Denitrification, a key process in soil nitrogen cycling, occurs predominantly within microbial hotspots, such as those around particulate organic matter (POM), where denitrifiers use nitrate as an alternative electron acceptor. For accurate prediction of dinitrogen (N2) and nitrous oxide (N2O) emissions from denitrification, a precise quantification of these microscale hotspots is required. The distribution of POM is of crucial importance in this context, as the local oxygen (O2) balance is governed not only by its high O2 demand but also by the local O2 availability. Employing a unique combination of X-ray CT imaging, microscale O2 measurements, and 15N labeling, we were able to quantify hotspots of aerobic respiration and denitrification. We analyzed greenhouse gas (GHG) fluxes, soil oxygen supply, and the distribution of POM in intact soil samples from grassland and cropland under different moisture conditions. Our findings reveal that both proximal and distal POM, identified through X-ray CT imaging, contribute to GHG emissions. The distal POM, i.e. POM at distant locations to air-filled pores, emerged as a primary driver of denitrification within structured soils of both land uses. Thus, the higher denitrification rates in the grassland could be attributed to the higher content of distal POM. Conversely, despite possessing compacted areas that could favor denitrification, the cropland had only small amounts of distal POM to stimulate denitrification in it. This underlines the complex interaction between soil structural heterogeneity, organic carbon supply, and microbial hotspot formation and thus contributes to a better understanding of soil-related GHG emissions. In summary, our study provides a holistic understanding of soil-borne greenhouse gas emissions and emphasizes the need to refine predictive models for soil denitrification and N2O emissions by incorporating the microscale distribution of POM.

Ice cream is a multiphase frozen dessert that often melts during distribution and upon consumption. The meltdown phenomenon is one of the concerns in the quality preservation of ice cream for consumer convenience in the frozen food industry. In this context, X-ray tomography was used to visualise and quantify 3D ice crystal and air bubble evolution during the meltdown of ice cream. Two ice cream products, namely I and II, with varying air volume fractions, were evaluated for this study. The results indicated a small mean diameter of 66.43 ± 2.07 µm at 0 min and decreased to 45.74 ± 3.92 µm during 10 min of the meltdown of ice cream II. A large mean diameter of ice crystals of 75.02 ± 3.14 µm was found in ice cream I, at 0 min that decreased significantly (p < 0.05) to 54.30 ± 2.63 µm during 10 min of the meltdown. The air bubbles were also observed to decrease in mean diameter. The 3D datasets on the ice crystals and air bubbles described in this work provide more insight into the 3D microstructural evolution during the meltdown and are useful in controlling the sensory quality attributes of ice cream desserts.

OBJECTIVE: Root system architecture (RSA) plays a key role in plant adaptation to drought because deep rooting enables better water uptake than shallow rooting under terminal drought. Understanding RSA during early plant development is essential for improving crop yields, as early drought can affect subsequent shoot growth. Herein, we demonstrate that root distribution in the topsoil significantly impacts shoot growth during the early stages of rice (Oryza sativa) development under drought, as assessed through three-dimensional (3D) image analysis.
METHODS: We used 109 F12 recombinant inbred lines (RILs) obtained from a cross between shallow-rooting lowland rice and deep-rooting upland rice, representing a population with diverse RSA. We applied a moderate drought during the early development of rice grown in a plant pot (25 cm height) by stopping irrigation 14 days after sowing (DAS). Time-series RSA at 14, 21, and 28 DAS was visualized by X-ray computed tomography, and subsequently compared between drought and well-watered conditions. Following this analysis, we further investigated drought-avoidant RSA by testing 20 randomly selected RILs under drought conditions.
RESULTS: We inferred the root location that most influences shoot growth using a hierarchical Bayes approach: the root segment depth, which positively impacted shoot growth, ranged between 1.7-3.4 cm under drought conditions and between 0.0-1.7 cm under well-watered conditions. Drought-avoidant RILs had a higher root density in the lower layers of the topsoil compared to the others.
CONCLUSIONS: Fine classification of soil layers using 3D image analysis revealed that increasing root density in the lower layers of the topsoil, rather than in the subsoil, is advantageous for drought avoidance during the early growth stage of rice.

This study investigates utilizing spherical polystyrene (PS) beads as artificial flaws to improve ultrahigh-performance concrete (UHPC) tensile performance using a uniaxial tensile test and explains the corresponding mechanisms by analyzing the internal material structure of UHPC specimens with X-ray CT scanning. With a hooked steel fiber volume fraction of 2%, three PS bead dosages were employed to study tensile behavior changes in dog-bone UHPC specimens. A 33.4% increase in ultimate tensile strength and 174.8% increase in ultimate tensile strain were recorded after adding PS beads with a volume fraction of 2%. To explain this improvement, X-ray CT scanning was utilized to investigate the post-test internal material structures of the dog-bone specimens. AVIZO software was used to analyze the CT information. The CT results revealed that PS beads could not only serve as the artificial flaws to increase the cracking behavior of the matrix of UHPC but also significantly optimize the fiber orientation. The PS beads could serve as stirrers during the mixing process to distribute fiber more uniformly. The test results indicate a relationship between fiber orientation and UHPC tensile strength.

While many studies have examined the role of biochar in carbon (C) accrual in short-term scale, few have explored the decadal scale influences of biochar on non-biochar C, e.g., native soil organic C (SOC) and added substrate. To address this knowledge gap, soils were collected from decade-old biochar field trials located in the United Kingdom (Cambisol) and China (Fluvisol), with each site having had three application rates (25-30, 50-60 and 75-100 Mg ha-1) of biochar plus an unamended Control, applied once in 2009. We assessed physicochemical and microbial properties associated with sucrose (representing the rhizodeposits) mineralization and the priming effect (PE) on native SOC. Here, we showed both soils amended with biochar at the middle application rate (50 Mg ha-1 biochar in Cambisol and 60 Mg ha-1 biochar in Fluvisol) resulted in greater substrate mineralization. The enhanced accessibility and availability of sucrose to microorganisms, particularly fast-growing bacterial genera like Arenimonas, Spingomonas, and Paenibacillus (r-strategists belonging to the Proteobacteria and Firmicutes phyla, respectively), can be attributed to the improved physicochemical properties of the soil, including pH, porosity, and pore connectivity, as revealed by synchrotron-based micro-CT. Random forest analysis also confirmed the contribution of the microbial diversity and physical properties such as porosity on sucrose mineralization. Biochar at the middle application rate, however, resulted in the lowest PE (0.3 and 0.4 mg of CO2-C g soil-1 in Cambisol and Fluvisol, respectively) after 53 days of incubation. This result might be associated with the fact that the biochar promoted large aggregates formation, which enclosed native SOC in soil macro-aggregates (2-0.25 mm). Our study revealed a diverging pattern between substrate mineralization and SOC priming linked to the biochar application rate. This suggests distinct mechanisms, biophysical and physicochemical, driving the mineralization of non-biochar carbon in a field where biochar was applied a decade before.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s42773-024-00327-0.

We have demonstrated a quantification of all component wires in a bent electric cable, which is necessary for discussion of cable products in actual use cases. Quantification became possible for the first time because of our new technologies for image analysis of bent cables. In this paper, various image analysis techniques to detect all wire tracks in a bent cable are demonstrated. Unique cross-sectional image construction and deep active learning schemes are the most important items in this study. These methods allow us to know the actual state of cables under external loads, which makes it possible to elucidate the mechanisms of various phenomena related to cables in the field and further improve the quality of cable products.

BACKGROUND: X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes.
RESULTS: VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data.
CONCLUSIONS: A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.

Aluminum foam has relatively low tensile and flexural strengths because it is composed of many pores with thin cell walls. One method of strengthening aluminum foam is to fabricate a composite material with a dense lightweight resin. In this study, the fabrication of composite materials by directly printing resin on an aluminum foam surface using a 3D printer was attempted. The resin was directly printed on both heated and unheated aluminum foam. It was shown that composite materials consisting of aluminum foam and resin can be fabricated by directly printing resin with a 3D printer on both heated and unheated aluminum foam. The resin was softened during the printing process in the case of directly printed resin on heated aluminum foam, allowing more resin to penetrate into the pores than in the case of directly printed resin on unheated aluminum foam. In addition, it was shown that resin can be directly printed on the aluminum foam with a high bonding strength, as a large amount of resin penetrated into the pores, resulting in an anchor effect. That is, composite materials consisting of aluminum foam and arbitrary-shaped resin with relatively high bonding strength can be fabricated when a large amount of resin is allowed to penetrate into the pore.

This study utilized X-ray computed tomography (CT) technology to analyze the meso-structure of concrete at different replacement rates, using a coal gangue coarse aggregate, after experiencing various freeze-thaw cycles (F-Ts). A predictive model for the degradation of the elastic modulus of Coal Gangue coarse aggregate Concrete (CGC), based on mesoscopic damage, was established to provide an interpretation of the macroscopic mechanical behavior of CGC after F-Ts damage at a mesoscopic scale. It was found that after F-Ts, the compressive strength of concrete, with coal gangue replacement rates of 30%, 60%, and 100%, respectively, decreased by 33.76%, 34.89%, and 42.05% compared with unfrozen specimens. The results indicate that an increase in the coal gangue replacement rate exacerbates the degradation of concrete performance during the F-Ts process. Furthermore, the established predictive formula for elastic modulus degradation closely matches the experimental data, offering a reliable theoretical basis for the durability design of CGC in F-Ts environments.

Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and artificial intelligence-based image processing that allowed a non-destructive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine (Vitis vinifera L.). Vineyard\'s sustainability is threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key-measurements in evaluating vines\' sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of tissue quality and plant health across plant species.