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Grain shape improvement, which determines grain yield, quality traits and commercial value, is an extremely important aspect of rice breeding. Grain size is controlled by multiple genes, and Maker Assistant Selection (MAS) breeding is effective for breeders in developing stable and efficient markers to aggregate these genes in order to speed up the selection of new lines with desirable traits during the breeding process. In this study, functional markers were developed based on the sequence differences of five grain-shaped genes (GL7, GW6a, GS6, GW5 and TGW6) between the long-grain japonica rice variety Zhendao and the indica-japonica restorer R2027. We then constructed a population of recombinant inbred lines (RILs) based on their cross. The newly designed functional markers were used to genotype grain-size genes, and a genetic effect analysis was conducted to screen high-quality long-grain restorers. Our results reveal diverse effects of different genes on grain size, and the five genotypes were distributed in the 36 selected BC1F8 lines. Specifically, gw5 positively regulates grain width and 1000-grain weight, gl7 and gs6 positively regulate grain length but negatively regulate grain width and 1000-grain weight, tgw6 positively regulates grain length and gw6a positively regulates 1000-grain weight. The most outstanding outcome is that 5 of the 36 lines achieved in this study showing an excellent performance of long grain and yield characters are ideal materials not only for studying the interaction and genetic effects between polygenes but also as restorers or donors for dominant genes in indica-japonica hybrid rice breeding. [ABSTRACT FROM AUTHOR]

• A novel and practical method to assess the feeding appetite of fish in outdoor ponds was proposed here. • The improved kinetic energy was constructed by combining motion influence maps to represent the feeding activity level of fish school. • The performance of the proposed method was evaluated through exhaustive testing. • The proposed method maintained good performance under both shadow disturbances and without shadow disturbances. Accurate quantification of fish appetite can provide effective feeding reference for aquaculture managers. Existing appetite assessment methods are focused on relatively stable indoor or net-cage farming environments, neglecting the interference of complex environmental factors in outdoor ponds on fish appetite assessment. Within the process of gathering video data for the feeding of ponds, the emergence of shaded area due to the implications of illumination and camera positioning angles poses an obstacle for subsequent handling. Therefore, from the perspective of actual production, according to the feeding behavior characteristics of fish in the feeding area, this study proposed an anti-shadow disturbance method for assessing fish appetite in outdoor ponds. Firstly, the motion features of the whole fish school were extracted using a grid, and the spatial relationship of the fish school was coupled to construct a modified motion influence map that represented the interaction characteristics between individuals of the fish school. Then, the advanced kinetic energy model based on the modified motion influence diagram was used to extract and quantify the spatial characteristics of fish behavior. Finally, the customized recurrent convolutional neural network (RCNN) was used to determine the fish's appetite level. Through comprehensive validation using a detailed dataset, it was observed that the proposed method achieved an average accuracy of 96.94 ± 0.33 % on multiple datasets with shadow disturbance. This performance surpassed that of current mainstream methods (with the highest accuracy at 84.30 ± 1.83). Furthermore, when compared to datasets without shadow disturbance, this method demonstrated only a slight decrease of 0.73 % in accuracy, outperforming current mainstream methods (with the lowest accuracy at 0.97 %). These results highlighted the excellent anti-shadow disturbance capabilities of the proposed method. [ABSTRACT FROM AUTHOR]

Key message: OsCOL5, an ortholog ofArabidopsis COL5, is involved in photoperiodic flowering and enhances rice yield through modulation ofGhd7andEhd2and interactions withOsELF3-1andOsELF3-2. Heading date, also known as flowering time, plays a crucial role in determining the adaptability and yield potential of rice (Oryza sativa L.). CONSTANS (CO)-like is one of the most critical flowering-associated gene families, members of which are evolutionarily conserved. Here, we report the molecular functional characterization of OsCOL5, an ortholog of Arabidopsis COL5, which is involved in photoperiodic flowering and influences rice yield. Structural analysis revealed that OsCOL5 is a typical member of CO-like family, containing two B-box domains and one CCT domain. Rice plants overexpressing OsCOL5 showed delayed heading and increases in plant height, main spike number, total grain number per plant, and yield per plant under both long-day (LD) and short-day (SD) conditions. Gene expression analysis indicated that OsCOL5 was primarily expressed in the leaves and stems with a diurnal rhythm expression pattern. RT-qPCR analysis of heading date genes showed that OsCOL5 suppressed flowering by up-regulating Ghd7 and down-regulating Ehd2, consequently reducing the expression of Ehd1, Hd3a, RFT1, OsMADS14, and OsMADS15. Yeast two-hybrid experiments showed direct interactions of OsCOL5 with OsELF3-1 and OsELF3-2. Further verification showed specific interactions between the zinc finger/B-box domain of OsCOL5 and the middle region of OsELF3-1 and OsELF3-2. Yeast one-hybrid assays revealed that OsCOL5 may bind to the CCACA motif. The results suggest that OsCOL5 functions as a floral repressor, playing a vital role in rice’s photoperiodic flowering regulation. This gene shows potential in breeding programs aimed at improving rice yield by influencing the timing of flowering, which directly impacts crop productivity. [ABSTRACT FROM AUTHOR]

Key Message: TAC1is involved in photoperiodic and gravitropic responses to modulate rice dynamic plant architecture likely by affecting endogenous auxin distribution, which could explainTAC1widespread distribution in indica rice. Plants experience a changing environment throughout their growth, which requires dynamic adjustments of plant architecture in response to these environmental cues. Our previous study demonstrated that Tiller Angle Control 1 (TAC1) modulates dynamic changes in plant architecture in rice; however, the underlying regulatory mechanisms remain largely unknown. In this study, we show that TAC1 regulates plant architecture in an expression dose-dependent manner, is highly expressed in stems, and exhibits dynamic expression in tiller bases during the growth period. Photoperiodic treatments revealed that TAC1 expression shows circadian rhythm and is more abundant during the dark period than during the light period and under short-day conditions than under long-day conditions. Therefore, it contributes to dynamic plant architecture under long-day conditions and loose plant architecture under short-day conditions. Gravity treatments showed that TAC1 is induced by gravistimulation and negatively regulates shoot gravitropism, likely by affecting auxin distribution. Notably, the tested indica rice containing TAC1 displayed dynamic plant architecture under natural long-day conditions, likely explaining the widespread distribution of TAC1 in indica rice. Our results provide new insights into TAC1-mediated regulatory mechanisms for dynamic changes in rice plant architecture. [ABSTRACT FROM AUTHOR]

Fish phenotyping serves as a cornerstone for refined management and sustainable development in industrialized aquaculture. High-precision instance segmentation for high-density fish is a key step in obtaining fish phenotypic data; however, it remains challenging in practical production. Given this, an innovative instance segmentation method that synergistically integrates fish body surface features with motion features was developed in this study to tackle the complex issue of high-density fish segmentation in real production effectively. In this method, the Gunnar Farnebäck optical flow is leveraged to express the motion features of fish quantitatively, following which a convolutional neural network of different depths is used to extract the depth feature information of the optical flow images. Simultaneously, the body features output by the backbone network are fused with the motion features. Subsequently, the final fish instance segmentation model was constructed based on SOLOv2. The results of experiments conducted on a self-built dataset of tiger puffer (Takifugu rubripes) revealed that integrating the body surface and motion features resulted in an enhancement of over 12% in the segmentation accuracy of the fish (APm = 48.0%, APl = 84.5%, AP = 84.5%) compared with the baseline (SOLOv2). Compared with other mainstream models of instance segmentation, the AP of the proposed method was improved by 8.3 to 12.8%. Thus, the proposed method can provide technical support for fish phenotypic data in industrialized aquaculture. [ABSTRACT FROM AUTHOR]

Plant architecture is dynamic as plants develop. Although many genes associated with specific plant architecture components have been identified in rice, genes related to underlying dynamic changes in plant architecture remain largely unknown. Here, we identified two highly similar recombinant inbred lines (RILs) with different plant architecture: RIL-Dynamic (D) and RIL-Compact (C). The dynamic plant architecture of RIL-D is characterized by 'loosetiller angle (tillering stage)–compact (heading stage)–loosecurved stem (maturing stage)' under natural long-day (NLD) conditions, and 'loosetiller angle (tillering and heading stages)–loosetiller angle and curved stem (maturing stage)' under natural short-day (NSD) conditions, while RIL-C exhibits a compact plant architecture both under NLD and NSD conditions throughout growth. The candidate locus was mapped to the chromosome 9 tail via the rice 8K chip assay and map-based cloning. Sequencing, complementary tests, and gene knockout tests demonstrated that Tiller Angle Control 1 (TAC1) is responsible for dynamic plant architecture in RIL-D. Moreover, TAC1 positively regulates loose plant architecture, and high TAC1 expression cannot influence the expression of tested tiller-angle-related genes. Our results reveal that TAC1 is necessary for the dynamic changes in plant architecture, which can guide improvements in plant architecture during the modern super rice breeding. [ABSTRACT FROM AUTHOR]

• A distribution calibration method based on linear fitting was proposed. • Achieved accurate classification of few-shot classes without complicated generative models. • The feature of adherent fish and the hyper-parameters of the proposed method were analyzed. Fish counting is critical to the success of fish farming since it serves as the foundation for evaluating fish health and growth, rationalizing feed and water quality management, and so on. To address the issue of unbalance distribution and a limited number of samples in the actual fish counting process, a distribution calibration method based on linear fitting was proposed, achieving accurate classification of few-shot or even no-shot classes without complicated generative models and parameter settings. Firstly, seven features were extracted from the adherent fish, followed by a linear fit of the features to the number of adherent fish; subsequently, the number of adherent fish is considered a class, and the mean and variance of the few-shot classes are calibrated based on the fitted linear relationship; following that, adequate training data for the classifiers are generated by extracting samples from the calibration distribution, and the machine learning classifiers model are trained to accurately classify the number of adherent fish. By testing and analyzing the datasets with 4 sets of long-tailed distributions and nine machine learning classifiers, the P , R , F1 scores of Support Vector Classification(SVC) classifier with Radial Basis Function(RBF) achieved 85.6% (±0.4%), 85.5% (±0.6%), and 85.5% (±0.5%), respectively, while R 2 was 0.924 (±0.003), with an average counting accuracy of over 96.2% for each class. In comparison to other classifiers, the SVC classifier demonstrated superior precision and stability. [ABSTRACT FROM AUTHOR]

Formation of the pollen wall, which is mainly composed of lipid substances secreted by tapetal cells, is important to ensure pollen development in rice. Although several regulatory factors related to lipid biosynthesis during pollen wall formation have been identified in rice, the molecular mechanisms controlling lipid biosynthesis are unclear. In this study, we isolated the male-sterile rice mutant oslddt1 (leaked and delayed degraded tapetum 1). oslddt1 plants show complete pollen abortion resulting from delayed degradation of the tapetum and blocked formation of Ubisch bodies and pollen walls. OsLDDT1 (LOC_Os03g02170) encodes a DUF726 containing protein of unknown function with highly conserved transmembrane and α/β Hydrolase domains. OsLDDT1 localizes to the endoplasmic reticulum and the gene is highly expressed in rice panicles. Genes involved in regulating fatty acid synthesis and formation of sporopollenin and pollen exine during anther development showed significantly different expression patterns in oslddt1 plants. Interestingly, the wax and cutin contents in mature oslddt1–1 anthers were decreased by 74.07 % and 72.22 % compared to WT, indicating that OsLDDT1 is involved in fatty acid synthesis and affects formation of the anther epidermis. Our results provide as deeper understanding of the role of OsLDDT1 in regulating male sterility and also provide materials for hybrid rice breeding. • OsLDDT1 encodes a DUF726 family protein, which regulates tapetum PCD and pollen formation. • Alternative splicing mutation of OsLDDT1 cause delayed tapetum PCD and pollen degradation. • OsLDDT1 affects fatty acid synthesis by patterning of Ubisch bodies and pollen exine [ABSTRACT FROM AUTHOR]

Chloroplast is an important organelle for photosynthesis and numerous essential metabolic processes, thus ensuring plant fitness or survival. Although many genes involved in chloroplast development have been identified, mechanisms underlying such development are not fully understood. Here, we isolated and characterized the stripe3 (st3) mutant which exhibited white-striped leaves with reduced chlorophyll content and abnormal chloroplast development during the seedling stage, but gradually produced nearly normal green leaves as it developed. Map-based cloning and transgenic tests demonstrated that a splicing mutation in ST3 , encoding a human deoxynucleoside triphosphate triphosphohydrolase (dNTPase) SAMHD1 homolog, was responsible for st3 phenotypes. ST3 is highly expressed in the third leaf at three-leaf stage and expressed constitutively in root, stem, leaf, sheath, and panicle, and the encoded protein, OsSAMHD1, is localized to the cytoplasm. The st3 mutant showed more severe albino leaf phenotype under exogenous 1-mM dATP/dA, dCTP/dC, and dGTP/dG treatments compared with the control conditions, indicating that ST3 is involved in dNTP metabolism. This study reveals a gene associated with dNTP catabolism, and propose a model in which chloroplast development in rice is regulated by the dNTP pool, providing a potential application of these results to hybrid rice breeding. • A splicing mutation in ST3 / OsSAMHD1 leads to white-striped leaves. • ST3 is required for chloroplast development during the seedling stage. • st3 phenotypes are affected by low temperatures during maturation. • ST3 is constitutively expressed and its encoded protein is localized to cytoplasm. • ST3 is involved in dNTP metabolism to regulate rice chloroplast development. [ABSTRACT FROM AUTHOR]

• An accurate and practical on-line counting method by using low-frame-rate camera was proposed. • The kinematic model of the fingerlings for motion prediction was constructed. • The fingerlings were associated by using the kinematic model and the probability density function (PDF). Accurate estimation of fingerlings quantity is one of the most challenging aspects of aquaculture, relevant simple and efficient solutions, however, are still lacking. To address this problem, an accurate and practical on-line counting method for 2–8 cm fingerlings, which was simple to implement, computational inexpensively (without huge pre-training like that in deep learning), and suitable for five different kinds of fingerlings, based on fish kinematics characteristics was proposed in this study. First, the kinematic model of the fingerlings for motion prediction was constructed based on the analysis of their kinematics characteristics; secondly, adaptive threshold segmentation (ATS) algorithm was used to segment and detect the fingerlings; and then, the fingerlings in previous and current frames were associated and tracked by using the kinematic model and the probability density function (PDF). Following this, the new fingerlings were identified and counted in real-time. Finally, through the exhaustive test on 102 datasets (acquired with a low frame rate (10 fps)), the present method showed the average counting accuracy rate (ACAR) and standard deviation (SD) of more than 98.78% and less than 0.95%, respectively. Specifically, the ACAR of Pseudosciaena Crocea (2–3 cm) and Ctenopharyngodon idellus (3–5 cm, 6–8 cm) exceeded 99.19%, and the corresponding SD was less than 0.59%. [ABSTRACT FROM AUTHOR]