Impact of Rhg1 copy number variation on a soybean cyst nematode resistance transcriptional network.

Soybean yield loss due to soybean cyst nematode (SCN) infestation has a negative impact on the U.S. economy. Most SCN-resistant soybeans carry a common resistance locus (Rhg1), conferred by copy number variation of a 31.2-kb segment at the Rhg1 locus. To identify the effects of Rhg1 copy number on the plant prior to SCN infection, we investigated genome-wide expression profiles in isogenic Fayette plants carrying different copy numbers at the Rhg1 locus (9-11 copies), that confer different levels of resistance to SCN. We found that even small differences in copy number lead to large changes in expression of downstream defense genes. The co-expression network constructed from differentially expressed genes (DEGs) outside the Rhg1 locus revealed complex effects of Rhg1 copy number on transcriptional regulation involving signal transduction and ethylene-mediated signaling pathways. Moreover, we report a variation in expression levels of phytoalexin biosynthesis-related genes that is correlated with copy number, and the activation of different NBS-LRR gene sets, indicating a broad effect of copy number on defense responses. Using qRT-PCR time series during SCN infection, we validated the SCN responses of DEGs detected in the copy number comparison and showed a stable upregulation of genes related to phytoalexin biosynthesis in resistant Fayette lines during the early stages of the incompatible interaction between soybeans and SCN, before syncytium formation. These results suggest additional genes that could enhance Rhg1-mediated SCN resistance.

A Comprehensive Evaluation of Arbitrary Image Style Transfer Methods.

Despite the remarkable process in the field of arbitrary image style transfer (AST), inconsistent evaluation continues to plague style transfer research. Existing methods often suffer from limited objective evaluation and inconsistent subjective feedback, hindering reliable comparisons among AST variants. In this study, we propose a multi-granularity assessment system that combines standardized objective and subjective evaluations. We collect a fine-grained dataset considering a range of image contexts such as different scenes, object complexities, and rich parsing information from multiple sources. Objective and subjective studies are conducted using the collected dataset. Specifically, we innovate on traditional subjective studies by developing an online evaluation system utilizing a combination of point-wise, pair-wise, and group-wise questionnaires. Finally, we bridge the gap between objective and subjective evaluations by examining the consistency between the results from the two studies. We experimentally evaluate CNN-based, flow-based, transformer-based, and diffusion-based AST methods by the proposed multi-granularity assessment system, which lays the foundation for a reliable and robust evaluation. Providing standardized measures, objective data, and detailed subjective feedback empowers researchers to make informed comparisons and drive innovation in this rapidly evolving field. Finally, for the collected dataset and our online evaluation system, please see http://ivc.ia.ac.cn.

MSEmbGAN: Multi-Stitch Embroidery Synthesis via Region-Aware Texture Generation.

Convolutional neural networks (CNNs) are widely used for embroidery feature synthesis from images. However, they are still unable to predict diverse stitch types, which makes it difficult for the CNNs to effectively extract stitch features. In this paper, we propose a multi-stitch embroidery generative adversarial network (MSEmbGAN) that uses a region-aware texture generation sub-network to predict diverse embroidery features from images. To the best of our knowledge, our work is the first CNN-based generative adversarial network to succeed in this task. Our region-aware texture generation sub-network detects multiple regions in the input image using a stitchclassifierandgeneratesastitchtextureforeachregionbasedonitsshapefeatures.Wealsoproposeacolorizationnetworkwitha color feature extractor, which helps achieve full image color consistency by requiring the color attributes of the output to closely resemble the input image. Because of the current lack of labeled embroidery image datasets, we provide a new multi-stitch embroidery dataset that is annotated with three single-stitch types and one multi-stitch type. Our dataset, which includes more than 30K high-quality multistitch embroidery images, more than 13K aligned content-embroidered images, and more than 17K unaligned images, is currently the largest embroidery dataset accessible, as far as we know. Quantitative and qualitative experimental results, including a qualitative user study, show that our MSEmbGAN outperforms current state-of-the-artembroiderysynthesisandstyle-transfermethodsonallevaluation indicators. Our demo and dataset sample can be found on the website https://csai.wtu.edu.cn/TVCG01/index.html.

Low-Shot Unsupervised Visual Anomaly Detection via Sparse Feature Representation.

Visual anomaly detection is an essential component in modern industrial manufacturing. Existing studies using notions of pairwise similarity distance between a test feature and nominal features have achieved great breakthroughs. However, the absolute similarity distance lacks certain generalizations, making it challenging to extend the comparison beyond the available samples. This limitation could potentially hamper anomaly detection performance in scenarios with limited samples. This article presents a novel sparse feature representation anomaly detection (SFRAD) framework, which formulates the anomaly detection as a sparse feature representation problem; and notably proposes an anomaly score by orthogonal matching pursuit (ASOMP) as a novel detection metric. Specifically, SFRAD calculates the Gaussian kernel distance between the test feature and its sparse representation in the nominal feature space for anomaly detection. Here, the orthogonal matching pursuit (OMP) algorithm is adopted to achieve the sparse feature representation. Moreover, to construct a low-redundancy memory bank storing the basis features for sparse representation, a novel basis feature sampling (BFS) algorithm is proposed by considering both the maximum coverage and the optimum feature representation simultaneously. As a result, SFRAD incorporates both the advantages of absolute similarity and linear representation; and this enhances the generalization in low-shot scenarios. Extensive experiments on the MVTec anomaly detection (MVTec AD), Kolektor surface-defect dataset (KolektorSDD), Kolektor surface-defect dataset 2 (KolektorSDD2), MVTec logical constraints anomaly detection (MVTec LOCO AD), Visual anomaly (VISA), Modified national institute of standards and technology (MNIST), and CIFAR-10 datasets demonstrate that our proposed SFRAD outperforms the previous methods and achieves state-of-the-art unsupervised anomaly detection performance. Notably, significantly improved outcomes and results have also been achieved on low-shot anomaly detection. Code is available at https://github.com/fanghuisky/SFRAD.

Whole genome sequence data of 516 F2 plants of tomato (Solanum lycopersicum).

The large-fruited fresh-market tomato cultivated in the U.S. represents a unique fruit market class of contemporary (modern) tomatoes for direct consumption. The genomes of F2 plants from crosses between inbred contemporary U.S. large-fruited fresh-market tomatoes were sequenced. 516 F2 individual plants randomly selected from five different biparental segregating populations were used for DNA extraction. The polymerase chain reaction (PCR)-free, paired-end (2 × 150 bp) sequencing libraries (350 bp DNA fragment length) were prepared, and sequenced on average 5 Gb for each plant using the Illumina next-generation sequencing technologies [1,2]. Raw Illumina reads with adapter contamination and/or uncertain nucleotides constitute (Ns, >10 % of either read; Q-score 5 or lower, >50 % of either read) were removed. This data article will contribute to improving our knowledge of the genetic recombination and variation in tomato.

Genome sequence data of the contemporary fresh-market tomatoes.

OBJECTIVE: The fresh-market tomato (Solanum lycopersicum) is bred for direct human consumption. It is selected for specific traits to meet market demands and production systems, and unique genetic variations underlying fresh-market tomato yields have been recently identified. However, DNA sequence variant-trait associations are not yet fully examined even for major traits. To provide a rich genome sequence resource for various genetics and breeding goals for fresh-market tomato traits, we report whole genome sequence data of a pool of contemporary U.S. fresh-market tomatoes. DATA DESCRIPTION: Eighty-one tomatoes were nominated by academic tomato breeding programs in the U.S. Of the 81 tomatoes, 68 were contemporary fresh-market tomatoes, whereas the remaining 13 were relevant fresh-market tomato breeding and germplasm accessions. Whole genome sequencing (WGS) of the 81 tomatoes was conducted using the Illumina next-generation sequencing technology. The polymerase chain reaction (PCR)-free, paired-end sequencing libraries were sequenced on an average depth per sequenced base of 24 × for each tomato. This data note enhances visibility and potential for use of the more diverse, freely accessible whole genome sequence data of contemporary fresh-market tomatoes.

Protein codes promote selective subcellular compartmentalization.

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must efficiently assemble. Here, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. A protein language model, ProtGPS, was developed that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in targeted subcellular compartments. ProtGPS also identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized code governing their distribution in specific cellular compartments.

Cartoon Animation Outpainting With Region-Guided Motion Inference.

Cartoon animation video is a popular visual entertainment form worldwide, however many classic animations were produced in a 4:3 aspect ratio that is incompatible with modern widescreen displays. Existing methods like cropping lead to information loss while retargeting causes distortion. Animation companies still rely on manual labor to renovate classic cartoon animations, which is tedious and labor-intensive, but can yield higher-quality videos. Conventional extrapolation or inpainting methods tailored for natural videos struggle with cartoon animations due to the lack of textures in anime, which affects the motion estimation of the objects. In this paper, we propose a novel framework designed to automatically outpaint 4:3 anime to 16:9 via region-guided motion inference. Our core concept is to identify the motion correspondences between frames within a sequence in order to reconstruct missing pixels. Initially, we estimate optical flow guided by region information to address challenges posed by exaggerated movements and solid-color regions in cartoon animations. Subsequently, frames are stitched to produce a pre-filled guide frame, offering structural clues for the extension of optical flow maps. Finally, a voting and fusion scheme utilizes learned fusion weights to blend the aligned neighboring reference frames, resulting in the final outpainting frame. Extensive experiments confirm the superiority of our approach over existing methods.

AnimeDiffusion: Anime Diffusion Colorization.

Being essential in animation creation, colorizing anime line drawings is usually a tedious and time-consuming manual task. Reference-based line drawing colorization provides an intuitive way to automatically colorize target line drawings using reference images. The prevailing approaches are based on generative adversarial networks (GANs), yet these methods still cannot generate high-quality results comparable to manually-colored ones. In this article, a new AnimeDiffusion approach is proposed via hybrid diffusions for the automatic colorization of anime face line drawings. This is the first attempt to utilize the diffusion model for reference-based colorization, which demands a high level of control over the image synthesis process. To do so, a hybrid end-to-end training strategy is designed, including phase 1 for training diffusion model with classifier-free guidance and phase 2 for efficiently updating color tone with a target reference colored image. The model learns denoising and structure-capturing ability in phase 1, and in phase 2, the model learns more accurate color information. Utilizing our hybrid training strategy, the network convergence speed is accelerated, and the colorization performance is improved. Our AnimeDiffusion generates colorization results with semantic correspondence and color consistency. In addition, the model has a certain generalization performance for line drawings of different line styles. To train and evaluate colorization methods, an anime face line drawing colorization benchmark dataset, containing 31,696 training data and 579 testing data, is introduced and shared. Extensive experiments and user studies have demonstrated that our proposed AnimeDiffusion outperforms state-of-the-art GAN-based methods and another diffusion-based model, both quantitatively and qualitatively.

Using machine learning and partial dependence to evaluate robustness of best linear unbiased prediction (BLUP) for phenotypic values.

Best linear unbiased prediction (BLUP) is widely used in plant research to address experimental variation. For phenotypic values, BLUP accuracy is largely dependent on properly controlled experimental repetition and how variable components are outlined in the model. Thus, determining BLUP robustness implies the need to evaluate contributions from each repetition. Here, we assessed the robustness of BLUP values for simulated or empirical phenotypic datasets, where the BLUP value and each experimental repetition served as dependent and independent (feature) variables, respectively. Our technique incorporated machine learning and partial dependence. First, we compared the feature importance estimated with the neural networks. Second, we compared estimated average marginal effects of individual repetitions, calculated with a partial dependence analysis. We showed that contributions of experimental repetitions are unequal in a phenotypic dataset, suggesting that the calculated BLUP value is likely to be influenced by some repetitions more than others (such as failing to detect simulated true positive associations). To resolve disproportionate sources, variable components in the BLUP model must be further outlined.

Distinct chemical environments in biomolecular condensates.

Diverse mechanisms have been described for selective enrichment of biomolecules in membrane-bound organelles, but less is known about mechanisms by which molecules are selectively incorporated into biomolecular assemblies such as condensates that lack surrounding membranes. The chemical environments within condensates may differ from those outside these bodies, and if these differed among various types of condensate, then the different solvation environments would provide a mechanism for selective distribution among these intracellular bodies. Here we use small molecule probes to show that different condensates have distinct chemical solvating properties and that selective partitioning of probes in condensates can be predicted with deep learning approaches. Our results demonstrate that different condensates harbor distinct chemical environments that influence the distribution of molecules, show that clues to condensate chemical grammar can be ascertained by machine learning and suggest approaches to facilitate development of small molecule therapeutics with optimal subcellular distribution and therapeutic benefit.

3DSN-Net: A 3-D Scale-Aware convNet With Nonlocal Context Guidance for Kidney and Tumor Segmentation From CT Volumes.

Automatic kidney and tumor segmentation from CT volumes is a critical prerequisite/tool for diagnosis and surgical treatment (such as partial nephrectomy). However, it remains a particularly challenging issue as kidneys and tumors often exhibit large-scale variations, irregular shapes, and blurring boundaries. We propose a novel 3-D network to comprehensively tackle these problems; we call it 3DSN-Net. Compared with existing solutions, it has two compelling characteristics. First, with a new scale-aware feature extraction (SAFE) module, the proposed 3DSN-Net is capable of adaptively selecting appropriate receptive fields according to the sizes of targets instead of indiscriminately enlarging them, which is particularly essential for improving the segmentation accuracy of the tumor with large scale variation. Second, we propose a novel yet efficient nonlocal context guidance (NCG) mechanism to capture global dependencies to tackle irregular shapes and blurring boundaries of kidneys and tumors. Instead of directly harnessing a 3-D NCG mechanism, which makes the number of parameters exponentially increase and hence the network difficult to be trained under limited training data, we develop a 2.5D NCG mechanism based on projections of feature cubes, which achieves a tradeoff between segmentation accuracy and network complexity. We extensively evaluate the proposed 3DSN-Net on the famous KiTS dataset with many challenging kidney and tumor cases. Experimental results demonstrate our solution consistently outperforms state-of-the-art 3-D networks after being equipped with scale aware and NCG mechanisms, particularly for tumor segmentation.

Adaptive Safe Reinforcement Learning With Full-State Constraints and Constrained Adaptation for Autonomous Vehicles.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

Data-Driven Linear Quadratic Optimization for Controller Synthesis With Structural Constraints.

For various typical cases and situations where the formulation results in an optimal control problem, the linear quadratic regulator (LQR) approach and its variants continue to be highly attractive. In certain scenarios, it can happen that some prescribed structural constraints on the gain matrix would arise. Consequently then, the algebraic Riccati equation (ARE) is no longer applicable in a straightforward way to obtain the optimal solution. This work presents a rather effective alternative optimization approach based on gradient projection. The utilized gradient is obtained through a data-driven methodology, and then projected onto applicable constrained hyperplanes. Essentially, this projection gradient determines a direction of progression and computation for the gain matrix update with a decreasing functional cost; and then the gain matrix is further refined in an iterative framework. With this formulation, a data-driven optimization algorithm is summarized for controller synthesis with structural constraints. This data-driven approach has the key advantage that it avoids the necessity of precise modeling which is always required in the classical model-based counterpart; and thus the approach can additionally accommodate various model uncertainties. Illustrative examples are also provided in the work to validate the theoretical results.

Precise sensorimotor control impacts reproductive fitness of C. elegans in 3D environments.

The ability of animals to sense and navigate towards relevant cues in complex and elaborate habitats is paramount for their survival and reproductive success. The nematode Caenorhabditis elegans uses a simple and elegant sensorimotor program to track odors in its environments. Whether this allows the worm to effectively navigate a complex environment and increase its evolutionary success has not been tested yet. We designed an assay to test whether C. elegans can track odors in a complex 3D environment. We then used a previously established 3D cultivation system to test whether defect in tracking odors to find food in a complex environment affected their brood size. We found that wild-type worms can accurately migrate toward a variety of odors in 3D. However, mutants of the muscarinic acetylcholine receptor GAR-3 which have a sensorimotor integration defect that results in a subtle navigational defect steering towards attractive odors, display decreased chemotaxis to the odor butanone not seen in the traditional 2D assay. We also show that the decreased ability to locate appetitive stimuli in 3D leads to reduced brood size not observed in the standard 2D culture conditions. Our study shows that mutations in genes previously overlooked in 2D conditions can have a significant impact in the natural habitat, and highlights the importance of considering the evolutionary selective pressures that have shaped the behavior, as well as the underlying genes and neural circuits.

Comparative Evaluation of the Efficacy of Combined Intramedullary Pinning with K-Wires Pinning in the Treatment of Fifth Metacarpal Neck Fractures versus Conventional Techniques-K-Wires Pinning and Intramedullary Pinning.

Background and Objectives: Since the neck is the weakest part of the metacarpals, the most common metacarpal fracture is a neck fracture, a type which accounts for 38% of all hand fractures. Such fractures can be fixed using a variety of conventional techniques, including intramedullary pinning and K-wire pinning. However, conventional techniques involve complications, such as angulation, stiffness, and rotational deformity. The purpose of this study was to compare the usefulness of our new technique, combined intramedullary pinning with K-wire pinning (IPKP), with those of intramedullary pinning (IP) and K-wire pinning (KP). Materials and Methods: This was a single-center, randomized controlled trial conducted between January 2005 and April 2023. A total of 158 patients with acute displaced fractures of the fifth-metacarpal neck were randomly assigned to either the IPKP group (n = 48), the KP group (n = 60), or the IP group (n = 50). We radiographically evaluated angulation and shortening in three visits: pre-operatively, post-operatively, and at a 1-year follow-up. We clinically evaluated the ranges of motion and Quick-DASH scores to assess daily living performance and the cosmetic scores, using the SBSES score, to assess patients' satisfaction with their cosmetic outcomes. Results: The IPKP group was superior to the KP group and the IP group regarding radiographical and clinical assessments at the 1-year follow-up visit. The angulation was 15.7° (±7.7) in the KP group, 17.0° (±5.9) in the IP group, and 12.6° (±2.5) in the IPKP group (p < 0.001) at the 1-year follow-up visit. The shortening was 0.9 mm (±0.3) in the KP group, 1.4 mm (±0.2) in the IP group, and 0.4 mm (±0.1) in the IPKP group (m < 0.001) at the 1-year follow-up visit. The TAM was 272.6° (±17.5) in the KP group, 271.1° (±18.0) in the IP group, and 274.1° (±14.9) in the IPKP group (p = 0.42). Four patients (6.6%) in the KP group and two patients (4%) in the IP group were reported as having stiffness, while no patients were found to have stiffness in the IPKP group. The average Quick-DASH score was 2.3 (±0.5) in the KP group, 2.5 (±0.4) in the IP group, and 1.9 (±0.4) in the IPKP group (p > 0.05). The average cosmetic score was 3.7 (±1.2) in the KP group, 3.8 (±0.9) in the IP group, and 4.7 (±0.8) in the IPKP group (p < 0.001). A complication involving nonunion occurred in one case (1.6%) in the KP group, while there were three cases (6%) of rotational deformity in the IP groups. Conclusions: With the IPKP technique, accurate reduction can be achieved to improve hand function and cosmetic outcomes.

Retargeting video with an end-to-end framework.

Video holds significance in computer graphics applications. Because of the heterogeneous of digital devices, retargeting videos becomes an essential function to enhance user viewing experience in such applications. In the research of video retargeting, preserving the relevant visual content in videos, avoiding flicking, and processing time are the vital challenges. Extending image retargeting techniques to the video domain is challenging due to the high running time. Prior work of video retargeting mainly utilizes time-consuming preprocessing to analyze frames. Plus, being tolerant of different video content, avoiding important objects from shrinking, and the ability to play with arbitrary ratios are the limitations that need to be resolved in these systems requiring investigation. In this paper, we present an end-to-end RETVI method to retarget videos to arbitrary aspect ratios. We eliminate the computational bottleneck in the conventional approaches by designing RETVI with two modules, content feature analyzer (CFA) and adaptive deforming estimator (ADE). The extensive experiments and evaluations show that our system outperforms previous work in quality and running time.

Transcription factors interact with RNA to regulate genes.

Transcription factors (TFs) orchestrate the gene expression programs that define each cell's identity. The canonical TF accomplishes this with two domains, one that binds specific DNA sequences and the other that binds protein coactivators or corepressors. We find that at least half of TFs also bind RNA, doing so through a previously unrecognized domain with sequence and functional features analogous to the arginine-rich motif of the HIV transcriptional activator Tat. RNA binding contributes to TF function by promoting the dynamic association between DNA, RNA, and TF on chromatin. TF-RNA interactions are a conserved feature important for vertebrate development and disrupted in disease. We propose that the ability to bind DNA, RNA, and protein is a general property of many TFs and is fundamental to their gene regulatory function.

MeshWGAN: Mesh-to-Mesh Wasserstein GAN With Multi-Task Gradient Penalty for 3D Facial Geometric Age Transformation.

As the metaverse develops rapidly, 3D facial age transformation is attracting increasing attention, which may bring many potential benefits to a wide variety of users, e.g., 3D aging figures creation, 3D facial data augmentation and editing. Compared with 2D methods, 3D face aging is an underexplored problem. To fill this gap, we propose a new mesh-to-mesh Wasserstein generative adversarial network (MeshWGAN) with a multi-task gradient penalty to model a continuous bi-directional 3D facial geometric aging process. To the best of our knowledge, this is the first architecture to achieve 3D facial geometric age transformation via real 3D scans. As previous image-to-image translation methods cannot be directly applied to the 3D facial mesh, which is totally different from 2D images, we built a mesh encoder, decoder, and multi-task discriminator to facilitate mesh-to-mesh transformations. To mitigate the lack of 3D datasets containing children's faces, we collected scans from 765 subjects aged 5-17 in combination with existing 3D face databases, which provided a large training dataset. Experiments have shown that our architecture can predict 3D facial aging geometries with better identity preservation and age closeness compared to 3D trivial baselines. We also demonstrated the advantages of our approach via various 3D face-related graphics applications. Our project will be publicly available at: https://github.com/Easy-Shu/MeshWGAN.