs have a selective impact on root and rhizosphere microbiota composition across different conditions. Taken together, this study identifies the BX pathway as an interesting breeding target to manipulate plant-microbiome interactions. 4atbM_n1-Xmh1rBaQ1rPHq Video Abstract [ABSTRACT FROM AUTHOR]

We tested the hypothesis that deep-sea fishes have poorly mineralized bone relative to shallower-dwelling species using data from a single family that spans a large depth range. The family Liparidae (snailfishes, Cottiformes) has representatives across the entire habitable depth range for bony fishes (0 m–> 8000 m), making them an ideal model for studying depth-related trends in a confined phylogeny. We used micro-computed tomography (micro-CT) scanning to test three aspects of skeletal reduction in snailfishes (50 species) across a full range of habitat depths: 1) reduction of structural dimensions, 2) loss of skeletal elements, and 3) reduction in bone density. Using depth data from the literature, we found that with increasing depth, the length of the dentary, neurocranium, and suborbital bones decreases. The ventral suction disk decreases width with increasing maximum habitat depth and is lost entirely in some deeper-living taxa, though not all. Although visual declines in bone density in deeper-living taxa were evident across full skeletons, individual densities of the lower jaw, vertebra, suction disk, hypural plate, and otoliths did not significantly decline with any depth metric. However, pelagic and polar taxa tended to show lower density bones compared to other species in the family. We propose that skeletal reductions allow snailfishes to maintain neutral buoyancy at great depths in the water column, while supporting efficient feeding and locomotion strategies. These findings suggest that changes in skeletal structure are non-linear and are driven not only by hydrostatic pressure, but by other environmental factors and by evolutionary ancestry, calling the existing paradigm into question. [ABSTRACT FROM AUTHOR]

Background: X-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm "Rootine" and present its succeeding version "Rootine v.2". In addition to gray value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (i) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (ii) a calculation of the root average gray value based on a histogram analysis, (iii) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (iv) a false negatives recovery based on shape criteria to increase root recovery. We compare the segmentation results of Rootine v.1 and Rootine v.2 with the results of root washing and subsequent analysis with WinRhizo. We use a benchmark dataset of maize roots (Zea mays L. cv. B73) grown in repacked soil for two scenarios with differing soil heterogeneity and image quality. Results: We demonstrate that Rootine v.2 outperforms its preceding version in terms of root recovery and enables to match better the root diameter distribution data obtained with root washing. Despite a longer processing time, Rootine v.2 comprises less user-defined parameters and shows an overall greater usability. Conclusion: The proposed method facilitates higher root detection accuracy than its predecessor and has the potential for improving high-throughput root phenotyping procedures based on X-ray computed tomography data analysis. [ABSTRACT FROM AUTHOR]

• Key message: A novel non-destructive method has been developed to predict modulus of elasticity (MOE) of logs using measurements taken from cores extracted from discs. The trees were felled and cut into logs to allow validation of our method; however, similar results would be obtained if the cores were extracted from standing trees. The method shows that a single core from breast height is sufficient to predict MOE of logs, allowing early grading and sorting of logs for optimal use and processing. • Context: Early estimation of log MOE allows efficient sorting and grading of logs which can improve the financial return and reduce wastage of wood. • Aims: This work aims to predict the MOE of logs accurately from measurements taken on cores obtained from trees. • Methods: The MOE of the logs was predicted using ultrasound measurements conducted on small segments obtained from cores using two different approaches: segment average and integral average. Sixty-eight trees from locally developed F1 and F2 hybrid pines (slash pine × Caribbean pine hybrids, Pinus elliottii var. elliottii × P. caribaea var. hondurensis (PEE × PCH cross)) were felled and cut into logs to validate the results. The Beam Identification by Non-destructive Grading (BING) method was used to measure a reference dynamic MOE (BING-MOE) for each log, and this was compared with the estimated log MOE. • Results: Strong correlations (r = 0.79 to 0.91) between measured log MOE and estimated log MOE were obtained. This study revealed that a single core from the breast height (1.3 m) of a tree allows a good prediction of the log MOE. Tree height, spacing, and diameter had no significant effect on the log MOE prediction. The segment average MOE under predicts the BING-MOE, whereas the integral average method provides very little bias in the prediction. Furthermore, the prediction errors from the regression analysis for all logs were greater in the segment average method compared with the integral average method. • Conclusion: This paper presented a novel non-destructive evaluation method capable of predicting the MOE of the whole log by combining data available from a single breast-height core extracted from standing trees with our integral average MOE approach. The integral average method predicted the BING-MOE more accurately with lower bias compared with other existing tools without any complex equipment, analysis, and statistical calibration for segregating out individual trees or stands. The method can potentially be used to predict the log MOE of other tree species and extended to predict MOE of individual boards that can be sawn from a log. [ABSTRACT FROM AUTHOR]

Background: Condition indices (CIs) are used in ecological studies as a way of measuring an individual animal's health and fitness. Noninvasive CIs are estimations of a relative score of fat content or rely on a ratio of body mass compared to some measure of size, usually a linear dimension such as tarsus or snout-vent length. CIs are generally validated invasively by lethal fat extraction as in a seasonal sample of individuals in a population. Many alternatives to lethal fat extraction are costly or time consuming. As an alternative, dual-energy X-ray absorptiometry (DXA) allows for non-destructive analysis of body composition and enables multiple measurements during an animal's life time. DXA has never been used for ecological studies in a small, free-ranging lizard before, therefore we calibrated this method against a chemical extraction of fat from a sample of 6 geckos (Israeli fan toed gecko Ptyodactylus guttatus) ranging in body mass between 4.2–11.5 g. We then used this calibrated DXA measurements to determine the best linear measurement calculated CI for this species. Results: We found that fat mass measured with DXA was significantly correlated with the mass of chemically extracted fat for specimens more than 4.8 g (N = 5, R2 = 0.995, P < 0.001). Fat percentage regressed with body mass significantly predicted the DXA fat percentage (N = 29, R2adj. = 0.862, p < 0.001). Live wet mass was significantly correlated with predicted fat mass (N = 30, R2 = 0.984, P < 0.001) for specimens more than 4.8 g. Among the five calculated non-invasive CIs that we tested, the best was mass/SVL. Conclusions: We recommend that in situations where DXA cannot be used, that the most accurate of the body condition estimators for this species is mass/SVL (snout-vent length) for both sexes. [ABSTRACT FROM AUTHOR]

Key message: Radial growth in a group ofPinus radiataD. Don. trees varies in magnitude around the circumference and follows synchronous but arrhythmic dynamics. Context: Eccentric and irregular girth growth is typically associated to specific growth responses, but it is generally assumed to be small or absent during normal development. The dynamics by which excess growth is formed are unclear. Aims: The objective of this study is to determine if growth anisotropy is a commonly occurring phenomenon without apparent mechanical imbalance of the tree and to document the temporality of differential radial growth. Methods: Six mature P. radiata trees were equipped with point dendrometers at different circumferential positions. Growth rates and periods of activity were monitored over 4 months. Results: The highest growth differential on a single tree exceeded a factor of two. The direction of the highest growth varied between trees. In one case, that direction switched over time. The amount of anisotropy was explained by differences in the number of growing days and growth rate entropy. Conclusion: Tree stem formation in fast-growing softwoods is a biological process characterized by high spatial heterogeneity and intermittent temporal activity. [ABSTRACT FROM AUTHOR]

Key message: The use of reduced planting spacings is an important strategy to increase the carbon storage in the above-belowground biomass and should be recommended for future exploitation of forest energy plantations when the purpose intended is the production of biomass for energy. Context: Recent concerns about global warming have resulted in more concerted studies on quantifying carbon storage in forest systems. Forest energy plantations play an essential role in the carbon storage. Aims: We proposed to evaluate the carbon storage and partitioning in short-rotation forest plantations and to characterize the elemental composition and energetic properties of the forest species Eucalyptus grandis W. Hill ex Maiden, Mimosa scabrella Benth, and Ateleia glazioviana Baill, grown under four planting spacings in Southern Brazil. Methods: A field study was conducted in order to evaluate forest carbon stock and wood composition using samples collected by direct method. The four spacings evaluated were 2.0 × 1.0, 2.0 × 1.5, 3.0 × 1.0, and 3.0 × 1.5 m. Results: The Eucalyptus grandis stored 327.1 Mg C ha−1 at 2.0 × 1.5-m spacing. When compared with the 3.0 × 1.5-m spacing, we observed a reduction of 29% in carbon stored. All forest species showed higher carbon storage in the following partitioning pattern: trunk>roots>branches>leaves>litter. Forest species energetic properties and elemental composition were not affected by planting spacing. On the other hand, variations according the tree portions were observed. For the carbon stocks in the soil, we observed an average accumulated carbon stock for the forest species studied of 77.4 Mg C ha−1 (0–40 cm). Conclusion: Forest managers can accelerate growth and increase the forest carbon storage and biomass yield by using reduced planting spacing that are smaller than the current pattern used by the majority of the forest producers, which is 3.0 × 1.5 m. For Eucalyptus grandis and Mimosa scabrella, the planting spacings recommended to produce biomass and improve carbon stocks were 2.0 × 1.5 and 2.0 × 1.0 m, respectively. [ABSTRACT FROM AUTHOR]

Background: Emergence of both viral zoonoses from bats and diseases that threaten bat populations has highlighted the necessity for greater insights into the functioning of the bat immune system. Particularly when considering hibernating temperate bat species, it is important to understand the seasonal dynamics associated with immune response. Body temperature is one of the factors that modulates immune functions and defence mechanisms against pathogenic agents in vertebrates. To better understand innate immunity mediated by phagocytes in bats, we measured respiratory burst and haematology and blood chemistry parameters in heterothermic greater mouse-eared bats (Myotis myotis) and noctules (Nyctalus noctula) and homeothermic laboratory mice (Mus musculus). Results: Bats displayed similar electrolyte levels and time-related parameters of phagocyte activity, but differed in blood profile parameters related to metabolism and red blood cell count. Greater mouse-eared bats differed from mice in all phagocyte activity parameters and had the lowest phagocytic activity overall, while noctules had the same quantitative phagocytic values as mice. Homeothermic mice were clustered separately in a high phagocyte activity group, while both heterothermic bat species were mixed in two lower phagocyte activity clusters. Stepwise regression identified glucose, white blood cell count, haemoglobin, total dissolved carbon dioxide and chloride variables as the best predictors of phagocyte activity. White blood cell counts, representing phagocyte numbers available for respiratory burst, were the best predictors of both time-related and quantitative parameters of phagocyte activity. Haemoglobin, as a proxy variable for oxygen available for uptake by phagocytes, was important for the onset of phagocytosis. Conclusions: Our comparative data indicate that phagocyte activity reflects the physiological state and blood metabolic and cellular characteristics of homeothermic and heterothermic mammals. However, further studies elucidating trade-offs between immune defence, seasonal lifestyle physiology, hibernation behaviour, roosting ecology and geographic patterns of immunity of heterothermic bat species will be necessary. An improved understanding of bat immune responses will have positive ramifications for wildlife and conservation medicine. [ABSTRACT FROM AUTHOR]

Background: Human-resident microbes can influence both health and disease. Investigating the microbiome using next-generation sequencing technology has revealed examples of mutualism and conflict between microbes and humans. Comparing to bacteria, the viral component of the microbiome (i.e., the "virome") is understudied. Somatic tissues of healthy individuals are usually inaccessible for the virome sampling; therefore, there is limited understanding of the presence and distribution of viruses in tissues in healthy individuals and how virus infection associates with human gene expression and perturbs immunological homeostasis. Results: To characterize the human virome in a tissue-specific manner, here we performed meta-transcriptomic analysis using the RNA-sequencing dataset from the Genotype-Tissue Expression (GTEx) Project. We analyzed the 8991 RNA-sequencing data obtained from 51 somatic tissues from 547 individuals and successfully detected 39 viral species in at least one tissue. We then investigated associations between virus infection and human gene expression and human disease onset. We detected some expected relationships; for instance, hepatitis C virus infection in the liver was strongly associated with interferon-stimulated gene upregulation and pathological findings of chronic hepatitis. The presence of herpes simplex virus type 1 in one subject's brain strongly associated with immune gene expression. While torque teno virus was detected in a broad range of human tissues, it was not associated with interferon responses. Being notable in light of its association with lymphoproliferative disorders, Epstein-Barr virus infection in the spleen and blood was associated with an increase in plasma cells in healthy subjects. Human herpesvirus 7 was often detected in the stomach; intriguingly, it associated with the proportion of human leukocytes in the stomach as well as digestive gene expression. Moreover, virus infections in the local tissues associated with systemic immune responses in circulating blood. Conclusions: To our knowledge, this study is the first comprehensive investigation of the human virome in a variety of tissues in healthy individuals through meta-transcriptomic analysis. Further investigation of the associations described here, and application of this analytical pipeline to additional datasets, will be useful to reveal the impact of viral infections on human health. [ABSTRACT FROM AUTHOR]

Background: Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms. Results: In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R2 was greater than 0.80), and biomass estimation at leaf group level was the most precise (R2 = 0.97, RMSE = 2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well. Conclusion: We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices. [ABSTRACT FROM AUTHOR]

Background: To study the impact of climate warming on crops, it is crucial to have a warming equipment suitable for their field environment. A facility is needed that can provide suitable combinations of different temperatures at reasonable cost for large plots. Results: Here, an additional field warming facility option named the hot-blast warming facility (HBWF), which comprised heaters, blowers, wind breaks, and a control board was developed. An application case based on HBWF was carried out to assess elevated temperature effects on rice in Central China during 2015 and 2016. We tested four elevated temperature treatments on four rice cultivars under paddy field conditions and measured yield and its components. Heating convection air directly, the facility could increase the temperature of the rice canopy up to 1–2 °C, which could properly simulate global warming. Considering the costs, the HBWF reduced the operating costs because of its relatively lower power consumption (0.164 kW/m2), which was 80% lower than that of Free Air Temperature Increase. Our results demonstrate that the HBWF could build a 25 m2 homogeneous heating area and had little effect on the relative humidity under a paddy field environment. Warming treatments significantly reduced the grain yield by 4.4–22.7% in 2015, and 30.8–61.9% in 2016, compared to the control. The main contribution to the significant decrease of the grain yields was the decrease in seed setting rate. Moreover, a reduction of 1000-grain weight led to the decline in grain yield. The increasing ranges of the temperature simulated by HBWF were stable in different years, however, whether the elevated treatments demonstrated significant difference on rice growth mainly decided by the basic atmospheric temperature (as the control) during the growth period. Conclusions: The new warming facility is suitable for field trials to assess elevated temperature combinations and provides an extra equipment option for use in elevated temperature research in the future. [ABSTRACT FROM AUTHOR]

Background: The disposable soma theory of ageing assumes that organisms optimally trade-off limited resources between reproduction and longevity to maximize fitness. Early reproduction should especially trade-off against late reproduction and longevity because of reduced investment into somatic protection, including immunity. Moreover, as optimal reproductive strategies of males and females differ, sexually dimorphic patterns of senescence may evolve. In particular, as males gain fitness through mating success, sexual competition should be a major factor accelerating male senescence. In a single experiment, we examined these possibilities by establishing artificial populations of the mealworm beetle, Tenebrio molitor, in which we manipulated the sex-ratio to generate variable levels of investment into reproductive effort and sexual competition in males and females. Results: As predicted, variation in sex-ratio affected male and female reproductive efforts, with contrasted sex-specific trade-offs between lifetime reproduction, survival and immunity. High effort of reproduction accelerated mortality in females, without affecting immunity, but high early reproductive success was observed only in balanced sex-ratio condition. Male reproduction was costly on longevity and immunity, mainly because of their investment into copulations rather than in sexual competition. Conclusions: Our results suggest that T. molitor males, like females, maximize fitness through enhanced longevity, partly explaining their comparable longevity. [ABSTRACT FROM AUTHOR]

Background: A previous cohort study in Malawi showed that users of new insecticide-treated bed nets (ITNs) were significantly protected against malaria compared to non-users, despite moderate levels of pyrethroid resistance among the primary mosquito vectors. The present study investigated whether ITNs that were 1–2 years old continued to protect users in the same area with moderate pyrethroid resistance. Methods: One year following a baseline cross-sectional malaria parasitaemia prevalence survey and universal distribution of deltamethrin ITNs (May 2012), a fixed cohort of 1223 children aged 6–59 months was enrolled (April 2013). Children were tested for parasitaemia at monthly scheduled visits and at unscheduled sick visits from May to December 2013 using rapid diagnostic tests. ITN use the prior night and the condition of ITNs (based on presence of holes) was assessed by caregiver self-report. The incidence rate ratio (RR) comparing malaria infection among users and non-users of ITNs was modelled using generalized estimating equations adjusting for potential confounders and accounting for repeated measures on each child. The protective efficacy (PE) of ITN use was calculated as 1 − RR. Results: In this cohort, self-reported ITN use remained consistently high (> 95%) over the study period. Although users of ITNs were slightly more protected compared to non-users of ITNs, the difference in incidence of infection was not statistically significant (RR 0.83, 95% confidence interval [CI] 0.54–1.27). Among ITN users, malaria incidence was significantly lower in users of ITNs with no holes (of any size) compared to users of ITNs with ≥ 1 hole (RR 0.82, 95% CI 0.69–0.98). Conclusions: There was no significant PE of using 1–2 year-old ITNs on the incidence of malaria in children in an area of moderate pyrethroid resistance, but among ITN users, the authors found increased protection by ITNs with no holes compared to ITNs with holes. Given the moderate levels of pyrethroid resistance in the primary malaria vector and recent evidence of added benefits of ITNs with synergists or non-pyrethroid insecticides, next-generation ITNs may be a useful strategy to address pyrethroid resistance and should be further explored in Malawi. [ABSTRACT FROM AUTHOR]

Background: Almond is an emerging crop due to the health benefits of almond consumption including nutritional, anti-inflammatory, and hypocholesterolaemia properties. Traditional almond producers were concentrated in California, Australia, and Mediterranean countries. However, almond is currently present in more than 50 countries due to breeding programs have modernized almond orchards by developing new varieties with improved traits related to late flowering (to reduce the risk of damage caused by late frosts) and tree architecture. Almond tree architecture and flowering are acquired and evaluated through intensive field labour for breeders. Flowering detection has traditionally been a very challenging objective. To our knowledge, there is no published information about monitoring of the tree flowering dynamics of a crop at the field scale by using color information from photogrammetric 3D point clouds and OBIA. As an alternative, a procedure based on the generation of colored photogrammetric point clouds using a low cost (RGB) camera on-board an unmanned aerial vehicle (UAV), and an semi-automatic object based image analysis (OBIA) algorithm was created for monitoring the flower density and flowering period of every almond tree in the framework of two almond phenotypic trials with different planting dates. Results: Our method was useful for detecting the phenotypic variability of every almond variety by mapping and quantifying every tree height and volume as well as the flowering dynamics and flower density. There was a high level of agreement among the tree height, flower density, and blooming calendar derived from our procedure on both fields with the ones created from on-ground measured data. Some of the almond varieties showed a significant linear fit between its crown volume and their yield. Conclusions: Our findings could help breeders and researchers to reduce the gap between phenomics and genomics by generating accurate almond tree information in an efficient, non-destructive, and inexpensive way. The method described is also useful for data mining to select the most promising accessions, making it possible to assess specific multi-criteria ranking varieties, which are one of the main tools for breeders. [ABSTRACT FROM AUTHOR]

Purpose: The Cd and Hg contents in soils can be elevated due to coal mining. To estimate the effects of these two heavy metals on soil enzymes and the key microbial groups, coal mine–affected agricultural soils were cultured for 30 days with Cd and/or Hg. Methods: Soil enzyme activities were measured by a colorimetric method, and microbial abundance was assessed according to real-time quantitative PCR analysis of the 16S rRNA and 18S rRNA genes. In addition, the microbial communities were analyzed by Illumina sequencing. Results: Heavy metals inhibited soil enzyme activities. For example, both Cd and Hg decreased 25.52–34.89% of the soil catalase activity; the highest level of Hg (30 mg kg−1) decreased 76.50–89.88% of the soil urease activity and 85.60–92.92% of the soil dehydrogenase activity; and the soil acid phosphatase activity significantly decreased by 15.18–32.64% under all the levels of Cd and decreased 17.09–30.32% under the high levels of the Cd–Hg combination (> 3 mg kg−1). In addition, increased Cd levels affected bacterial number more than fungal abundance; however, addition of Hg alone decreased the bacterial number but increased the fungal abundance. Furthermore, the bacterial communities but not fungal communities were altered by heavy metals. A total of 23 highly sensitive genera and 16 highly resistant genera were identified. The sensitive genera were assigned to Actinobacteria, Acidobacteria, Candidate division WS3, Chloroflexi, Gemmatimonadetes, Proteobacteria, and Thermotogae, while the resistant genera were affiliated to Bacteroidetes and Proteobacteria. Conclusion: Soils containing the highest level of the combination of Cd and Hg exhibited the lowest soil enzyme activities; bacterial communities were more sensitive to heavy metal contamination than fungi. [ABSTRACT FROM AUTHOR]

Context Accurate estimates of tree component and total biomass are necessary for evaluating alternative forest management strategies for biomass feedstock, carbon sequestration, and products. Previous biomass equations developed for white birch trees in natural stands provided substantially biased predictions for white birch plantations. Aims A new system of additive tree biomass equations was developed for juvenile white birch plantations in the northeastern China. Methods With destructive biomass sampling data from 501 trees sampled from white birch provenance and family trails at ages 7, 9, 10, and 13 in three provinces, a system of nonlinear additive tree biomass equations based on DBH and tree height was developed using the nonlinear seemingly unrelated regressions (NSUR) approach. Results Compared with previously published equations developed for natural white birch forests, the new system provided more accurate predictions of white birch tree component and aboveground and total biomass, especially of branch, foliage, and root biomass. Conclusion The new system extended the applicability of biomass equations to white birch plantations in the northeastern China. [ABSTRACT FROM AUTHOR]

Context Fast-growing and high-yielding poplar plantations have been identified as major commercial forests in China. Intensive management of irrigation and fertilization can greatly increase productivity of plantations. Quantitative investigations on the cumulative effect of drip irrigation and fertigation over years are quite infrequent. Aims We aimed to quantitatively evaluate the effects of drip irrigation and fertigation plans on tree growth and productivity in a poplar plantation, and to analyze their possible cumulative promoting effect over multiple years. Methods Treatments including nine drip irrigation and fertigation combinations, and single furrow irrigation in spring as control, were conducted in a poplar plantation for three successive years. The combined treatments consist of three irrigation levels (WP−75, WP−50, and WP−25, in ascending order) and three levels of nitrogen addition (N60, N120, and N180, in ascending order). Soil nitrogen and water content were measured throughout the 3 years. Based on tree surveys, tree growth, volume, and biomass production were evaluated each year. Results Nitrogen and water availability in the surface soil increased in the drip irrigation- and fertigation-treated plots. Drip irrigation and fertigation treatments resulted in significant higher growth, stem volume, and biomass productions compared to control. Biomass increments in drip irrigation- and fertigation-treated plots were 4.8–50.0, 5.3–26.5, and 4.3–52.2% higher than control in the three experimental years, respectively, with WP−25N180 and WP−50N180 recording the highest increments. Fertigation showed cumulative effects over multiple years and the positive effects increased with the dosage. However, irrigation showed little cumulative effect and the greatest effect was obtained under medium level. Conclusion Combined drip irrigation and fertigation greatly promoted the plantation productivity. The combined management effect varied with application plans and plantation ages, and showed a cumulative effect over years. [ABSTRACT FROM AUTHOR]

Context The sustainability of forest stands depends on the ability of species and communities to adapt by combining plasticity and genetic evolution. Although well-documented at the scale of species distributions, the variability and adaptation of forest tree genetic resources are less understood at the short-distance scale. Aims We analysed the effects of genetic and environmental factors on the local-scale phenotypic diversity of traits related to adaptation in Abies alba. We also sought to highlight local adaptation, revealing past selection. Methods Six adaptive traits related to growth, phenology and survival were measured on seedlings from 57 half-sib families collected from 15 provenances and planted in a nine-site reciprocal transplant experiment distributed along three elevational gradients. Results Most part of the phenotypic variability was attributed to the environmental factors. Provenances and families had also significant effects on seedling performances, but the genetic variability was mostly attributed to the families. No pattern of local adaptation was observed, except in the presence of lateral branches in the driest garden. Conclusion The absence of local adaptation suggests a similar ability of all silver fir populations to develop in the various environments. This result provides favourable conditions for coping with the ongoing climate change without exotic resources enrichment. [ABSTRACT FROM AUTHOR]

Background: Conventional metabolomics approaches face the problem of hidden metabolic phenotypes where only fluxes are altered but pool sizes stay constant. Metabolic flux experiments are used to detect such hidden flux phenotypes. These experiments are, however, time consuming, may be cost intensive, and involve specialists for modeling. We fill the gap between conventional metabolomics and flux modeling. We present rapid stable isotope tracing assays and analysis strategies of 13C labeling data. For this purpose, we combine the conventional metabolomics approach that detects significant relative changes of metabolite pool sizes with analyses of differential utilization of 13C labeled carbon. As a test case, we use uniformly labeled 13C-sucrose. Results: We present petiole and hypocotyl feeding assays for the rapid in situ feeding (≤ 4 h) of isotopically labeled metabolic precursor to whole Arabidopsis thaliana rosettes. The assays are assessed by conventional gas chromatography-mass spectrometry based metabolite profiling that was extended by joined differential analysis of 13C-labeled sub-pools and of 13C enrichment of metabolites relative to the enrichment of 13C-sucrose within each sample. We apply these analyses to the sink to source transition continuum of leaves from single A. thaliana rosettes and characterize the associated relative changes of metabolite pools, as well as previously hidden changes of sucrose-derived carbon partitioning. We compared the contribution of sucrose as a carbon source in predominantly sink to predominantly source leaves and identified a set of primary metabolites with differential carbon utilization during sink to source transition. Conclusion: The presented feeding assays and data evaluation strategies represent a rapid and easy-to-use tool box for enhanced metabolomics studies that combine differential pool size analysis with screening for differential carbon utilization from defined stable isotope labeled metabolic precursors. [ABSTRACT FROM AUTHOR]