The role of hydrogen-rich water in delaying the pulp breakdown of litchi fruit during postharvest storage.

Previous studies have indicated that hydrogen-rich water (HW) treatment can delay fruit ripening and senescence. However, little is known about the HW-delaying pulp breakdown. In this study, eight physiological characteristics revealed that HW treatment delayed both pericarp browning and pulp breakdown of litchi fruit. To gain a comprehensive understanding of the changes in litchi pulp, a combination of multiple metabolomics and gene expression analyses was conducted, assessing 67 primary metabolites, 103 volatiles, 31 amino acids, and 13 crucial metabolite-related genes. Results showed that HW treatment promoted starch degradation, decelerated cell wall degradation and glycolysis, and maintained the flavor and quality of litchi fruit. Furthermore, HW treatment stimulated the production of volatile alcohols, aldehydes, ketones, olefins, and amino acids, which might play a vital role in HW-delaying pulp breakdown. This study sheds light on the mechanism by which HW delayed pulp breakdown by investigating small molecule metabolites and metabolic pathways.

High-quality genome assemblies for two Australimusa bananas (Musa spp.) and insights into regulatory mechanisms of superior fiber properties.

Bananas (Musa spp.) are monocotyledonous plants with high genetic diversity in the Musaceae family that are cultivated mainly in tropical and subtropical countries. The fruits are a popular food, and the plants themselves have diverse uses. Four genetic groups (genomes) are thought to have contributed to current banana cultivars: Musa acuminata (A genome), Musa balbisiana (B genome), Musa schizocarpa (S genome), and species of the Australimusa section (T genome). However, the T genome has not been effectively explored. Here, we present the high-quality TT genomes of two representative accessions, Abaca (Musa textilis), with high-quality natural fiber, and Utafun (Musa troglodytarum, Fe'i group), with abundant ß-carotene. Both the Abaca and Utafun assemblies comprise 10 pseudochromosomes, and their total genome sizes are 613 Mb and 619 Mb, respectively. Comparative genome analysis revealed that the larger size of the T genome is likely attributable to rapid expansion and slow removal of transposons. Compared with those of Musa AA or BB accessions or sisal (Agava sisalana), Abaca fibers exhibit superior mechanical properties, mainly because of their thicker cell walls with a higher content of cellulose, lignin, and hemicellulose. Expression of MusaCesA cellulose synthesis genes peaks earlier in Abaca than in AA or BB accessions during plant development, potentially leading to earlier cellulose accumulation during secondary cell wall formation. The Abaca-specific expressed gene MusaMYB26, which is directly regulated by MusaMYB61, may be an important regulator that promotes precocious expression of secondary cell wall MusaCesAs. Furthermore, MusaWRKY2 and MusaNAC68, which appear to be involved in regulating expression of MusaLAC and MusaCAD, may at least partially explain the high accumulation of lignin in Abaca. This work contributes to a better understanding of banana domestication and the diverse genetic resources in the Musaceae family, thus providing resources for Musa genetic improvement.

Increasing the Robustness of Deep Learning Models for Object Segmentation: A Framework for Blending Automatically Annotated Real and Synthetic Data.

Recent problems in robotics can sometimes only be tackled using machine learning technologies, particularly those that utilize deep learning (DL) with transfer learning. Transfer learning takes advantage of pretrained models, which are later fine-tuned using smaller task-specific datasets. The fine-tuned models must be robust against changes in environmental factors such as illumination since, often, there is no guarantee for them to be constant. Although synthetic data for pretraining has been shown to enhance DL model generalization, there is limited research on its application for fine-tuning. One limiting factor is that the generation and annotation of synthetic datasets can be cumbersome and impractical for the purpose of fine-tuning. To address this issue, we propose two methods for automatically generating annotated image datasets for object segmentation, one for real-world and another for synthetic images. We also introduce a novel domain adaptation approach called filling the reality gap (FTRG), which can blend elements from real-world and synthetic scenes in a single image to achieve domain adaptation. We demonstrate through experimentation on a representative robot application that FTRG outperforms other domain adaptation techniques, such as domain randomization or photorealistic synthetic images, in creating robust models. Furthermore, we evaluate the benefits of using synthetic data for fine-tuning in transfer learning and continual learning with experience replay using our proposed methods and FTRG. Our findings indicate that fine-tuning with synthetic data can produce superior results compared to solely using real-world data.

Two haplotype-resolved genome assemblies for AAB allotriploid bananas provide insights into banana subgenome asymmetric evolution and Fusarium wilt control.

Bananas (Musa spp.) are one of the world's most important fruit crops and play a vital role in food security for many developing countries. Most banana cultivars are triploids derived from inter- and intraspecific hybridizations between the wild diploid ancestor species Musa acuminate (AA) and M. balbisiana (BB). We report two haplotype-resolved genome assemblies of the representative AAB-cultivated types, Plantain and Silk, and precisely characterize ancestral contributions by examining ancestry mosaics across the genome. Widespread asymmetric evolution is observed in their subgenomes, which can be linked to frequent homologous exchange events. We reveal the genetic makeup of triploid banana cultivars and verify that subgenome B is a rich source of disease resistance genes. Only 58.5% and 59.4% of Plantain and Silk genes, respectively, are present in all three haplotypes, with >50% of genes being differentially expressed alleles in different subgenomes. We observed that the number of upregulated genes in Plantain is significantly higher than that in Silk at one-week post-inoculation with Fusarium wilt tropical race 4 (Foc TR4), which confirms that Plantain can initiate defense responses faster than Silk. Additionally, we compared genomic and transcriptomic differences among the genes related to carotenoid synthesis and starch metabolism between Plantain and Silk. Our study provides resources for better understanding the genomic architecture of cultivated bananas and has important implications for Musa genetics and breeding.

Adherent-invasive Escherichia coli LF82 aggravated intestinal inflammation in colitis mice by affecting the gut microbiota and Th17/Treg cell differentiation balance.

The imbalance of Th17 and Treg cell differentiation, intestinal flora imbalance, and intestinal mucosal barrier damage may be important links in the occurrence and development of inflammatory bowel disease (IBD) since Th17 and Treg differentiation are affected by the intestinal flora. This study aimed to explore the effect of Escherichia coli (E. coli) LF82 on the differentiation of Th17 and Treg cells and the role of the intestinal flora in mouse colitis. The effects of E. coli LF82 infection on intestinal inflammation were evaluated by analyzing the disease activity index, histology, myeloperoxidase activity, FITC-D fluorescence value, and claudin-1 and ZO-1 expression. The effects of E. coli LF82 on the Th17/Treg balance and intestinal flora were analyzed by flow cytometry and 16S rDNA sequencing. Inflammatory markers, changes in the intestinal flora, and Th17/Treg cells were then detected after transplanting fecal bacteria from normal mice into colitis mice infected by E. coli LF82. We found that E. coli LF82 infection can aggravate the intestinal inflammation of mice colitis, destroy their intestinal mucosal barrier, increase intestinal mucosal permeability, and aggravate the imbalance of Th17/Treg differentiation and the disorder of intestinal flora. After improving the intestinal flora imbalance by fecal bacteria transplantation, intestinal inflammation and intestinal mucosal barrier damage were reduced, and the differentiation balance of Th17 and Treg cells was restored. This study showed that E. coli LF82 infection aggravates intestinal inflammation and intestinal mucosal barrier damage in colitis by affecting the intestinal flora composition and indirectly regulating the Th17 and Treg cell differentiation balance.

Trajectory Tracking of Variable Centroid Objects Based on Fusion of Vision and Force Perception.

Compared with traditional rigid objects' dynamic throwing and catching by the robot, the in-flight trajectory of nonrigid objects (incredibly variable centroid objects) throwing is more challenging to predict and track. This article proposes a variable centroid trajectory tracking network (VCTTN) with the fusion of vision and force information by introducing force data of throw processing to the vision neural network. The VCTTN-based model-free robot control system is developed to perform highly precise prediction and tracking with a part of the in-flight vision. The flight trajectories dataset of variable centroid objects generated by the robot arm is collected to train VCTTN. The experimental results show that trajectory prediction and tracking with the vision-force VCTTN is superior to the ones with the traditional vision perception and has an excellent tracking performance.

Genome-wide analysis of the polyphenol oxidase gene family reveals that MaPPO1 and MaPPO6 are the main contributors to fruit browning in Musa acuminate.

Introduction: Polyphenol oxidases (PPOs), which are widely present in plants, play an important role in the growth, development, and stress responses. They can catalyze the oxidization of polyphenols and result in the browning of damaged or cut fruit, which seriously affects fruit quality and compromises the sale of fruit. In banana (Musa acuminata, AAA group), 10 PPO genes were determined based on the availability of a high-quality genome sequence, but the role of PPO genes in fruit browning remains unclear. Methods: In this study, we analyzed the physicochemical properties, gene structure, conserved structural domains, and evolutionary relationship of the PPO gene family of banana. The expression patterns were analyzed based on omics data and verified by qRT-PCR analysis. Transient expression assay in tobacco leaves was used to identify the subcellular localization of selected MaPPOs, and we analyzed the polyphenol oxidase activity using recombinant MaPPOs and transient expression assay. Results and discussion: We found that more than two-thirds of the MaPPO genes had one intron, and all contained three conserved structural domains of PPO, except MaPPO4. Phylogenetic tree analysis revealed that MaPPO genes were categorized into five groups. MaPPOs did not cluster with Rosaceae and Solanaceae, indicating distant affinities, and MaPPO6/7/8/9/10 clustered into an individual group. Transcriptome, proteome, and expression analyses showed that MaPPO1 exhibits preferential expression in fruit tissue and is highly expressed at respiratory climacteric during fruit ripening. Other examined MaPPO genes were detectable in at least five different tissues. In mature green fruit tissue, MaPPO1 and MaPPO6 were the most abundant. Furthermore, MaPPO1 and MaPPO7 localized in chloroplasts, and MaPPO6 was a chloroplast- and Endoplasmic Reticulum (ER)-localized protein, whereas MaPPO10 only localized in the ER. In addition, the enzyme activity in vivo and in vitro of the selected MaPPO protein showed that MaPPO1 had the highest PPO activity, followed by MaPPO6. These results imply that MaPPO1 and MaPPO6 are the main contributors to banana fruit browning and lay the foundation for the development of banana varieties with low fruit browning.

Multiple metabolomics comparatively investigated the pulp breakdown of four dragon fruit cultivars during postharvest storage.

Pulp breakdown is the main reason for the reduction of fruit quality. However, there are relatively few studies on small molecule metabolites based on the pulp breakdown of dragon fruit. In this study, four dragon fruit cultivars were comparatively analyzed during pulp breakdown. According to five firmness-related and six quality-related indicators, the pulp breakdown rates from low to high were 'Baiyulong (WP, with white pulp)', 'Dahong (RP, with red pulp)', 'Hongshuijing (CRP, with red pulp)' and 'Baishuijing (CWP, with white pulp)'. Five secondary metabolites showed cultivar-specific accumulation, and the increase of their contents during postharvest storage might be related to delaying pulp breakdown. After multiple metabolomics analysis, a total of 186 metabolites were identified, among which 14 primary metabolites, 23 volatiles, 2 hydrolyzed amino acids and 12 free amino acids were considered as key metabolites. The contents of hydrocarbons in WP and RP were much higher than that in CWP and CRP, which was negatively correlated with pulp breakdown. White pulp were rich in amino acids, while red pulp had more soluble sugars, aldehydes and terpenes. The contents of 13 key metabolites increased during pulp breakdown in all four cultivars, mainly including amino acids and alkanes. The contents and changes of those key metabolites might directly or indirectly respond to the pulp quality and resistance of dragon fruit.

Hybrid Visual-Ranging Servoing for Positioning Based on Image and Measurement Features.

In this article, a hybrid visual-ranging servoing method is proposed to realize high-precision positioning tasks with a 6-degree of freedom (DOF) manipulator. This method utilizes the image and measurement features directly in the control loop. Without the need of complex image feature design and attitude estimation, this method realizes the 6-DOF control of a robot. A vital challenge in traditional vision-based systems is avoiding local minima and singularity problems. To tackle this issue, a full-rank interaction matrix hybrid visual servo (FRHVS) design criterion is proposed, which guarantees that the hybrid interaction matrix and its pseudoinverse matrix are both full rank. Moreover, the interaction matrix for these hybrid strategies, which combines image features with other sensors features, is derived in an analytical form. Experiments on a 6-DOF manipulator show that the proposed method is effective and has global asymptotic stability and high precision.

Computationally Relaxed Unscented Kalman Filter.

Advanced robotics and autonomous vehicles rely on filtering and sensor fusion techniques to a large extent. These mobile applications need to handle the computations onboard at high rates while the computing capacities are limited. Therefore, any improvement that lowers the CPU time of the filtering leads to more accurate control or longer battery operation. This article introduces a generic computational relaxation for the unscented transformation (UT) that is the key operation of the Unscented Kalman filter-based applications. The central idea behind the relaxation is to pull out the linear part of the filtering model and avoid the calculations for the kernel of the nonlinear part. The practical merit of the proposed relaxation is demonstrated through a simultaneous localization and mapping (SLAM) implementation that underpins the superior performance of the algorithm in the practically relevant cases, where the nonlinear dependencies influence only an affine subspace of the image space. The numerical examples show that the computational demand can be mitigated below 50% without decreasing the accuracy of the approximation. The method described in this article is implemented and published as an open-source C++ library RelaxedUnscentedTransformation on GitHub.

Data Augmentation in Defect Detection of Sanitary Ceramics in Small and Non-i.i.d Datasets.

In this study, a data-augmentation method is proposed to narrow the significant difference between the distribution of training and test sets when small sample sizes are concerned. Two major obstacles exist in the process of defect detection on sanitary ceramics. The first results from the high cost of sample collection, namely, the difficulty in obtaining a large number of training images required by deep-learning algorithms, which limits the application of existing algorithms in sanitary-ceramic defect detection. Second, due to the limitation of production processes, the collected defect images are often marked, thereby resulting in great differences in distribution compared with the images of test sets, which further affects the performance of detect-detection algorithms. The lack of training data and the differences in distribution between training and test sets lead to the fact that existing deep learning-based algorithms cannot be used directly in the defect detection of sanitary ceramics. The method proposed in this study, which is based on a generative adversarial network and the Gaussian mixture model, can effectively increase the number of training samples and reduce distribution differences between training and test sets, and the features of the generated images can be controlled to a certain extent. By applying this method, the accuracy is improved from approximately 75% to nearly 90% in almost all experiments on different classification networks.

WRKY transcription factor MaWRKY49 positively regulates pectate lyase genes during fruit ripening of Musa acuminata.

Fruit ripening is the last phase of fruit growth and development. The initiation and progression of fruit ripening are highly modulated by a plethora of key genes, such as transcription factor (TF) genes. The WRKY gene family is a large group of TFs that play important roles in various cellular processes; nevertheless, the role of WRKY TF on fruit ripening remains enigmatic. Here, we report that a banana WRKY TF, MaWRKY49 functions in ethylene-induced fruit ripening by modulating the expression of fruit softening-related genes. We found that the expression of MaWRKY49 is highly induced by ethephon and inhibited by 1-methylcyclopropene, which is synchronous with the ripening process. Moreover, based on transcriptome data on fruit ripening, two pectate lyase (PL) genes that are involved in fruit softening were determined, and their expression pattern is also consistent with the fruit ripening process. Yeast one-hybrid and dual-luciferase assay confirmed that MaWRKY49 activated the transcription of two PL genes. In addition, transient overexpression of MaWRKY49 in banana fruits can apparently accelerate fruit ripening processs. Taken together, our findings indicate that MaWRKY49 acts as a potential modulator of fruit ripening by direct regulation of PL expression. This work contributes to developing the technology for improving the shelf-life of banana fruit.

ResDNet: Efficient Dense Multi-Scale Representations With Residual Learning for High-Level Vision Tasks.

Deep feature fusion plays a significant role in the strong learning ability of convolutional neural networks (CNNs) for computer vision tasks. Recently, works continually demonstrate the advantages of efficient aggregation strategy and some of them refer to multiscale representations. In this article, we describe a novel network architecture for high-level computer vision tasks where densely connected feature fusion provides multiscale representations for the residual network. We term our method the ResDNet which is a simple and efficient backbone made up of sequential ResDNet modules containing the variants of dense blocks named sliding dense blocks (SDBs). Compared with DenseNet, ResDNet enhances the feature fusion and reduces the redundancy by shallower densely connected architectures. Experimental results on three classification benchmarks including CIFAR-10, CIFAR-100, and ImageNet demonstrate the effectiveness of ResDNet. ResDNet always outperforms DenseNet using much less computation on CIFAR-100. On ImageNet, ResDNet-B-129 achieves 1.94% and 0.89% top-1 accuracy improvement over ResNet-50 and DenseNet-201 with similar complexity. Besides, ResDNet with more than 1000 layers achieves remarkable accuracy on CIFAR compared with other state-of-the-art results. Based on MMdetection implementation of RetinaNet, ResDNet-B-129 improves mAP from 36.3 to 39.5 compared with ResNet-50 on COCO dataset.

Hierarchical Multiobjective Heuristic for PCB Assembly Optimization in a Beam-Head Surface Mounter.

This article proposes a hierarchical multiobjective heuristic (HMOH) to optimize printed-circuit board assembly (PCBA) in a single beam-head surface mounter. The beam-head surface mounter is the core facility in a high-mix and low-volume PCBA line. However, as a large-scale, complex, and multiobjective combinatorial optimization problem, the PCBA optimization of the beam-head surface mounter is still a challenge. This article provides a framework for optimizing all the interrelated objectives, which has not been achieved in the existing studies. A novel decomposition strategy is applied. This helps to closely model the real-world problem as the head task assignment problem (HTAP) and the pickup-and-place sequencing problem (PAPSP). These two models consider all the factors affecting the assembly time, including the number of pickup-and-place (PAP) cycles, nozzle changes, simultaneous pickups, and the PAP distances. Specifically, HTAP consists of the nozzle assignment and component allocation, while PAPSP comprises place allocation, feeder set assignment, and place sequencing problems. Adhering strictly to the lexicographic method, the HMOH solves these subproblems in a descending order of importance of their involved objectives. Exploiting the expert knowledge, each subproblem is solved by an elaborately designed heuristic. Finally, the proposed HMOH realizes the complete and optimal PCBA decision making in real time. Using industrial PCB datasets, the superiority of HMOH is elucidated through comparison with the built-in optimizer of the widely used Samsung SM482.

Adaptive Sensor Fault Accommodation for Vehicle Active Suspensions via Partial Measurement Information.

In this article, an adaptive sensor fault accommodation scheme is proposed for uncertain vehicle active suspensions via output-feedback control where vehicle body displacement is the only measurable output signal corrupted by sensor bias. An adaptive observer with variable gains is constructed to obtain state estimates whose design procedure involves parameter adaption of the uncertain system parameters and sensor bias, and an output-feedback controller is designed to attenuate the vehicle body displacement based on the partial measurement information, estimates of the states, and unknown parameters. Compensation for measurement error is made both in the design process of the adaptive observer and output-feedback controller in order to weaken the influence brought about by sensor bias fault. In order to guarantee system stability, the variable observer gains are determined in real time using a switching strategy where their values can be modified in finite times by monitoring the state estimates generated by the observer itself. It is proved that the vehicle body displacement will converge to a small neighborhood around zero, and all the signals of the closed-loop system are ensured to be bounded through selecting suitable control parameters. Simulation is carried out to show the effectiveness of the proposed method and results indicate that better stabilization of the suspension vertical motion can be achieved through adaptive compensation for sensor bias.

Octopus-Inspired Microgripper for Deformation-Controlled Biological Sample Manipulation.

Predators in nature grip their prey in different ways, which give innovational ideas of gripping approaches in industrial applications. Octopus performs flexible gripping with the help of vacuum grippers, suction cups, which inspired a new type of microgripper for biological sample micromanipulation. The proposed gripper consists of a glass pipette and a pump driven by a step-motor. The step-motor is controlled with adaptive robust control to adjust the gripping pressure applied on the biological sample. A dynamic model is developed for the biological sample aiming for better deformation control performance. A visual detection algorithm is developed for data processing to identify the parameters in the dynamic model and the detection result of visual algorithm is also used as feedback of adaptive robust control, which diminishes the negative influence of parameter and model uncertainties. Zebrafish larva was used as the testing sample for experiment and the corresponding parameters were identified experimentally. The experimental results correlated well with the model predicted deformation curve and visual detection algorithm provided promising accuracy, which is less than [Formula: see text]. Adaptive robust control provides fast and accuracy response in point-to-point deformation testing, and the average responding time is less than 30 s and the average error is no larger than 1 pixel.

The role of hydrogen water in delaying ripening of banana fruit during postharvest storage.

Experiments were conducted to identify the role of hydrogen water (HW) in banana fruit ripening. Banana fruit soaked with 0.8 ppm HW showed longer ripening than control fruit. HW treatment significantly reduced ethylene production and respiratory rate, and inhibited the expressions of ethylene synthesis- and signaling-related genes. Similarly, HW treatment inhibited the down-regulation of chlorophylls binding proteins and delayed the increase of chromaticity a*, b* and L* in banana peel. Furthermore, HW-treated peel exhibited lower expressions of cell wall degradation-related genes and higher levels of fruit firmness, pectin, hemicellulose and lignin. In addition, HW-treated pulp exhibited higher levels of starch, lower level of total soluble solids (TSS) and lower expression of flavor-related genes. Microstructural observation further confirmed that HW treatment delayed the degradations of starch and cell walls. Those results indicated that HW treatment delayed banana ripening via the role of ethylene in relation to degreening, flavor and softening.

Metabolic variations in the pulp of four litchi cultivars during pulp breakdown.

Fruit of four litchi cultivars were stored at 25 ± 1 °C. The shelf life changed from long to short respectively was "Feizixiao (FXZ), "Jingganghongnuo (JGHN)", "Huaizhi (HZ)" and "Nuomici (NMC)". During pulp breakdown, marketable fruit and total soluble solids (TSS) decreased significantly, while respiratory rate increased significantly. After metabolomics analysis, a total of 179 metabolites were detected in litchi pulp, including 56 primary metabolites, 79 volatile compounds, 28 free amino acids and 16 hydrolyzed amino acids. Compared with other litchi cultivars, FZX pulp was rich in volatile alcohols and amino acids, NMC pulp was rich in soluble sugars and sesquiterpenes, and JGHN and HZ pulp were rich in sesquiterpenes. During the postharvest storage, most of volatiles and amino acids were induced in NMC pulp, while most of volatiles were reduced in JGHN and HZ pulp. The specific metabolites accumulated in a litchi pulp might be related to its shelf life and fruit quality. The increased metabolites during pulp breakdown might be also related to the resistance of litchi pulp.

Transcriptome and metabolome profiling provide insights into molecular mechanism of pseudostem elongation in banana.

BACKGROUND: Banana plant height is an important trait for horticultural practices and semi-dwarf cultivars show better resistance to damages by wind and rain. However, the molecular mechanisms controlling the pseudostem height remain poorly understood. Herein, we studied the molecular changes in the pseudostem of a semi-dwarf banana mutant Aifen No. 1 (Musa spp. Pisang Awak sub-group ABB) as compared to its wild-type dwarf cultivar using a combined transcriptome and metabolome approach. RESULTS: A total of 127 differentially expressed genes and 48 differentially accumulated metabolites were detected between the mutant and its wild type. Metabolites belonging to amino acid and its derivatives, flavonoids, lignans, coumarins, organic acids, and phenolic acids were up-regulated in the mutant. The transcriptome analysis showed the differential regulation of genes related to the gibberellin pathway, auxin transport, cell elongation, and cell wall modification. Based on the regulation of gibberellin and associated pathway-related genes, we discussed the involvement of gibberellins in pseudostem elongation in the mutant banana. Genes and metabolites associated with cell wall were explored and their involvement in cell extension is discussed. CONCLUSIONS: The results suggest that gibberellins and associated pathways are possibly developing the observed semi-dwarf pseudostem phenotype together with cell elongation and cell wall modification. The findings increase the understanding of the mechanisms underlying banana stem height and provide new clues for further dissection of specific gene functions.