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Abstract Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non‐additive genetic effects is essential in genome prediction (GP) models of crops with highly heterozygous polyploid genomes. This study incorporates non‐additive genetic effects and pedigree information using machine learning methods to track sugarcane breeding lines and enhance the prediction by assessing the degree of association between genotypes. This study measured the stalk biomass and sugar content of 297 clones from 87 families within a breeding population used in the Japanese sugarcane breeding program. Subsequently, we conducted analyses based on the marker genotypes of 33,149 single‐nucleotide polymorphisms. To validate the accuracy of GP in the population, we first predicted the prediction accuracy of the best linear unbiased prediction (BLUP) based on a genomic relationship matrix. Prediction accuracy was assessed using two different cross‐validation methods: repeated 10‐fold cross‐validation and leave‐one‐family‐out cross‐validation. The accuracy of GP of the first and second methods ranged from 0.36 to 0.74 and 0.15 to 0.63, respectively. Next, we compared the prediction accuracy of BLUP and two machine learning methods: random forests and simulation annealing ensemble (SAE), a newly developed machine learning method that explicitly models the interaction between variables. Both pedigree and genomic information were utilized as input in these methods. Through repeated 10‐fold cross‐validation, we found that the accuracy of the machine learning methods consistently surpassed that of BLUP in most cases. In leave‐one‐family‐out cross‐validation, SAE demonstrated the highest accuracy among the methods. These results underscore the effectiveness of GP in Japanese sugarcane breeding and highlight the significant potential of machine learning methods.

Objectives To investigate the association between multimorbidity during pregnancy and neurodevelopmental delay in offspring using data from a Japanese nationwide birth cohort study.Design This study was a prospective birth cohort study.Setting This study population included 104 059 fetal records who participated in The Japan Environment and Children’s Study from 2011 to 2014.Participants Pregnant women whose children had undergone developmental testing were included in this analysis.Primary and secondary outcome measures Neurodevelopment of offspring was assessed using the Japanese version of the Ages and Stages Questionnaire, third edition, comprising five developmental domains. The number of comorbidities among the pregnant women was categorised as zero, single disease or multimorbidity (two or more diseases). Maternal chronic conditions included in multimorbidity were defined as conditions with high prevalence among women of reproductive age. A multivariate logistic regression analysis was conducted to examine the association between multimorbidity in pregnant women and offspring development.Results Pregnant women with multimorbidity, single disease and no disease accounted for 3.6%, 30.6% and 65.8%, respectively. The ORs for neurodevelopmental impairment during the follow-up period were similar for infants of mothers with no disease comorbidity and those with a single disease comorbidity. However, the ORs for neurodevelopmental impairment were significantly higher for children born to mothers with multimorbidity compared with those born to healthy mothers.Conclusion An association was observed between the number of comorbidities in pregnant women and developmental delay in offspring. Multimorbidity in pregnant women may be associated with neurodevelopmental delay in their offspring. Further research is required in this regard in many other regions of the world.

The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics.

Abstract Objective While opioids are a key part of palliative care, few studies have evaluated opioid demand in the home care context. This study aims to compare opioid usage in home care and hospital care settings. Methods This cross-sectional study retrospectively recruited patients receiving palliative care in home care and hospital settings, between November 2018 and October 2020. Opioid prescriptions were standardized to oral morphine equivalent (OME) doses at 7 and 14 days prior to death and analyzed. Additional analysis performed multivariable linear regression on the outcome of OME at 7 days, adjusting for medical setting and confounders in patients with opioid prescriptions. Results After 21 exclusions, 209 patients (48 home care and 161 hospital care) were eligible for analysis. The home care group had a higher mean age (74.8 years) and Palliative Prognosis Score (50), than the hospital group (70.1 and 40, respectively). Mean OME at 7 and 14 days before death was numerically higher in the home care group (72.8 mg/day and 53.0 mg/day, respectively) than the hospital care group (57.7 mg/day and 35.7 mg/day). Student’s t-test produced p-values of 0.49 and 0.32, and the Wilcoxon rank sum test found p-values of 0.24 and 0.11 at 7 and 14 days, respectively. Multivariable regression analysis of the home care group found mean OME of 40.7 mg/day; 95% confidence interval [-0.62, 82.0 (mg/day)], p = 0.06. Additional analysis found a p-value of 0.06 for medical setting. Conclusions We did not find a statistically significant difference in opioid use between home care and hospital care. However, the numerically higher rate of use in the home care group suggests that further research is warranted.

Anorectal malignant melanoma (ARMM) is extremely rare and generally lethal, irrespective of the treatment administered. The disease is often diagnosed late, metastases being present in approximately two-thirds of patients at the time of initial diagnosis. Solitary metastasis of ARMM to a distant organ is exceedingly rare. A 76-year-old woman with a history of laparoscopic abdominoperineal resection of an ARMM 13 months previously, was found to have a solitary liver metastasis in the follow-up computed tomography. A preoperative work-up showed no other distant metastases nor contraindication to surgery. It was therefore considered that resection was indicated. The metachronous solitary liver metastasis from an ARMM was treated by laparoscopic wedge hepatectomy of the eighth segment 18 months after excision of her primary ARMM. Adjuvant therapy with pembrolizumab was initiated and continued at 6-week intervals. The patient has not exhibited any immune related Adverse Effects (irAE) during or subsequent to treatment with pembrolizmab and has now completed 12 months of adjuvant pembrolizumab therapy, having survived 33 months from the initial operation for primary ARMM, and remaining recurrence-free 14 months after hepatectomy. ARMM is extremely rare and resection of a metachronous solitary metastasis followed by adjuvant therapy has not previously been reported. We hope this case will be useful for clinicians who might treat similar patients.

Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function, whose input is a weighted sum of multiple features for the allocation, including expected genetic variance of progenies and selection criteria such as different types of breeding values, to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency.

Background: Heavy metals such as lead (Pb) and cadmium (Cd) have been associated with adverse pregnancy and developmental outcomes, including congenital abnormalities. This study investigated the association between exposure to heavy metals and trace elements during fetal life and congenital limb abnormalities in infants. Methods: This study is based on a prospective ongoing nationwide birth cohort from the Japan Environment and Children’s Study (JECS). The concentrations of Cd, Pb, mercury (Hg), selenium (Se), and manganese (Mn) were measured in maternal blood collected during the mid–late trimesters. Inclusion criteria were available from questionnaires filled in during pregnancy, including information about congenital limb abnormalities at birth or at one month. To examine the associations with limb anomalies and individual chemicals, logistic regression models were applied following log-transformation or division into quartiles of Cd, Pb, Hg, Se, and Mn concentrations. To assess the associations with the heavy metals and trace elements mixture, quantile g-computation was employed. All models were adjusted for age, maternal smoking history, maternal alcohol intake, history of smoking, and infant sex. Results: Data from 90,163 participants were included in the analysis, of whom 369 had congenital limb abnormalities in any of the collected information, and 89,794 had none. Among the 369 cases of congenital limb abnormalities, there were 185 and 142 cases of polydactyly and syndactyly, respectively. The median concentrations of Pb, Cd, Hg, Se, and Mn were 5.85, 0.66, 3.64, 168, and 15.3 ng/g, respectively. There were no associations between maternal blood concentrations of Pb [adjusted odd ratio = 0.83; 95% confidence interval = 0.61, 1.11], Cd [0.87; 0.68, 1.10], Hg [0.88; 0.73, 1.07], Se [1.07; 0.44, 2.59], and Mn [0.91; 0.64, 1.30] with congenital limb abnormalities. No significant association was observed between the mixture of heavy metals and trace elements [0.85; 0.72, 1.02] and any congenital limb abnormalities. Moreover, there was no association with all polydactylies and all syndactylies, or any type of abnormality as a subdivision. Conclusion: At the maternal exposure levels of Cd, Pb, Hg, Se, and Mn assessed in the present study, no association was identified with the risk of developing congenital limb abnormalities in children.

Abstract Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Y p) calculated by a weather-driven crop growth model, called as the “YpCGM method”. We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world’s changing climate.

High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of 5 conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

BackgroundChildhood asthma is known to be affected by a range of factors, including conditions in the indoor environment. While flooring material influences indoor air conditions, the potential association between flooring materials and childhood asthma remains poorly understood in Japan.ObjectiveThe present study aims to assess the association between childhood asthma incidence and the primary flooring material with the ongoing prospective nationwide birth cohort data of the Japan Environment and Children's Study (JECS).MethodsThe JECS gathered data on mothers and children through 15 Regional Centres across Japan. The present study assessed flooring materials used in the home and asthma incidence at age four among children born between 2011 and 2014. We implemented logistic regressions, setting asthma incidence among the children as the outcome and home floor type as the exposure. Additional analyses were conducted, stratifying the home's age as a proxy for tatami age, to assess whether the potential effect of tatami flooring on asthma risk is influenced by its age.ResultsThe present study included total of 75,629 infants. For tatami flooring, the main multivariable regression and additional sub-group regression for homes over ten years old produced odds ratios of 1.09; 95% Confidence Interval (CI) [1.01-1.17] and 1.10; 95% CI [1.00-1.21] compared with flooring, respectively.ConclusionThese results imply that exposure to tatami flooring, particularly in older homes, may be associated with childhood asthma incidence. Moreover, our study highlights the importance of evaluating the relationship between regional and cultural differences between asthma and flooring materials.

Kawasaki disease (KD) is common among pediatric patients and is associated with an increased risk of later cardiovascular complications, though the precise pathophysiology of KD remains unknown. Per- and polyfluoroalkyl substances (PFAS) have gathered notoriety as the causal pathogens of numerous diseases as well as for their immunosuppressive effects. The present epidemiological study aims to assess whether PFAS may affect KD risk. We evaluated research participants included in the ongoing prospective nationwide birth cohort of the Japan Environment and Children’s Study (JECS). Among the over 100,000 pregnant women enrolled in the JECS study, 28 types of PFAS were measured in pregnancy in a subset of participants (N = 25,040). The JECS followed their children born between 2011 and 2014 (n total infants = 25,256; n Kawasaki disease infants = 271), up to age four. Among the 28 types of PFAS, those which were detected in >60 % of participants at levels above the method reporting limit (MRL) were eligible for analyses. Multivariable logistic regressions were implemented on the seven eligible PFAS, adjusting for multiple comparison effects. Finally, we conducted Weighted Quantile Sum (WQS) and Bayesian kernel machine regression (BKMR) to assess the effects of the PFAS mixture on KD. Therefore, we ran the BKMR model using kernel mechanical regression equations to examine PFAS exposure and the outcomes of KD. Upon analysis, the adjusted multivariable regression results did not reach statistical significance for the seven eligible substances on KD, while odds ratios were all under 1.0. WQS regression was used to estimate the mixture effect of the seven eligible PFAS, revealing a negative correlation with KD incidence; similarly, BKMR implied an inverse association between the PFAS mixture effect and KD incidence. In conclusion, PFAS exposure was not associated with increased KD incidence.

Abstract 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

Abstract 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.

Abstract Background Pelvic inflammatory disease (PID) is not a mere transient infection. PID can lead to chronic pain, ectopic pregnancy, and infertility. Although the Centers for Disease Control and Prevention have established minimum diagnostic criteria, including pelvic examination, the diagnostic value of pelvic tenderness has recently garnered controversy. Our meta‐analysis aimed to confirm whether pelvic tenderness, cervical motion tenderness, and adnexal tenderness can help diagnose PID. Methods We searched for studies reporting the diagnostic test accuracy of pelvic tenderness, cervical motion tenderness, and adnexal tenderness among female patients at risk for PID, using MEDLINE, EMBASE, CENTRAL, CINAHL, Google, and Google Scholar through May 25th, 2022. After quality assessment using QUADAS‐2, we performed data synthesis using a bivariate random effect model and Bayesian hierarchical summary receiver operating characteristic model. We then conducted sensitivity analysis excluding studies with non‐PID cases. Results The literature search produced 6769 articles. After quality assessment, 14 studies and their 2808 participants were eligible for synthesis on pelvic tenderness. Laparoscopy, either alone or in combination, was the most frequent reference standard. The main results for pelvic tenderness sensitivity and specificity were 0.81, 95% confidence interval (CI) [0.67–0.90] and 0.40, 95% CI [0.25–0.57], respectively. Sensitivity and specificity were 0.72, 95% CI [0.57–0.83] and 0.50, 95% CI [0.34–0.66], for cervical motion tenderness, and 0.87 [0.64–0.96] and 0.27, 95% CI [0.12–0.52] for adnexal tenderness, respectively. Conclusions Our meta‐analysis suggests that pelvic tenderness assessed by pelvic examination may be useful for PID examination with moderate‐to‐high sensitivity, whereas clinicians should be aware of the diagnostic significance of pelvic tenderness.

Abstract Background Hepatojugular reflux is a cardiac physical examination with a long history of use in heart failure diagnosis across many clinical settings. However, the development of new diagnostic methods has thrown the clinical role of hepatojugular reflux into question. Our meta‐analysis aimed to determine the diagnostic accuracy of hepatojugular reflux and assess its usefulness in diagnosing congestive heart failure among at‐risk patients. Methods This meta‐analysis of studies reporting diagnostic hepatojugular reflux values of patients at risk for congestive heart failure followed PRISMA guidelines. We searched MEDLINE, EMBASE, Web of Science, CENTRAL, and Google Scholar for eligible studies from inception through February 1, 2021. After QUADAS‐2 quality assessment, we conducted data synthesis using the random effects model and a hierarchical summary receiver operating characteristic model. As an additional analysis, we sorted the studies by clinical setting and performed synthesis again. We submitted our protocol to PROSPERO (International Prospective Register of Systematic Reviews; ID No. CRD42020215004). Results The literature search provided 4121 studies for evaluation. Seven studies and their 5195 participants were deemed eligible for synthesis. Clinical diagnosis was the most frequent reference standard. Bivariate random‐effects analysis found hepatojugular reflux sensitivity of 0.12, 95% confidence interval (CI) [0.07–0.19], and specificity of 0.96, 95% CI [0.95–0.97]. The DOR was 29.7, 95% CI [18.4–45.3]. The additional analysis of the emergency settings provided a sensitivity of 0.14, 95% CI [0.12–0.17] and specificity of 0.95, 95% CI [0.93–0.96]. Conclusions Our meta‐analysis suggests that hepatojugular reflux has practical value for diagnosis of congestive heart failure with high specificity.

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.

A fetal growth restriction is related to adverse child outcomes. We investigated risk ratios and population-attributable fractions (PAF) of small-for-gestational-age (SGA) infants in the Japanese population. Among 28,838 infants from five ongoing prospective birth cohort studies under the Japan Birth Cohort Consortium, two-stage individual-participant data meta-analyses were conducted to calculate risk ratios and PAFs for SGA in advanced maternal age, pre-pregnancy underweight, and smoking and alcohol consumption during pregnancy. Risk ratio was calculated using modified Poisson analyses with robust variance and PAF was calculated in each cohort, following common analyses protocols. Then, results from each cohort study were combined by meta-analyses using random-effects models to obtain the overall estimate for the Japanese population. In this meta-analysis, an increased risk (risk ratio, [95% confidence interval of SGA]) was significantly associated with pre-pregnancy underweight (1.72 [1.42–2.09]), gestational weight gain (1.95 [1.61–2.38]), and continued smoking during pregnancy (1.59 [1.01–2.50]). PAF of underweight, inadequate gestational weight gain, and continued smoking during pregnancy was 10.0% [4.6–15.1%], 31.4% [22.1–39.6%], and 3.2% [−4.8–10.5%], respectively. In conclusion, maternal weight status was a major contributor to SGA births in Japan. Improving maternal weight status should be prioritized to prevent fetal growth restriction.

Good appearance throughout the year is important for perennial ornamental plants used for rooftop greenery. However, the methods for evaluating appearance throughout the year, such as plant color and growth activity, are not well understood. In this study, evergreen and winter-dormant parents of Phedimus takesimensis and 94 F1 plants were used for multispectral imaging. We took 16 multispectral image measurements from March 2019 to April 2020 and used them to calculate 15 vegetation indices and the area of plant cover. QTL analysis was also performed. Traits such as the area of plant cover and vegetation indices related to biomass were high during spring and summer (growth period), whereas vegetation indices related to anthocyanins were high in winter (dormancy period). According to the PCA, changes in the intensity of light reflected from the plants at different wavelengths over the course of a year were consistent with the changes in plant color and growth activity. Seven QTLs were found to be associated with major seasonal growth changes. This approach, which monitors not only at a single point in time but also over time, can reveal morphological changes during growth, senescence, and dormancy throughout the year.

BackgroundDespite the broad global use of antibiotics, there is no established definition of early antibiotic treatment failure (EATF) to aid clinical evaluation of treatment, which leads to inconsistent assessments of drug effectiveness.AimThis scoping review aims to identify common components of EATF definitions by synthesizing studies mentioning EATF and its relevant thesaurus matches.DesignScoping review.MethodsThis scoping review was conducted following the PRISMA Scoping review guidelines. A systematic literature search was conducted using MEDLINE (PubMed), CENTRAL, CINAHL, and Web of Science, as well as a manual Google search. Search terms were EATF and its thesaurus matches. After removing duplications, candidate studies were screened by title and abstract prior to full text searches, and quality analysis was performed on eligible studies using the Critical Appraisal Skills Programme. From each eligible study, the timing of evaluation, basic components, and detailed information for each definition of EATF were collected. The components of each definition for EATF were then summarized and counted, and finally the most common essential components were identified.ResultsOur systematic literature search found 2,472 candidate studies. After title and abstract screening, full text search and quality assessment, 61 studies, including 56 original studies and five reviews, were eligible for our analysis. Of these 56 original studies, 43 mentioned the timing of EATF evaluation 72 hours after the start of treatment with antibiotics. From these 43 studies, the most common indicators of EATF were extracted, among which a set of essential components for a definition of EATF were identified: mortality, vital signs, fever, symptoms, and additional treatment.ConclusionsOur scoping review uncovered five essential factors for EATF. Further study is needed to evaluate the validity of our findings.

The change in appearance during the seasonal transitions in ornamental greening plants is an important characteristic. In particular, the early onset of green leaf color is a desirable trait for a cultivar. In this study, we established a method for phenotyping leaf color change by multispectral imaging and performed genetic analysis based on the phenotypes to clarify the potential of the approach in breeding greening plants. We performed multispectral phenotyping and quantitative trait locus (QTL) analysis of an F1 population derived from 2 parental lines of Phedimus takesimensis, known to be a drought and heat-tolerant rooftop plant species. The imaging was conducted in April of 2019 and 2020 when dormancy breakage occurs and growth extension begins. Principal component analysis of 9 different wavelength values showed a high contribution from the first principal component (PC1), which captured variation in the visible light range. The high interannual correlation in PC1 and in the intensity of visible light indicated that the multispectral phenotyping captured genetic variation in the color of leaves. We also performed restriction site-associated DNA sequencing and obtained the first genetic linkage map of Phedimus spp. QTL analysis revealed 2 QTLs related to early dormancy breakage. Based on the genotypes of the markers underlying these 2 QTLs, the F1 phenotypes with early (late) dormancy break, green (red or brown) leaves, and a high (low) degree of vegetative growth were classified. The results suggest the potential of multispectral phenotyping in the genetic dissection of seasonal leaf color changes in greening plants.

The coronavirus disease 2019 (COVID-19) pandemic not only encouraged people to practice good hygiene but also caused behavioral inhibitions and resulted reduction in both endemic and imported infectious diseases. However, the changing patterns of vector-borne diseases under human mobility restrictions remain unclear. Hence, we aimed to investigate the impact of transborder and local mobility restrictions on vector-borne diseases through a descriptive epidemiological study. The analysis was conducted using data from the National Epidemiological Surveillance of Infectious Diseases system in Japan. We defined the pre-pandemic period as the period between the 1st week of 2016 to the 52nd week of 2019 and defined the pandemic period as from the 1st week of 2020 to the 52nd week of 2021, with the assumption that human mobility was limited throughout the pandemic period. This study addressed 24 diseases among notifiable vector borne diseases. Datasets were obtained from weekly reports from the National Epidemiological Surveillance of Infectious Diseases, and the incidence of each vector-borne disease was examined. Interrupted time series analysis was conducted on the epidemic curves for the two periods. Between the pre- and post-pandemic periods, the incidence of dengue fever and malaria significantly decreased, which may be related to limited human transboundary mobility (p = 0.003/0.002). The incidence of severe fever with thrombocytopenia syndrome, scrub typhus, and Japanese spotted fever did not show changes between the two periods or no association with human mobility. This study suggests that behavioral control may reduce the incidence of new mosquito-borne diseases from endemic areas but may not affect tick-borne disease epidemics within an endemic area.

IntroductionAdvances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not guarantee efficient genetic improvement. Indeed, multiple components of breeding schemes may affect the efficiency of genetic improvement and controlling all components may not be possible. In this study, we propose a new application of Bayesian optimisation for optimizing breeding schemes under specific constraints using computer simulation.MethodsBreeding schemes are simulated according to nine different parameters. Five of those parameters are considered constraints, and 4 can be optimised. Two optimisation methods are used to optimise those parameters, Bayesian optimisation and random optimisation.ResultsThe results show that Bayesian optimisation indeed finds breeding scheme parametrisations that provide good breeding improvement with regard to the entire parameter space and outperforms random optimisation. Moreover, the results also show that the optimised parameter distributions differ according to breeder constraints.DiscussionThis study is one of the first to apply Bayesian optimisation to the design of breeding schemes while considering constraints. The presented approach has some limitations and should be considered as a first proof of concept that demonstrates the potential of Bayesian optimisation when applied to breeding schemes. Determining a general "rule of thumb" for breeding optimisation may be difficult and considering the specific constraints of each breeding campaign is important for finding an optimal breeding scheme.

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.

Abstract 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.

Abstract Background Although bacteremia is one of the most pressing situation in the field of hospital medicine, little is known about the differences between community‐ and hospital‐acquired bacteremia (CAB and HAB, respectively). Methods Objective is to know the epidemiologic characteristics of CAB and HAB. Study design is a single‐center retrospective cohort study. Participants were all patients over the age of 16 years who were blood cultures positive at single acute care hospital from April 2013 to March 2018. HAB was defined as positive culture acquired at least 48 h after admission or blood culture‐positive patients transferred from other hospital. The primary outcome was 30 day mortality, and the secondary outcome was 1 year mortality. We compared the primary and secondary outcomes between HAB and CAB using logistic regression analyses. Results There were 325 participants in this study. The number of patients with CAB was 189 (58.1%). HAB was associated with a higher 30 day mortality rate than CAB (n = 31, 22.8% vs. n = 9, 4.8%, adjusted odds ratio (AOR) 2.60; 95% confidence interval (CI) 1.04–6.53, p

Abstract This study examined the efficacy of end-ischemic hypothermic oxygenated machine perfusion preservation (HOPE) using an originally developed machine perfusion system for split-liver transplantation. Porcine split-liver grafts were created via 75% liver resection after 10 min of warm ischemia. In Group 1, grafts were preserved by simple cold storage (CS) for 8 h (CS group; n = 4). In Group 2, grafts were preserved by simple CS for 6 h and end-ischemic HOPE for 2 h (HOPE group; n = 5). All grafts were evaluated using an isolated ex vivo reperfusion model with autologous blood for 2 h. Biochemical markers (aspartate aminotransferase and lactate dehydrogenase levels) were significantly better immediately after reperfusion in the HOPE group than in the CS group. Furthermore, the HOPE group had a better histological score. The levels of inflammatory cytokines (tumor necrosis factor-α, interferon-γ, interleukin-1β, and interleukin-10) were significantly lower after reperfusion in the HOPE group. Therefore, we concluded that end-ischemic HOPE for split-liver transplantation can aid in recovering the graft function and reducing ischemia–reperfusion injury. HOPE, using our originally developed machine perfusion system, is safe and can improve graft function while attenuating liver injury due to preservation.

Abstract Organophosphate is the commonly used pesticide to control pest outbreak, such as those by aphids in many crops. Despite its wide use, however, necrotic lesion and/or cell death following the application of organophosphate pesticides has been reported to occur in several species. To understand this phenomenon, called organophosphate pesticide sensitivity (OPS) in sorghum, we conducted QTL analysis in a recombinant inbred line derived from the Japanese cultivar NOG, which exhibits OPS. Mapping OPS in this population identified a prominent QTL on chromosome 5, which corresponded to Organophosphate-Sensitive Reaction (OSR) reported previously in other mapping populations. The OSR locus included a cluster of three genes potentially encoding nucleotide-binding leucine-rich repeat (NB-LRR, NLR) proteins, among which NLR-C was considered to be responsible for OPS in a dominant fashion. NLR-C was functional in NOG, whereas the other resistant parent, BTx623, had a null mutation caused by the deletion of promoter sequences. Our finding of OSR as a dominant trait is important not only in understanding the diversified role of NB-LRR proteins in cereals but also in securing sorghum breeding free from OPS.

Lymphoepithelioma-like cholangiocarcinoma (LEL-CC) is a type of lymphoepithelioma-like carcinoma (LELC) and a rare variant of primary liver tumor. Although it is uncommon and only 100 cases have been reported thus far, the number of reports has increased in recent years. LEL-CC reportedly occurs more frequently in Asian women; Epstein–Barr virus (EBV) and hepatitis viruses are both strongly associated with tumor development. Here, we describe a 76-year-old woman who exhibited LEL-CC not associated with EBV or hepatitis virus. She was referred to our department with a 3.0-cm × 2.8-cm tumor in the left lobe of the liver. Based on computed tomography and magnetic resonance imaging findings, the tumor was preoperatively diagnosed as hepatocellular carcinoma. Thus, we performed extended left hepatectomy with caudal lobectomy. Histopathological examinations revealed columnar tumor cells with atypical nuclei that proliferated in a cord-like or glandular tubular pattern with dense lymphocytic infiltration. Immunohistochemical analysis showed negative HepPar-1 and arginase findings, indicating non-hepatocyte origin; however, the biliary-type cytokeratins CK7 and CK19 were detected. Based on these findings, the tumor was identified as LEL-CC. EBV-encoded RNA in situ hybridization findings were negative; the patient’s clinical characteristics were not suggestive of hepatitis virus infection. In conclusion, we suggest that clinicians consider LEL-CC as a differential diagnosis for liver tumors in Asian women, including patients without EBV or hepatitis virus.

Abstract Aim Osteoporosis and bone fractures occur often on psychiatric wards. Although recent studies showed that bone mineral density (BMD) decreases in psychiatric patients, many risk factors remain unknown. This study aimed to explore the risk factors for decreased BMD in long‐term psychiatric inpatients in a closed ward. Methods A cross‐sectional study of psychiatric inpatients hospitalized for over 20 weeks was conducted. Patients were divided into three groups according to BMD: normal, osteopenia, and osteoporosis. Psychiatric symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS). The relevant history of psychiatric diseases was collected, and biomarkers related to osteoporosis were measured. Univariable and multivariable ordinal logistic regression analyses were performed to identify variables significantly associated with BMD category. Additional analyses evaluated the associations between an identified clinical variable and biomarkers and psychiatric symptoms that may be related to osteoporosis. Results Seventy‐one patients (28 normal BMD, 17 osteopenia, and 26 osteoporosis) participated in the study. The multivariable ordinal logistic analysis showed that the duration of untreated psychosis (DUP) was a risk factor (odds ratio = 0.77, 95% confidence interval: 0.63–0.91, p = 0.006), adjusting for the major confounders of sex and age. Additional analysis showed significant differences in BPRS, BPRS Negative Symptom score, and the Cu/Zn ratio between the short‐DUP group (DUP ≤ 1 year) and the long‐DUP group (DUP > 1 year). Conclusion The DUP may affect BMD in long‐term psychiatric inpatients, presumably partly through increased severity of negative symptoms and micronutrient abnormalities. Shortening the untreated period might reduce the risk of osteoporosis.

Abstract Although spikelet-related traits such as size of anther, spikelet, style, and stigma are associated with sexual reproduction in grasses, no QTLs have been reported in sorghum. Additionally, there are only a few reports on sorghum QTLs related to grain size, such as grain length, width, and thickness. In this study, we performed QTL analyses of nine spikelet-related traits (length of sessile spikelet, pedicellate spikelet, pedicel, anther, style, and stigma; width of sessile spikelet and stigma; and stigma pigmentation) and six grain-related traits (length, width, thickness, length/width ratio, length/thickness ratio, and width/thickness ratio) using sorghum recombinant inbred lines. We identified 36 and 7 QTLs for spikelet-related traits and grain-related traits, respectively, and found that most sorghum spikelet organ length- and width-related traits were partially controlled by the dwarf genes Dw1 and Dw3. Conversely, we found that these Dw genes were not strongly involved in the regulation of grain size. The QTLs identified in this study aid in understanding the genetic basis of spikelet- and grain-related traits in sorghum.

Abstract Background OryzaGenome ( http://viewer.shigen.info/oryzagenome21detail/index.xhtml ), a feature within Oryzabase ( https://shigen.nig.ac.jp/rice/oryzabase/ ), is a genomic database for wild Oryza species that provides comparative and evolutionary genomics approaches for the rice research community. Results Here we release OryzaGenome2.1, the first major update of OryzaGenome. The main feature in this version is the inclusion of newly sequenced genotypes and their meta-information, giving a total of 217 accessions of 19 wild Oryza species (O. rufipogon, O. barthii, O. longistaminata, O. meridionalis, O. glumaepatula, O. punctata, O. minuta, O. officinalis, O. rhizomatis, O. eichingeri, O. latifolia, O. alta, O. grandiglumis, O. australiensis, O. brachyantha, O. granulata, O. meyeriana, O. ridleyi, and O. longiglumis). These 19 wild species belong to 9 genome types (AA, BB, CC, BBCC, CCDD, EE, FF, GG, and HHJJ), representing wide genomic diversity in the genus. Using the genotype information, we analyzed the genome diversity of Oryza species. Other features of OryzaGenome facilitate the use of information on single nucleotide polymorphisms (SNPs) between O. sativa and its wild progenitor O. rufipogon in rice research, including breeding as well as basic science. For example, we provide Variant Call Format (VCF) files for genome-wide SNPs of 33 O. rufipogon accessions against the O. sativa reference genome, IRGSP1.0. In addition, we provide a new SNP Effect Table function, allowing users to identify SNPs or small insertion/deletion polymorphisms in the 33 O. rufipogon accessions and to search for the effect of these polymorphisms on protein function if they reside in the coding region (e.g., are missense or nonsense mutations). Furthermore, the SNP Viewer for 446 O. rufipogon accessions was updated by implementing new tracks for possible selective sweep regions and highly mutated regions that were potentially exposed to selective pressures during the process of domestication. Conclusion OryzaGenome2.1 focuses on comparative genomic analysis of diverse wild Oryza accessions collected around the world and on the development of resources to speed up the identification of critical trait-related genes, especially from O. rufipogon. It aims to promote the use of genotype information from wild accessions in rice breeding and potential future crop improvements. Diverse genotypes will be a key resource for evolutionary studies in Oryza, including polyploid biology.

In sorghum [Sorghum bicolor (L.) Moench], hybrid cultivars for the biofuel industry are desired. Along with selection based on testcross performance, evaluation of the breeding population per se is also important for the success of hybrid breeding. In addition to additive genetic effects, non-additive (i.e., dominance and epistatic) effects are expected to contribute to the performance of early generations. Unfortunately, studies on early generations in sorghum breeding programs are limited. In this study, we analyzed a breeding population for bioenergy sorghum, which was previously developed based on testcross performance, to compare genomic selection models both trained on and evaluated for the per se performance of the 3rd generation S0 individuals. Of over 200 ancestral inbred accessions in the base population, only 13 founders contributed to the 3rd generation as progenitors. Compared to the founders, the performances of the population per se were improved for target traits. The total genetic variance within the S0 generation progenies themselves for all traits was mainly additive, although non-additive variances contributed to each trait to some extent. For genomic selection, linear regression models explicitly considering all genetic components showed a higher predictive ability than other linear and non-linear models. Although the number and effect distribution of underlying loci was different among the traits, the influence of priors for marker effects was relatively small. These results indicate the importance of considering non-additive effects for dissecting the genetic architecture of early breeding generations and predicting the performance per se.

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.

The characterization of genomes with great detail offered by the modern genotyping platforms have opened a venue for accurately predicting the genotype-by-environment interaction (GE) effects of untested genotypes in different environmental conditions. Already developed statistical models have shown the advantages of including the GE interaction component in the prediction context using molecular markers, pedigree, or both. In order to leverage the family information of highly structured populations when pedigree data is not available, we developed a model that uses the family membership instead. The proposed model extends the reaction norm model by including the interaction between families and environments (FE). A representative fraction of a soybean Nested Association Mapping population (16,187 grain yield records) comprising 38 bi-parental families (1358 genotypes) observed in 18 environments (2011, 2012, and 2013) was used to contrast the proposed model with three conventional prediction models. Two cross-validation scenarios (prediction of tested [CV2] and untested [CV1] genotypes) with a twofold design (50% for training and testing sets) were used for mimicking prediction situations that breeders face in fields. Results showed that the family factor in interaction with environments explains a sizable amount of the phenotypic variability. This helped to improve the predictive ability with respect to the main effects model (GBLUP) around 41% (CV2) and 49% (CV1), and about 17% with respect to the conventional reaction norm model. The inclusion of the FE term not only improved the global results but also significantly increased the prediction accuracy of those environments where the conventional models showed a very poor performance. These results show the importance of taking into consideration the family structure existing in breeding programs for improving the selection strategies in multi-parental populations.

Abstract Background Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes. Results Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa), depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. Conclusion Through this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from HTP data.

Abstract Background Japan faces the most elderly society in the world, and the Japanese government has launched an unprecedented health plan to reinforce home care medicine and increase the number of home care physicians, which means that an understanding of future needs for geriatric home care is vital. However, little is known about the future need for home care physicians. We attempted to estimate the basic need for home care physicians from 2020 to 2060. Methods Our estimation is based on modification of major health work force analysis methods using previously reported official data. Two models were developed to estimate the necessary number of full-time equivalent (FTE) home care physicians: one based on home care patient mortality, the other using physician-to-patient ratio, working with estimated numbers of home and nursing home deaths from 2020 to 2060. Moreover, the final process considered and adjusted for future changes in the proportion of patients dying at home. Lastly, we converted estimated FTE physicians to an estimated head count. Results Results were concordant between our two models. In every instance, there was overlap of high- and low-estimations between the mortality method and the physician-to-patient method, and the estimates show highly similar patterns. Furthermore, our estimation is supported by the current number of physicians, which was calculated using a different method. Approximately 1.7 times (1.6 by head count) the current number of FTE home care physicians will be needed in Japan in the late 2030’s, peaking at 33,500 FTE (71,500 head count). However, the need for home care physicians is anticipated to begin decreasing by 2040. Conclusion The results indicate that the importance of home care physicians will rise with the growing elderly population, and that improvements in home care could partially suppress future need for physicians. After the late 2030’s, the supply can be reduced gradually, accounting for the decreasing total number of deaths after 2040. In order to provide sufficient home care and terminal care at home, increasing the number of home care physicians is indispensable. However, the unregulated supply of home care physicians will require careful attention in the future.

“Genomics-assisted breeding”, which utilizes genomics-based methods, e.g., genome-wide association study (GWAS) and genomic selection (GS), has been attracting attention, especially in the field of fruit breeding. Low-cost genotyping technologies that support genome-assisted breeding have already been established. However, efficient collection of large amounts of high-quality phenotypic data is essential for the success of such breeding. Most of the fruit quality traits have been sensorily and visually evaluated by professional breeders. However, the fruit morphological features that serve as the basis for such sensory and visual judgments are unclear. This makes it difficult to collect efficient phenotypic data on fruit quality traits using image analysis. In this study, we developed a method to automatically measure the morphological features of citrus fruits by the image analysis of cross-sectional images of citrus fruits. We applied explainable machine learning methods and Bayesian networks to determine the relationship between fruit morphological features and two sensorily evaluated fruit quality traits: easiness of peeling (Peeling) and fruit hardness (FruH). In each of all the methods applied in this study, the degradation area of the central core of the fruit was significantly and directly associated with both Peeling and FruH, while the seed area was significantly and directly related to FruH alone. The degradation area of albedo and the area of flavedo were also significantly and directly related to Peeling and FruH, respectively, except in one or two methods. These results suggest that an approach that combines explainable machine learning methods, Bayesian networks, and image analysis can be effective in dissecting the experienced sense of a breeder. In breeding programs, collecting fruit images and efficiently measuring and documenting fruit morphological features that are related to fruit quality traits may increase the size of data for the analysis and improvement of the accuracy of GWAS and GS on the quality traits of the citrus fruits.

Recent reports indicate that respiratory infectious diseases were suppressed during the novel coronavirus disease-2019 (COVID-19) pandemic. COVID-19 led to behavioral changes aimed to control droplet transmission or contact transmission. In this study, we examined the incidence of common infectious diseases in Japan during the COVID-19 pandemic. COVID-19 data were extracted from the national data based on the National Epidemiological Surveillance of Infectious Diseases (NESID). Common infectious diseases were selected from notifiable infectious diseases under the NESID. The epidemic activity of the diseases during 2015–2020 was evaluated based on the Infectious Disease Weekly Reports published by the National Institute of Infectious Diseases. Each disease was then categorized according to the route of transmission. Many Japanese people had adopted hygienic activities, such as wearing masks and hand washing, even before the COVID-19 pandemic. We examined the correlation between the time-series of disease counts of common infectious diseases and COVID-19 over time using cross-correlation analysis. The weekly number of cases of measles, rotavirus, and several infections transmitted by droplet spread, was negatively correlated with the weekly number of cases of COVID-19 for up to 20 weeks in the past. According to the difference-in-differences analysis, the activity of influenza and rubella was significantly lower starting from the second week in 2020 than that in 2015–2019. Only legionellosis was more frequent throughout the year than in 2015–2019. Lower activity was also observed in some contact transmitted, airborne-transmitted, and fecal-oral transmitted diseases. However, carbapenem-resistant Enterobacteriaceae, exanthema subitum, showed the same trend as that over the previous 5 years. In conclusion, our study shows that public health interventions for the COVID-19 pandemic may have effectively prevented the transmission of most droplet-transmitted diseases and those transmitted through other routes.

Abstract 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.

Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.

To enrich carotenoids, especially β-cryptoxanthin, in juice sac tissues of fruits via molecular breeding in citrus, allele mining was utilized to dissect allelic variation of carotenoid metabolic genes and identify an optimum allele on the target loci characterized by expression quantitative trait (eQTL) analysis. SNPs of target carotenoid metabolic genes in 13 founders of the Japanese citrus breeding population were explored using the SureSelect target enrichment method. An independent allele was determined based on the presence or absence of reliable SNPs, using trio analysis to confirm inheritability between parent and offspring. Among the 13 founders, there were 7 PSY alleles, 7 HYb alleles, 11 ZEP alleles, 5 NCED alleles, and 4 alleles for the eQTL that control the transcription levels of PDS and ZDS among the ancestral species, indicating that some founders acquired those alleles from them. The carotenoid composition data of 263 breeding pedigrees in juice sac tissues revealed that the phenotypic variance of carotenoid composition was similar to that in the 13 founders, whereas the mean of total carotenoid content increased. This increase in total carotenoid content correlated with the increase in either or both β-cryptoxanthin and violaxanthin in juice sac tissues. Bayesian statistical analysis between allelic composition of target genes and carotenoid composition in 263 breeding pedigrees indicated that PSY-a and ZEP-e alleles at PSY and ZEP loci had strong positive effects on increasing the total carotenoid content, including β-cryptoxanthin and violaxanthin, in juice sac tissues. Moreover, the pyramiding of these alleles also increased the β-cryptoxanthin content. Interestingly, the offset interaction between the alleles with increasing and decreasing effects on carotenoid content and the epistatic interaction among carotenoid metabolic genes were observed and these interactions complexed carotenoid profiles in breeding population. These results revealed that allele composition would highly influence the carotenoid composition in citrus fruits. The allelic genotype information for the examined carotenoid metabolic genes in major citrus varieties and the trio-tagged SNPs to discriminate the optimum alleles (PSY-a and ZEP-e) from the rest would promise citrus breeders carotenoid enrichment in fruit via molecular breeding.

Accurate prediction of heading date under various environmental conditions is expected to facilitate the decision-making process in cultivation management and the breeding process of new cultivars adaptable to the environment. Days to heading (DTH) is a complex trait known to be controlled by multiple genes and genotype-by-environment interactions. Crop growth models (CGMs) have been widely used to predict the phenological development of a plant in an environment; however, they usually require substantial experimental data to calibrate the parameters of the model. The parameters are mostly genotype-specific and are thus usually estimated separately for each cultivar. We propose an integrated approach that links genotype marker data with the developmental genotype-specific parameters of CGMs with a machine learning model, and allows heading date prediction of a new genotype in a new environment. To estimate the parameters, we implemented a Bayesian approach with the advanced Markov chain Monte-Carlo algorithm called the differential evolution adaptive metropolis and conducted the estimation using a large amount of data on heading date and environmental variables. The data comprised sowing and heading dates of 112 cultivars/lines tested at 7 locations for 14 years and the corresponding environmental variables (day length and daily temperature). We compared the predictive accuracy of DTH between the proposed approach, a CGM, and a single machine learning model. The results showed that the extreme learning machine (one of the implemented machine learning models) was superior to the CGM for the prediction of a tested genotype in a tested location. The proposed approach outperformed the machine learning method in the prediction of an untested genotype in an untested location. We also evaluated the potential of the proposed approach in the prediction of the distribution of DTH in 103 F2 segregation populations derived from crosses between a common parent, Koshihikari, and 103 cultivars/lines. The results showed a high correlation coefficient (ca. 0.8) of the 10, 50, and 90th percentiles of the observed and predicted distribution of DTH. In this study, the integration of a machine learning model and a CGM was better able to predict the heading date of a new rice cultivar in an untested potential environment.

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.

Difficulty in detecting rare variants is one of the problems in conventional genome-wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set method named "RAINBOW" and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes. We prepared 100 datasets of simulated phenotypic data and real marker genotype data of Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. Our proposed method was shown to be superior to these in three aspects: (1) controlling false positives; (2) in detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset; and (3) it showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, primarily when the causal variants were located very close to each other, and the directions of their effects were opposite. By using the SNP-set approach as in this study, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as an R package named "RAINBOWR" and is available from CRAN (https://cran.r-project.org/web/packages/RAINBOWR/index.html) and GitHub (https://github.com/KosukeHamazaki/RAINBOWR).

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.

Water lilies (Nymphaea spp.) have diverse floral morphologies. Water lilies are not only commonly used as ornamental plants, but they are also important for understanding the diversification of basal angiosperms. Although the diversity in floral morphology of water lily provides useful information for evolutionary biology, horticulture, and horticultural science, it is difficult to describe and analyze the three-dimensional morphology of flowers. In this study, we propose a method to describe the floral morphology of water lily using a three-dimensional theoretical morphological model. The theoretical model was constructed based on three components, i.e., (1) the gradual change in size of floral organs, (2) spiral phyllotaxis, and (3) the interpolation of elevation angles, which were integrated into the model. We generated three-dimensional representation of water lily flowers and visualized theoretical morphospaces by varying each morphological parameter. The theoretical morphospace is a mathematical space of morphological spectrum generated by a theoretical morphological model. These morphospaces seems to display the large part of morphological variations of water lily. We measured morphological parameters of real flowers based on our theoretical model and display the occupation pattern of morphological parameters. We also surveyed the relation between morphological parameters and flower shape descriptions found in a catalog. In some parameters, we found breeders' description can link to our morphological model. In addition, the relationship between the global features of floral morphology and the parameters of the theoretical model was calculated with flower silhouettes simulated with a range of parameter values and the global features of the silhouette. We used two simple indices to assess the global morphological features, which were calculated with the convex hull. The results indicated that our method can effectively provide an objective and quantitative overview of the diversity in the floral morphology of water lily.

Oryza sativa L. ‘Takanari’ is one of the most productive indica cultivars [1,2]. Reciprocal chromosome segment substitution lines (CSSLs) derived from a cross between ‘Koshihikari’ and ‘Takanari’ are useful tools for the detection and precise mapping of target quantitative trait loci (QTL) in ‘Takanari’. Although the available Os-Nipponbare-Reference-IRGSP-1.0 reference genome is available and useful for evaluating genetic diversity among japonica cultivars, it is not always useful for evaluating genetic diversity harbored by indica cultivars such as ‘Takanari’. To reveal sequence variants in ‘Takanari’ and to exploit these variants in rice breeding programs, the whole genome of ‘Takanari’ was sequenced using a combination of Illumina HiSeq X Ten (20,983,495 reads and %GC 43) and PacBio (2,847,220 high-quality subreads). NGS data obtained have been deposited in the DNA Data Bank of Japan (DDBJ) under accession number DRA007557. Keywords: Oryza sativa L., Rice, Next-generation sequencing, Long-read sequencing

Abstract Novel genomics-based approaches such as genome-wide association studies (GWAS) and genomic selection (GS) are expected to be useful in fruit tree breeding, which requires much time from the cross to the release of a cultivar because of the long generation time. In this study, a citrus parental population (111 varieties) and a breeding population (676 individuals from 35 full-sib families) were genotyped for 1,841 single nucleotide polymorphisms (SNPs) and phenotyped for 17 fruit quality traits. GWAS power and prediction accuracy were increased by combining the parental and breeding populations. A multi-kernel model considering both additive and dominance effects improved prediction accuracy for acidity and juiciness, implying that the effects of both types are important for these traits. Genomic best linear unbiased prediction (GBLUP) with linear ridge kernel regression (RR) was more robust and accurate than GBLUP with non-linear Gaussian kernel regression (GAUSS) in the tails of the phenotypic distribution. The results of this study suggest that both GWAS and GS are effective for genetic improvement of citrus fruit traits. Furthermore, the data collected from breeding populations are beneficial for increasing the detection power of GWAS and the prediction accuracy of GS.

Seed shape is an important agronomic trait with continuous variation among genotypes. Therefore, the quantitative evaluation of this variation is highly important. Among geometric morphometrics methods, elliptic Fourier analysis and semi-landmark analysis are often used for the quantification of biological shape variations. Elliptic Fourier analysis is an approximation method to treat contours as a waveform. Semi-landmark analysis is a method of superimposed points in which the differences of multiple contour positions are minimized. However, no detailed comparison of these methods has been undertaken. Moreover, these shape descriptors vary when the scale and direction of the contour and the starting point of the contour trace change. Thus, these methods should be compared with respect to the standardization of the scale and direction of the contour and the starting point of the contour trace. In the present study, we evaluated seed shape variations in a sorghum (Sorghum bicolor Moench) germplasm collection to analyze the association between shape variations and genome-wide single-nucleotide polymorphisms by genomic prediction (GP) and genome-wide association studies (GWAS). In our analysis, we used all possible combinations of three shape description methods and eight standardization procedures for the scale and direction of the contour as well as the starting point of the contour trace; these combinations were compared in terms of GP accuracy and the GWAS results. We compared the shape description methods (elliptic Fourier descriptors and the coordinates of superposed pseudo-landmark points) and found that principal component analysis of their quantitative descriptors yielded similar results. Different scaling and direction standardization procedures caused differences in the principal component scores, average shape, and the results of GP and GWAS.