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Multi-camera systems for structure from motion (SfM) are widely deployed in many mapping applications. Existing solutions assume known rig calibration, synchronized frames among cameras, as well as overlapping field of views (FoVs). In this paper, we derive novel geometric constraints assuming minimal knowns about the multi-camera systems, to benefit low-cost and non-expert use cases where uncalibrated multi-camera systems with non-typical geometry setups present, i.e., no rig calibration, no overlapping FoVs. Assuming that these cameras are co-located and share the same motion of the platform, the proposed constraints utilize the parallelism and length proportionality of motion vectors of these co-located cameras and formulate them as translation constraints into the bundle adjustment (BA). The proposed constraints (called motion constraints) impose a first-order penalty to co-located cameras whose motion speeds and directions between frames do not match. With soft constraints, it can handle loosely synchronized frames (with an error within one second). The proposed constraints are integrated into the BA framework and experimented with different camera setups, i.e., on a group of casually co-located GoPro cameras with no rig calibration, and some with no overlapping views. Our results show that the constraints are extremely effective in improving the reconstruction and pose accuracy for ground motion images: in our self-collected open trajectories without loop closure, the proposed constraints are effective in correcting topographical errors (i.e., trajectory drifts) of the resulting models, and the dense point clouds achieve up to 11.34 m (86.12 %) of mean absolute error (MAE) improvement as compared to reference LiDAR point clouds; our results on KITTI-odometry and KITTI-360 datasets also show an improvement of up to 28.82 m (81.05 %) in terms of the root mean square error (RMSE) of absolute pose error (APE). We expect that the proposed constraints are significant not only as additional geometric constraints for image-based mobile mapping, but also will benefit the broader use of photogrammetry, since it empowers the possibility to harness the traditionally so-called low-quality stereo/multi-camera data (e.g., by non-photogrammetry citizen scientists) into improved 3D products. [ABSTRACT FROM AUTHOR]

In this paper, we present a case study that performs an unmanned aerial vehicle (UAV) based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event. Multi-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the demolition. The geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds, achieving an average distance of 12 cm and 16 cm for roof and façade respectively. We propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation (free of ground control points), reconstruction, and a coarse-to-fine 3D density change analysis. This work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented progressively. Our change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92, with consistently high precision (0.92–0.99). Volumetric changes through the demolition progress are derived from change detection and have been shown to favorably reflect the qualitative and quantitative building demolition progression. [ABSTRACT FROM AUTHOR]

Neural radiance fields (NeRF) offer the potential to benefit 3D reconstruction tasks, including aerial photogrammetry. However, the scalability and accuracy of the inferred geometry are not well‐documented for large‐scale aerial assets. We aim to provide a thorough assessment of NeRF in 3D reconstruction from aerial images and compare it with three traditional multi‐view stereo (MVS) pipelines. However, typical NeRF approaches are not designed for large‐format aerial images, which result in very high memory consumption (often cost‐prohibitive) and slow convergence when directly applied to aerial assets. Despite a few NeRF variants adopting a representation tiling scheme to increase scalability, the random ray‐sampling strategy during training still hinders its general applicability for aerial assets. To perform an effective evaluation, we propose a new scheme to scale NeRF. In addition to representation tiling, we introduce a location‐specific sampling technique as well as a multi‐camera tiling (MCT) strategy to reduce memory consumption during image loading for RAM, representation training for GPU memory and increase the convergence rate within tiles. The MCT method decomposes a large‐frame image into multiple tiled images with different camera models, allowing these small‐frame images to be fed into the training process as needed for specific locations without a loss of accuracy. This enables NeRF approaches to be applied to aerial datasets on affordable computing devices, such as regular workstations. The proposed adaptation can be implemented to adapt for scaling any existing NeRF methods. Therefore, in this paper, instead of comparing accuracy performance against different NeRF variants, we implement our method based on a representative approach, Mip‐NeRF, and compare it against three traditional photogrammetric MVS pipelines on a typical aerial dataset against lidar reference data to assess NeRF's performance. Both qualitative and quantitative results suggest that the proposed NeRF approach produces better completeness and object details than traditional approaches, although as of now, it still falls short in terms of accuracy. The codes and datasets are made publicly available at https://github.com/GDAOSU/MCT_NERF. [ABSTRACT FROM AUTHOR]

Cellulose, which can be converted into numerous industrial products, has important impacts on the global economy. It has long been known that cellulose synthesis in plants is tightly regulated by various phytohormones. However, the underlying mechanism of cellulose synthesis regulation remains elusive. Here, we show that in rice (Oryza sativa), gibberellin (GA) signals promote cellulose synthesis by relieving the interaction between SLENDER RICE1 (SLR1), a DELLA repressor of GA signaling, and NACs, the top-layer transcription factors for secondary wall formation. Mutations in GA -related genes and physiological treatments altered the transcription of CELLULOSE SYNTHASE genes (CESA s) and the cellulose level. Multiple experiments demonstrated that transcription factors NAC29/31 and MYB61 are CESA regulators in rice; NAC29/31 directly regulates MYB61 , which in turn activates CESA expression. This hierarchical regulation pathway is blocked by SLR1-NAC29/31 interactions. Based on the results of anatomical analysis and GA content examination in developing rice internodes, this signaling cascade was found to be modulated by varied endogenous GA levels and to be required for internode development. Genetic and gene expression analyses were further performed in Arabidopsis thaliana   GA -related mutants. Altogether, our findings reveal a conserved mechanism by which GA regulates secondary wall cellulose synthesis in land plants and provide a strategy for manipulating cellulose production and plant growth. [ABSTRACT FROM AUTHOR]

Synthetic biology approaches are highly sought-after to facilitate the regulation of targeted gene expression in plants for functional genomics research and crop trait improvement. To date, synthetic regulation of gene expression predominantly focuses at the transcription level via engineering of synthetic promoters and transcription factors, while pioneering examples have started to emerge for synthetic regulation of gene expression at the levels of mRNA stability, translation, and protein degradation. This review discusses recent advances in plant synthetic biology for the regulation of gene expression at multiple levels, and highlights their future directions. [ABSTRACT FROM AUTHOR]

Background: Sucrose (Suc), as the precursor molecule for rubber biosynthesis in Hevea brasiliensis, is transported via phloem-mediated long-distance transport from leaves to laticifers in trunk bark, where latex (cytoplasm of laticifers) is tapped for rubber. In our previous report, six Suc transporter (SUT) genes have been cloned in Hevea tree, among which HbSUT3 is verified to play an active role in Suc loading to the laticifers. In this study, another latex-abundant SUT isoform, HbSUT5, with expressions only inferior to HbSUT3 was characterized especially for its roles in latex production. Results: Both phylogenetic analysis and subcellular localization identify HbSUT5 as a tonoplast-localized SUT protein under the SUT4-clade (=type III). Suc uptake assay in baker's yeast reveals HbSUT5 to be a typical Suc-H+ symporter, but its high affinity for Suc (Km = 2.03 mM at pH 5.5) and the similar efficiency in transporting both Suc and maltose making it a peculiar SUT under the SUT4-clade. At the transcript level, HbSUT5 is abundantly and preferentially expressed in Hevea barks. The transcripts of HbSUT5 are conspicuously decreased both in Hevea latex and bark by two yield-stimulating treatments of tapping and ethephon, the patterns of which are contrary to HbSUT3. Under the ethephon treatment, the Suc level in latex cytosol decreases significantly, but that in latex lutoids (polydispersed vacuoles) changes little, suggesting a role of the decreased HbSUT5 expression in Suc compartmentalization in the lutoids and thus enhancing the Suc sink strength in laticifers. Conclusions: Our findings provide insights into the roles of a vacuolar sucrose transporter, HbSUT5, in Suc exchange between lutoids and cytosol in rubber-producing laticifers. [ABSTRACT FROM AUTHOR]

Key message: A switchgrass vascular tissue-specific promoter (PvPfn2) and its 5′-end serial deletions drive high levels of vascular bundle transgene expression in transgenic rice.Abstract: Constitutive promoters are widely used for crop genetic engineering, which can result in multiple off-target effects, including suboptimal growth and epigenetic gene silencing. These problems can be potentially avoided using tissue-specific promoters for targeted transgene expression. One particularly urgent need for targeted cell wall modification in bioenergy crops, such as switchgrass (Panicum virgatum L.), is the development of vasculature-active promoters to express cell wall-affective genes only in the specific tissues, i.e., xylem and phloem. From a switchgrass expression atlas we identified promoter sequence upstream of a vasculature-specific switchgrass profilin gene (PvPfn2), especially in roots, nodes and inflorescences. When the putative full-length (1715 bp) and 5′-end serial deletions of the PvPfn2 promoter (shortest was 413 bp) were used to drive the GUS reporter expression in stably transformed rice (Oryza sativa L.), strong vasculature-specificity was observed in various tissues including leaves, leaf sheaths, stems, and flowers. The promoters were active in both phloem and xylem. It is interesting to note that the promoter was active in many more tissues in the heterologous rice system than in switchgrass. Surprisingly, all four 5′-end promoter deletions, including the shortest fragment, had the same expression patterns as the full-length promoter and with no attenuation in GUS expression in rice. These results indicated that the PvPfn2 promoter variants are new tools to direct transgene expression specifically to vascular tissues in monocots. Of special interest is the very compact version of the promoter, which could be of use for vasculature-specific genetic engineering in monocots. [ABSTRACT FROM AUTHOR]

Bacterial leaf pustule (BLP), caused by Xanthornonas axonopodis pv. glycines (Xag), is a worldwide disease of soybean, particularly in warm and humid regions. To date, little is known about the underlying molecular mechanisms of BLP resistance. The only single recessive resistance gene rxp has not been functionally identified yet, even though the genotypes carrying the gene have been widely used for BLP resistance breeding. Using a linkage mapping in a recombinant inbred line (RIL) population against the Xag strain Chinese C5, we identified that quantitative trait locus (QTL) qrxp–17–2 accounted for 74.33% of the total phenotypic variations. We also identified two minor QTLs, qrxp–05–1 and qrxp–17–1, that accounted for 7.26% and 22.26% of the total phenotypic variations, respectively, for the first time. Using a genome-wide association study (GWAS) in 476 cultivars of a soybean breeding germplasm population, we identified a total of 38 quantitative trait nucleotides (QTNs) on chromosomes (Chr) 5, 7, 8, 9,15, 17, 19, and 20 under artificial infection with C5, and 34 QTNs on Chr 4, 5, 6, 9, 13, 16, 17, 18, and 20 under natural morbidity condition. Taken together, three QTLs and 11 stable QTNs were detected in both linkage mapping and GWAS analysis, and located in three genomic regions with the major genomic region containing qrxp_17_2. Real-time RT-PCR analysis of the relative expression levels of five potential candidate genes in the resistant soybean cultivar W82 following Xag treatment showed that of Glyma.17G086300, which is located in qrxp–17–2, significantly increased in W82 at 24 and 72 h post-inoculation (hpi) when compared to that in the susceptible cultivar Jack. These results indicate that Glyma.17G086300 is a potential candidate gene for rxp and the QTLs and QTNs identified in this study will be useful for marker development for the breeding of Xag-resistant soybean cultivars. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]