Analysis of microstructures and macrotextures for different apple cultivars based on parenchyma morphology

Automatic 3D cell segmentation of fruit parenchyma tissue from X-ray micro CT images using deep learning

BACKGROUND

High quality 3D information of the microscopic plant tissue morphology-the spatial organization of cells and intercellular spaces in tissues-helps in understanding physiological processes in a wide variety of plants and tissues. X-ray micro-CT is a valuable tool that is becoming increasingly available in plant research to obtain 3D microstructural information of the intercellular pore space and individual pore sizes and shapes of tissues. However, individual cell morphology is difficult to retrieve from micro-CT as cells cannot be segmented properly due to negligible density differences at cell-to-cell interfaces. To address this, deep learning-based models were trained and tested to segment individual cells using X-ray micro-CT images of parenchyma tissue samples from apple and pear fruit with different cell and porosity characteristics.

RESULTS

The best segmentation model achieved an Aggregated Jaccard Index (AJI) of 0.86 and 0.73 for apple and pear tissue, respectively, which is an improvement over the current benchmark method that achieved AJIs of 0.73 and 0.67. Furthermore, the neural network was able to detect other plant tissue structures such as vascular bundles and stone cell clusters (brachysclereids), of which the latter were shown to strongly influence the spatial organization of pear cells. Based on the AJIs, apple tissue was found to be easier to segment, as the porosity and specific surface area of the pore space are higher and lower, respectively, compared to pear tissue. Moreover, samples with lower pore network connectivity, proved very difficult to segment.

CONCLUSIONS

The proposed method can be used to automatically quantify 3D cell morphology of plant tissue from micro-CT instead of opting for laborious manual annotations or less accurate segmentation approaches. In case fruit tissue porosity or pore network connectivity is too low or the specific surface area of the pore space too high, native X-ray micro-CT is unable to provide proper marker points of cell outlines, and one should rely on more elaborate contrast-enhancing scan protocols.

Differential pulp cell wall structures lead to diverse fruit textures in apple (Malus domestica)

In this study, we aimed to elucidate the effect of pulp cell wall structure on fruit hardness and crispness in apples. To this end, we studied the cell wall polysaccharides in two apple varieties, "Hanfu" and "Honeycrisp," during fruit development. Compared with Hanfu, the crispness of Honeycrisp was higher, whereas its harness was lower. The intensity and distribution of immunofluorescence signals indicated that galactose and arabinose contributed to the higher hardness of Hanfu, whereas arabinose, egg-box structure, and fucosylated xyloglucans, distributed in the corners of tricellular junctions, enhanced the cell-cell adhesion and improved the crispness of Honeycrisp. Besides, fucosylated xyloglucan played an important role in promoting the formation and maintaining the strength of the cell wall skeleton and, consequently, retaining the fruit crispness. The esterification state of pectin had little effect on the fruit hardness and crispness in both varieties. Collectively, our findings provided information on the underlying mechanism of fruit texture formation in apples.

Texture of Hot-Air-Dried Persimmon (Diospyros kaki) Chips: Instrumental, Sensory, and Consumer Input for Product Development

Persimmon (Diospyros kaki) is an underutilized tree fruit. Previous studies have shown the feasibility of making a hot-air-dried, chip-style product from persimmon. However, the texture of this type of product has not been explored or connected to consumer preference. Thus, for dried samples representing 37 cultivars, this study aimed to (1) predict trained sensory panel texture attributes from instrumental measurements, (2) predict consumer liking from instrumental measurements and sensory texture attributes, and (3) elucidate whether astringency type affects dried product texture. Partial least-squares regression models of fair-to-good quality predicted all measured sensory texture attributes (except Tooth Packing) from instrumental measurements. Modeling also identified that consumer preference is for a moist, smooth texture. Lastly, while astringency type has significant (p < 0.05) effects on several individual texture attributes, astringency type should not be used a priori to screen-in or -out persimmon cultivars for processing into a hot-air-dried product.

Nanovibrational Stimulation of Mesenchymal Stem Cells Induces Therapeutic Reactive Oxygen Species and Inflammation for Three-Dimensional Bone Tissue Engineering

There is a pressing clinical need to develop cell-based bone therapies due to a lack of viable, autologous bone grafts and a growing demand for bone grafts in musculoskeletal surgery. Such therapies can be tissue engineered and cellular, such as osteoblasts, combined with a material scaffold. Because mesenchymal stem cells (MSCs) are both available and fast growing compared to mature osteoblasts, therapies that utilize these progenitor cells are particularly promising. We have developed a nanovibrational bioreactor that can convert MSCs into bone-forming osteoblasts in two- and three-dimensional, but the mechanisms involved in this osteoinduction process remain unclear. Here, to elucidate this mechanism, we use increasing vibrational amplitude, from 30 nm (N30) to 90 nm (N90) amplitudes at 1000 Hz and assess MSC metabolite, gene, and protein changes. These approaches reveal that dose-dependent changes occur in MSCs' responses to increased vibrational amplitude, particularly in adhesion and mechanosensitive ion channel expression and that energetic metabolic pathways are activated, leading to low-level reactive oxygen species (ROS) production and to low-level inflammation as well as to ROS- and inflammation-balancing pathways. These events are analogous to those that occur in the natural bone-healing processes. We have also developed a tissue engineered MSC-laden scaffold designed using cells' mechanical memory, driven by the stronger N90 stimulation. These mechanistic insights and cell-scaffold design are underpinned by a process that is free of inductive chemicals.

Transcriptomic and Metabolic Analyses Provide New Insights into the Apple Fruit Quality Decline during Long-Term Cold Storage

Long-term low-temperature conditioning (LT-LTC) decreases apple fruit quality, but the underlying physiological and molecular basis is relatively uncharacterized. We identified 12 clusters of differentially expressed genes (DEGs) involved in multiple biological processes (i.e., sugar, malic acid, fatty acid, lipid, complex phytohormone, and stress-response pathways). The expression levels of genes in sugar pathways were correlated with decreasing starch levels during LT-LTC. Specifically, starch-synthesis-related genes (e.g., BE, SBE, and GBSS genes) exhibited downregulated expression, whereas sucrose-metabolism-related gene expression levels were up- or downregulated. The expression levels of genes in the malic acid pathway (ALMT9, AATP1, and AHA2) were upregulated, as well as the content of malic acid in apple fruit during LT-LTC. A total of 151 metabolites, mainly related to amino acids and their isoforms, amines, organic acids, fatty acids, sugars, and polyols, were identified during LT-LTC. Additionally, 35 organic-acid-related metabolites grouped into three clusters, I (3), II (22), and III (10), increased in abundance during LT-LTC. Multiple phytohormones regulated the apple fruit chilling injury response. The ethylene (ET) and abscisic acid (ABA) levels increased at CS2 and CS3, and jasmonate (JA) levels also increased during LT-LTC. Furthermore, the expression levels of genes involved in ET, ABA, and JA synthesis and response pathways were upregulated. Finally, some key transcription factor genes (MYB, bHLH, ERF, NAC, and bZIP genes) related to the apple fruit cold acclimation response were differentially expressed. Our results suggest that the multilayered mechanism underlying apple fruit deterioration during LT-LTC is a complex, transcriptionally regulated process involving cell structures, sugars, lipids, hormones, and transcription factors.

A comparison of food crispness based on the cloud model

The cloud model is a typical model which transforms the qualitative concept into the quantitative description. The cloud model has been used less extensively in texture studies before. The purpose of this study was to apply the cloud model in food crispness comparison. The acoustic signals of carrots, white radishes, potatoes, Fuji apples, and crystal pears were recorded during compression. And three time-domain signal characteristics were extracted, including sound intensity, maximum short-time frame energy, and waveform index. The three signal characteristics and the cloud model were used to compare the crispness of the samples mentioned above. The crispness based on the Ex value of the cloud model, in a descending order, was carrot > potato > white radish > Fuji apple > crystal pear. To verify the results of the acoustic signals, mechanical measurement and sensory evaluation were conducted. The results of the two verification experiments confirmed the feasibility of the cloud model. The microstructures of the five samples were also analyzed. The microstructure parameters were negatively related with crispness (p < .01).

PRACTICAL APPLICATIONS

The cloud model method can be used for crispness comparison of different kinds of foods. The method is more accurate than the traditional methods such as mechanical measurement and sensory evaluation. The cloud model method can also be applied to other texture studies extensively.