Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.

KEY MESSAGE: We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles. Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments, and their time-series soil moisture was measured. The models were evaluated using three cross-validation schemes. Although accuracy of the proposed models did not surpass that of single-trait genomic prediction, the results suggest that our model can capture G × E, especially the latter growth period for the random forest model. Also, significant variations in the G × E of the canopy height during the early growth period were visualized using the spline model. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G × E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data.

Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data.

Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant's stability under drought stresses. We built a genomic prediction model (MTRRM model) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of MTRRM models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MTRRM model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.

Stochastic variational variable selection for high-dimensional microbiome data.

BACKGROUND: The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the approach of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge. RESULTS: We propose a novel approach to improve the performance of the widely used DMM approach by combining three ideas: (i) we propose an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; (ii) to address the computational burden of high-dimensional microbiome data, we propose a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation; and (iii) we extend the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a variational parameter. Using the proposed method, stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment, the human gut microbiome data from three published datasets of large-scale case-control studies and the healthy human microbiome data from the Human Microbiome Project. CONCLUSIONS: SVVS demonstrates a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1000 samples. Furthermore, a core set of representative microbial species is identified using SVVS that can improve the interpretability of Bayesian mixture models for a wide range of microbiome studies. Video Abstract.

High throughput method of 16S rRNA gene sequencing library preparation for plant root microbial community profiling.

Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plant science and agriculture. Here, we have increased sample handling efficiency in a two-step PCR amplification protocol for 16S rRNA gene sequencing of plant root microbiota, improving DNA extraction using AMPure XP magnetic beads and PCR purification using exonuclease. These modifications reduce sample handling and capture microbial diversity comparable to that obtained by the manual method. We found a buffer with AMPure XP magnetic beads enabled efficient extraction of microbial DNA directly from plant roots. We also demonstrated that purification using exonuclease before the second PCR step enabled the capture of higher degrees of microbial diversity, thus allowing for the detection of minor bacteria compared with the purification using magnetic beads in this step. In addition, our method generated comparable microbiome profile data in plant roots and soils to that of using common commercially available DNA extraction kits, such as DNeasy PowerSoil Pro Kit and FastDNA SPIN Kit for Soil. Our method offers a simple and high-throughput option for maintaining the quality of plant root microbial community profiling.

Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.

Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross-combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.

Schwannoma in and around the porta hepatis: radiological features in eight cases and literature review.

PURPOSE: Schwannomas in and around the porta hepatis (porta hepatic schwannomas) are rare benign tumors easily misdiagnosed as other pathologies, including malignancies. We aimed to evaluate their imaging features on ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and 18F-fluorodeoxyglucose-positron emission tomography/CT (FDG-PET/CT). METHODS: We performed a multi-institutional retrospective study by reviewing the clinical and imaging findings of pathologically proven eight porta hepatic schwannomas (mean age, 55 years; range, 38-80 years; one male and seven females). Preoperative imaging included three ultrasonography, eight CT, eight MRI, and two FDG-PET/CT. RESULTS: All patients were asymptomatic. The mean tumor size was 61.9 mm (range, 30-180 mm), and all tumors demonstrated well-defined lesions on ultrasonography and their solid components showed soft tissue attenuation on non-contrast CT. MRI showed two distinct components in all cases: the component with T1-weighted hypointensities and T2-weighted hyperintensities with poor enhancement (suggestive of Antoni B histology); the component with T2-weighted hypointensities with gradually increasing enhancement (suggestive of Antoni A histology), resulting in a heterogeneous pattern on post-contrast CT or MRI (8/8, 100%). The separated deviation of surrounding bile ducts and vessels without obstruction allowed the recognition of extrahepatic localization and their benign nature. A ginger root-like morphology (2/8, 25%) seemed to be suggestive of extension along the Glisson's sheath, although this finding was not seen frequently. CONCLUSION: Recognizing imaging features such as extrahepatic location, benign nature with internal structures suggestive of Antoni A/B histology, and characteristic tumor extension may provide key diagnostic clues for porta hepatic schwannomas.

Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations.

With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

Effects of irrigation on root growth and development of soybean: A 3-year sandy field experiment.

Increasing the water use efficiency of crops is an important agricultural goal closely related to the root system -the primary plant organ for water and nutrient acquisition. In an attempt to evaluate the response of root growth and development of soybean to water supply levels, 200 genotypes were grown in a sandy field for 3 years under irrigated and non-irrigated conditions, and 14 root traits together with shoot fresh weight and plant height were investigated. Three-way ANOVA revealed a significant effect of treatments and years on growth of plants, accounting for more than 80% of the total variability. The response of roots to irrigation was consistent over the years as most root traits were improved by irrigation. However, the actual values varied between years because the growth of plants was largely affected by the field microclimatic conditions (i.e., temperature, sunshine duration, and precipitation). Therefore, the best linear unbiased prediction values for each trait were calculated using the original data. Principal component analysis showed that most traits contributed to principal component (PC) 1, whereas average diameter, the ratio of thin and medium thickness root length to total root length contributed to PC2. Subsequently, we focused on selecting genotypes that exhibited significant improvements in root traits under irrigation than under non-irrigated conditions using the increment (I-index) and relative increment (RI-index) indices calculated for all traits. Finally, we screened for genotypes with high stability and root growth over the 3 years using the multi-trait selection index (MTSI).Six genotypes namely, GmJMC130, GmWMC178, GmJMC092, GmJMC068, GmWMC075, and GmJMC081 from the top 10% of genotypes scoring MTSI less than the selection threshold of 7.04 and 4.11 under irrigated and non-irrigated conditions, respectively, were selected. The selected genotypes have great potential for breeding cultivars with improved water usage abilities, meeting the goal of water-saving agriculture.

Metabolome Profiling of Heat Priming Effects, Senescence, and Acclimation of Bread Wheat Induced by High Temperatures at Different Growth Stages.

Our previous study described stage-specific responses of 'Norin 61' bread wheat to high temperatures from seedling to tillering (GS1), tillering to flowering (GS2), flowering to full maturity stage (GS3), and seedling to full maturity stage (GS1-3). The grain development phase lengthened in GS1 plants; source tissue decreased in GS2 plants; rapid senescence occurred in GS3 plants; all these effects occurred in GS1-3 plants. The present study quantified 69 flag leaf metabolites during early grain development to reveal the effects of stage-specific high-temperature stress and identify markers that predict grain weight. Heat stresses during GS2 and GS3 showed the largest shifts in metabolite contents compared with the control, followed by GS1-3 and GS1. The GS3 plants accumulated nucleosides related to the nucleotide salvage pathway, beta-alanine, and serotonin. Accumulation of these compounds in GS1 plants was significantly lower than in the control, suggesting that the reduction related to the high-temperature priming effect observed in the phenotype (i.e., inhibition of senescence). The GS2 plants accumulated a large quantity of free amino acids, indicating residual effects of the previous high-temperature treatment and recovery from stress. However, levels in GS1-3 plants tended to be close to those in the control, indicating an acclimation response. Beta-alanine, serotonin, tryptophan, proline, and putrescine are potential molecular markers that predict grain weight due to their correlation with agronomic traits.

Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters.

The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because the inclusion of remote sensing data in multitrait models improved prediction accuracies of target traits. However, the present multitrait GP model cannot incorporate high-dimensional remote sensing data due to the difficulty in the estimation of a covariance matrix among the traits, which leads to failure in improving its prediction accuracy. In this study, we focused on growth models to express growth patterns using remote sensing data with a few parameters and investigated whether a multitrait GP model using these parameters could derive better prediction accuracy of soybean [Glycine max (L.) Merr.] biomass. A total of 198 genotypes of soybean germplasm were cultivated in experimental fields, and longitudinal changes of their canopy height and area were measured continuously via remote sensing with an unmanned aerial vehicle. Growth parameters were estimated by applying simple growth models and incorporated into the GP of biomass. By evaluating heritability and correlation, we showed that the estimated growth parameters appropriately represented the observed growth curves. Also, the use of these growth parameters in the multitrait GP model contributed to successful biomass prediction. We conclude that the growth models could describe the genetic variation of soybean growth curves based on several growth parameters. These dimension-reduction growth models will be indispensable for extracting useful information from remote sensing data and using this data in GP and plant breeding.

Stage-Specific Characterization of Physiological Response to Heat Stress in the Wheat Cultivar Norin 61.

Bread wheat (Triticum aestivum) is less adaptable to high temperatures than other major cereals. Previous studies of the effects of high temperature on wheat focused on the reproductive stage. There are few reports on yield after high temperatures at other growth stages. Understanding growth-stage-specific responses to heat stress will contribute to the development of tolerant lines suited to high temperatures at various stages. We exposed wheat cultivar "Norin 61" to high temperature at three growth stages: seedling-tillering (GS1), tillering-flowering (GS2), and flowering-maturity (GS3). We compared each condition based on agronomical traits, seed maturity, and photosynthesis results. Heat at GS2 reduced plant height and number of grains, and heat at GS3 reduced the grain formation period and grain weight. However, heat at GS1 reduced senescence and prolonged grain formation, increasing grain weight without reducing yield. These data provide fundamental insights into the biochemical and molecular adaptations of bread wheat to high-temperature stresses and have implications for the development of wheat lines that can respond to high temperatures at various times of the year.

Risk factors and appropriate therapeutic strategies for thrombotic microangiopathy after allogeneic HSCT.

Transplant-associated thrombotic microangiopathy (TA-TMA) is a fatal complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, so far, no large cohort study determined the risk factors and the most effective therapeutic strategies for TA-TMA. Thus, the present study aimed to clarify these clinical aspects based on a large multicenter cohort. This retrospective cohort study was performed by the Kyoto Stem Cell Transplantation Group (KSCTG). A total of 2425 patients were enrolled from 14 institutions. All patients were aged ≥16 years, presented with hematological diseases, and received allo-HSCT after the year 2000. TA-TMA was observed in 121 patients (5.0%) on day 35 (median) and was clearly correlated with inferior overall survival (OS) (hazard ratio [HR], 4.93). Pre- and post-HSCT statistically significant risk factors identified by multivariate analyses included poorer performance status (HR, 1.69), HLA mismatch (HR, 2.17), acute graft-versus-host disease (aGVHD; grades 3-4) (HR, 4.02), Aspergillus infection (HR, 2.29), and veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS; HR, 4.47). The response rate and OS significantly better with the continuation or careful reduction of calcineurin inhibitors (CNI) than the conventional treatment strategy of switching from CNI to corticosteroids (response rate, 64.7% vs 20.0%). In summary, we identified the risk factors and the most appropriate therapeutic strategies for TA-TMA. The described treatment strategy could improve the outcomes of patients with TA-TMA in the future.

Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models.

Genomic prediction (GP) is expected to become a powerful technology for accelerating the genetic improvement of complex crop traits. Several GP models have been proposed to enhance their applications in plant breeding, including environmental effects and genotype-by-environment interactions (G×E). In this study, we proposed a two-step model for plant biomass prediction wherein environmental information and growth-related traits were considered. First, the growth-related traits were predicted by GP. Second, the biomass was predicted from the GP-predicted values and environmental data using machine learning or crop growth modeling. We applied the model to a 2-year-old field trial dataset of recombinant inbred lines of japonica rice and evaluated the prediction accuracy with training and testing data by cross-validation performed over two years. Therefore, the proposed model achieved an equivalent or a higher correlation between the observed and predicted values (0.53 and 0.65 for each year, respectively) than the model in which biomass was directly predicted by GP (0.40 and 0.65 for each year, respectively). This result indicated that including growth-related traits enhanced accuracy of biomass prediction. Our findings are expected to contribute to the spread of the use of GP in crop breeding by enabling more precise prediction of environmental effects on crop traits.

An Oculopharyngeal Subtype of Guillain-Barré Syndrome Sparing the Trochlear and Abducens Nerves.

Guillain-Barré syndrome (GBS) comprises a group of polyneuropathies characterized by rapid progression of limb paralysis. Various subtypes of GBS have been reported. The oculopharyngeal subtype of GBS is currently understood to be primarily a cranial polyneuropathy without limb weakness or cerebellar ataxia. In our case of 62-year-old man, gastrointestinal infection was followed by paranesthesia of the hands. He had bilateral ptosis, pharyngeal disorder, and tongue and bifacial weakness. We diagnosed oculopharyngeal subtype of GBS. It responded to intravenous immunoglobulin. This case highlights the need for further characterization of unusual GBS subtypes.

[Successful palliative surgical treatment for drug-resistant epilepsy after anti-N-methyl-D-aspartate (NMDA) receptor encephalitis: Two case reports].

Epilepsy surgery for patients with drug-resistant epilepsy after anti-N-methyl-D-aspartate (NMDA) receptor encephalitis has been rarely reported. The present study reports two patients with anti-NMDA receptor encephalitis, who later underwent epilepsy surgery due to drug-resistant epilepsy. The patients had refractory status epilepticus in the acute phase. The cerebrospinal fluid was positive for anti-NMDA receptor antibodies. Systemic corticosteroid therapy and plasma exchange were effective. Seizure control, however, worsened over several months after discharge, and was refractory to antiepileptic drugs. They underwent palliative epilepsy surgery, and their seizure control improved. Epilepsy surgery should be considered in patients with drug-resistant epilepsy after anti-NMDA receptor encephalitis.

End-Capping π-Conjugated Systems with Medium-Sized Sulfur-Containing Rings: A Route Towards Solution-Processable Air-Stable Semiconductors.

The sulfur-containing nine-membered heterocycle thiacyclononene (TN) was evaluated as a new type of end-capping group for π-conjugated systems. A systematic study on TN-capped α-oligothiophenes (TNnTs; n=4-7) revealed that the capping with TN, which adopts a bent conformation, imparts the resulting oligothiophenes with drastically increased solubility at approximately 140 °C and high electrochemical stability, whereas the electronic structure remains virtually unperturbed. The even-numbered oligothiophenes TN4T and TN6T form characteristic offset herringbone-type packing structures on account of the steric repulsion between the TN rings and the presence of intermolecular nonbonding S⋅⋅⋅S interactions. This packing mode in combination with the high solubility enabled the solution-process fabrication of field-effect transistors based on TN6T, which exhibited a high performance without degradation even upon exposure to air.

Acute basophilic leukemia associated with the t(16;21)(p11;q22)/FUS-ERG fusion gene.

We herein report a rare case of acute basophilic leukemia with t(16;21)(p11;q22) generating the FUS-ERG fusion gene. The basophilic nature of leukemia blasts was demonstrated by cytomorphology, toluidine blue metachromasia, mature basophil-associated antigen expression, and characteristic granules under electron microscopy. The molecular link between t(16;21)/FUS-ERG and basophilic differentiation remains unclear.