The National Agriculture and Food Research Organization (農業・食品産業技術総合研究機構, Nōgyō Shokuhin Sangyō Gijutsu Sōgō Kenkyū Kikō, NARO) is a Japanese research facility headquartered in Tsukuba Science City, Ibaraki, and the workforce is located in Tsukuba and in several cities and towns throughout Japan. The organization is dedicated to scientific research related to Agriculture. It became a new legal body of Independent Administrative Institution in 2001 originally as National Agricultural Research Organization, remaining under the Ministry of Agriculture, Forestry and Fisheries (MAFF).
Tomato spotted wilt virus (TSWV) is distributed all over Japan and globally, seriously damaging infected plants. A resistance-conferring gene, Tsw, that controls the yellow spotted wilt disease in bell pepper (Capsicum annuum) plants caused by TSWV infection in Japan, is now commercially available. In this study, we isolated for the first time in Japan, a resistance-breaking isolate, TSWV-JRB, from bell pepper plants harboring Tsw. The virus overcame the resistance conferred by Tsw in its heterozygous and homozygous configurations. The host range or virulence of TSWV-JRB and one of the non-resistance breaking isolates from Japan did not differ, except in the plants harboring Tsw. The TSWV-JRB acquisition rates and transmission rates of the thrip pests Frankliniella occidentalis and F. intonsa were 93
Managing near-term risks from human–carnivore encounters has traditionally relied on mechanistic models that require extensive real-time data on causal factors, offering limited support for operational decision-making when short-term predictions are needed. We developed a decision-support system that predicts monthly bear sightings from the start of each month by exploiting temporal dynamics without mechanistic assumptions. The ensemble integrates three components: sequential estimation via non-stationary Poisson processes, seasonal baselines with ratio corrections, and rule-based transitions as data accumulate. Applied to Asiatic black bear (Ursus thibetanus) sighting records from two Japanese regions differing 18-fold in encounter frequency (maximum monthly counts: 83 vs. 1490) and with contrasting seasonal peaks, the ensemble achieved correlations ≥0.75 between predicted and observed totals from day 1, rising to ≥0.96 by day 20, and substantially outperformed a null model. After controlling for baseline spatial and temporal risk, we detected localized short-term clustering: prior sightings increased encounter probability within 500 m for up to 3 days. This system demonstrates that temporal dynamics alone can approach practical prediction limits for wildlife encounters without any bear-specific covariates or detailed environmental predictors, and can be directly adapted to other wildlife conflicts and short-term environmental hazards wherever incident time series are available. By quantifying when (daily risk levels) and where (localized hotspots) encounters are most likely, it provides wildlife managers and residents with an immediately implementable tool for issuing targeted warnings, deploying patrols, and reducing human injuries in regions experiencing increasing human-wildlife conflict.
To exploit allelic variation in Hordeum vulgare subsp. spontaneum, the Wild Barley Diversity Collection was subjected to paired-end Illumina sequencing at ∼9 × depth and evaluated for several agronomic traits. We discovered 240.2 million single nucleotide polymorphisms (SNPs) after alignment to the Morex V3 assembly and 24.4 million short (1 to 50 bp) insertions and deletions. A genome-wide association study of lemma color identified one marker-trait association (MTA) on chromosome 1H close to HvBlp, the cloned gene controlling black lemma. Four MTAs were identified for seedling stem rust resistance, including 2 novel loci on chromosomes 1H and 6H and one co-locating to the complex RMRL1-RMRL2 locus on 5H. The whole-genome sequence data described herein will facilitate the identification and utilization of new alleles for barley improvement.
Intermediate omics traits, which mediate the effects of genetic variation on phenotypic traits, are increasingly recognized as valuable components of genetic evaluation. In particular, rhizosphere microbiota play a crucial role in plant health and productivity; however, their complex interactions with host genetics remain challenging to model. Although two-step modeling frameworks have been proposed to integrate intermediate omics traits into phenotype prediction, existing approaches do not incorporate nonlinear relationships between different omics layers. To address this, we have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omics-omics nonlinearities. The first step is to predict meta-metabolome traits from genetic and microbial features, thus effectively isolating them from the environmental noise. In this process, intermediate "proxy" omics traits are generated as general biological information to provide robust models. The second step utilizes this "proxy" to enhance the accuracy of the phenotype prediction. We compared a linear mixed model (Best Linear Unbiased Prediction, BLUP) and a nonlinear model (Random Forest, RF) at each step, as demonstrated through simulations and empirical analysis of a multi-omics soybean dataset in which nonlinear modeling captures intricate omics interactions. Notably, our approach enables phenotype prediction without requiring the original meta-metabolome data used in model training, thereby reducing reliance on costly omics measurements. This framework integrates intermediate omics traits into genomic prediction to improve prediction accuracy and provide solutions for deeper insights into plant-microbiome interactions.
The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Thematic Assessment Report on Invasive Alien Species and Their Control presents a “conceptual diagram of management-invasion continuum”, introducing a versatile framework to support decision-making on the management of biological invasions. Drawing on an extensive synthesis of current knowledge, this IPBES invasion-management framework has been developed to broaden the scope of existing invasion curves—which primarily overlay generic management objectives onto a sigmoid curve depicting the expansion of the affected area over time—and to illustrate the applicability of the concept of effective management at different stages of the biological invasion process. To introduce the IPBES invasion-management framework to a wider audience, this paper explains the features of the framework and defines the invasion-stage-based management approaches and the potential outcomes envisaged therein. Reflecting the currently limited management options for biological invasions in marine and other connected-water systems, unlike in terrestrial and closed-water systems, the IPBES invasion-management framework clearly distinguishes between these two groups of systems. For each, it presents management approaches including three key factors that decision-makers should consider concurrently: management objectives, targets, and actions. This framework supports informed decision-making in the management of biological invasions in all ecosystems.
