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Image-guided liver surgery aims to enhance the precision of resection and ablation by providing fast localisation of tumours and adjacent complex vasculature to improve oncologic outcome. This Letter presents a novel end-to-end solution for fast stereo reconstruction and motion estimation that demonstrates high accuracy with phantom and clinical data. The authors’ computationally efficient coarse-to-fine (CTF) stereo approach facilitates liver imaging by accounting for low texture regions, enabling precise three-dimensional (3D) boundary recovery through the use of adaptive windows and utilising a robust 3D motion estimator to reject spurious data. To the best of their knowledge, theirs is the only adaptive CTF matching approach to reconstruction and motion estimation that registers time series of reconstructions to a single key frame for registration to a volumetric computed tomography scan. The system is evaluated empirically in controlled laboratory experiments with a liver phantom and motorised stages for precise quantitative evaluation. Additional evaluation is provided through testing with patient data during liver resection.

Coffee, cashew and avocado are of high socio-economic importance in many tropical smallholder farming systems around the globe. As plantation crops with a long lifespan, their cultivation requires long-term planning. The evaluation of climate change impacts on their biophysical suitability is therefore essential for developing adaptation measures and selecting appropriate varieties or crops. In this study, we modelled the current and future suitability of coffee arabica, cashew and avocado on a global scale based on climatic and soil requirements of the three crops. We used climate outputs of 14 global circulation models based on three emission scenarios to model the future (2050) climate change impacts on the crops both globally and in the main producing countries. For all three crops, climatic factors, mainly long dry seasons, mean temperatures (high and low), low minimum temperatures and annual precipitation (high and low), were more restrictive for the global extent of suitable growing regions than land and soil parameters, which were primarily low soil pH, unfavourable soil texture and steep slopes. We found shifts in suitable growing regions due to climate change with both regions of future expansion and contraction for all crops investigated. Coffee proved to be most vulnerable, with negative climate impacts dominating in all main producing regions. For both cashew and avocado, areas suitable for cultivation are expected to expand globally while in most main producing countries, the areas of highest suitability may decrease. The study reveals that climate change adaptation will be necessary in most major producing regions of all three crops. At high latitudes and high altitudes, however, they may all profit from increasing minimum temperatures. The study presents the first global assessment of climate change impacts on cashew and avocado suitability.

Soil texture data are the basic input parameters for many Earth System Models. As the largest middle–low altitude permafrost regions on the planet, the land surface processes on the Qinghai–Tibet Plateau can affect regional and even global water and energy cycles. However, the spatial distribution of soil texture data on the plateau is largely unavailable due to the difficulty of obtaining field data. Based on collection data from field surveys and environmental factors, we predicted the spatial distribution of clay, silt, and sand contents at a 1 km resolution, from 0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm soil depth layers. The random forest models were constructed to predict the soil texture according to the relationships between environmental factors and soil texture data. The results showed that the soil particles of the QTP are dominated by sand, which accounts for more than 70% of the total particles. As for the spatial distribution, silt and clay contents are high in the southeast plateau, and low values of silt and clay mainly appeared in the northwest plateau. Climate and NDVI values are the most important factors that affect the spatial distribution of soil texture on the QTP. The results of this study provide the soil texture data at different depths for the whole plateau at a spatial resolution of 1 km, and the dataset can be used as an input parameter for many Earth System Models.

PurposeThe aim of this study was to determine whether MRI texture analysis could predict the prognosis of patients with non-specific chronic low back pain.MethodsA prospective observational study was conducted on 100 patients with non-specific chronic low back pain, who underwent a conventional MRI, followed by rehabilitation treatment, and revisited after 6 months. Sociodemographic variables, numeric pain scale (NPS) value, and the degree of disability as measured by the Roland–Morris disability questionnaire (RMDQ), were collected. The MRI analysis included segmentation of regions of interest (vertebral endplates and intervertebral disks from L3–L4 to L5–S1, paravertebral musculature at the L4–L5 space) to extract texture variables (PyRadiomics software). The classification random forest algorithm was applied to identify individuals who would improve less than 30% in the NPS or would score more than 4 in the RMDQ at the end of the follow-up. Sensitivity, specificity, and the area under the ROC curve were calculated.ResultsThe final series included 94 patients. The predictive model for classifying patients whose pain did not improve by 30% or more offered a sensitivity of 0.86, specificity 0.57, and area under the ROC curve 0.71. The predictive model for classifying patients with a RMDQ score 4 or more offered a sensitivity of 0.83, specificity of 0.20, and area under the ROC curve of 0.52.ConclusionThe texture analysis of lumbar MRI could help identify patients who are more likely to improve their non-specific chronic low back pain through rehabilitation programs, allowing a personalized therapeutic plan to be established.

Net ecosystem productivity (NEP) serves as a key indicator for assessing regional carbon sink potential, with its dynamics regulated by nonlinear interactions among multiple factors. However, its driving factors and their coupling processes remain insufficiently characterized. This study investigated terrestrial ecosystems in Yunnan Province, China, to elucidate the drivers of NEP using 14 environmental factors (including topography, meteorology, soil texture, and human activities) and 21 remote sensing features. We developed a research framework based on “Feature Selection–Machine Learning–Mechanism Interpretation.” The results demonstrated that the Variable Selection Using Random Forests (VSURF) feature selection method effectively reduced model complexity. The selected features achieved high estimation accuracy across three machine learning models, with the eXtreme Gradient Boosting Regression (XGBR) model performing optimally (R2 = 0.94, RMSE = 76.82 gC/(m2·a), MAE = 55.11 gC/(m2·a)). Interpretation analysis using the SHAP (SHapley Additive exPlanations) method revealed the following: (1) The Enhanced Vegetation Index (EVI), soil pH, solar radiation, air temperature, clay content, precipitation, sand content, and vegetation type were the primary drivers of NEP in Yunnan. Notably, EVI’s importance exceeded that of other factors by approximately 3 to 10 times. (2) Significant interactions existed between soil texture and temperature: Under low-temperature conditions (−5 °C to 12.15 °C), moderate clay content (13–25%) combined with high sand content (40–55%) suppressed NEP. Conversely, within the medium to high temperature range (5 °C to 23.79 °C), high clay content (25–40%) coupled with low sand content (25–43%) enhanced NEP. These findings elucidate the complex driving mechanisms of NEP in subtropical ecosystems, confirming the dominant role of EVI in carbon sequestration and revealing nonlinear regulatory patterns in soil–temperature interactions. This study provides not only a robust “Feature Selection–Machine Learning–Mechanism Interpretation” modeling framework for assessing carbon budgets in mountainous regions but also a scientific basis for formulating regional carbon management policies.

One of the most effective systems for managing water is subsurface trickle irrigation. Finding empirical formulas and studying the effect of soil texture are the main purposes of this paper. In order to reach an ideal irrigation system as a modern technique to save water, especially in arid regions, soil textures of loam, silt, and silt loam were studied on a subsurface trickle irrigation system by utilizing HYDRUS/2D. The trickle system is usually operated at low pressure, in this paper the used pressure is 30 cm with an emitter buried at 10, 15, and 20 cm at different diameters. Patterns of wetting fronts in both directions at various times depending on soil texture are gathered to find out empirical formulas that are a function of operative time, emitter diameter, depth of buried, and finally, saturated hydraulic conductivity. The statistical parameters make it evident that the formulas provide reasonable prediction accuracy in both dimensions wetted width & depth. The percentage of mean relative error is less than 3%, and the coefficient of determination of both dimensions is more than 0.990. Moreover, the simulation results of this paper are compared with field experiments and based on relative error to find a precise distribution of water around a trickle.

Summary Salvia miltiorrhiza Bunge, a renowned medicinal herb in traditional Chinese medicine, displays distinctive root texture and high phenolic acid content, traits influenced by genetic and environmental factors. However, the underlying regulatory networks remain unclear. Here, we performed multi‐omics analyses on ecotypes from four major Chinese regions, focusing on environmental impacts on root structure, phenolic acid accumulation and lignin composition. Lower temperatures and increased UV‐B radiation were associated with elevated rosmarinic acid (RA) and salvianolic acid B (SAB) levels, particularly in the Sichuan ecotype. Structural models indicated that the radial arrangement of xylem conduits contributes to greater root hardness. Genomic assembly and comparative analysis of the Sichuan ecotype revealed a unique phenolic acid metabolism gene cluster, including SmWRKY40, a WRKY transcription factor essential for RA and SAB biosynthesis. Overexpression of SmWRKY40 enhanced phenolic acid levels and lignin content, whereas its knockout reduced root hardness. Integrating high‐throughput (DNA affinity purification sequencing) and point‐to‐point (Yeast One‐Hybrid, Dual‐Luciferase and Electrophoretic Mobility Shift Assay) protein‐DNA interaction detection platform further identified SmWRKY40 binding sites across ecotypes, revealing specific regulatory networks. Our findings provide insights into the molecular basis of root texture and bioactive compound accumulation, advancing breeding strategies for quality improvement in S. miltiorrhiza.

Soil bacteria known as methanotrophs are the sole biological sink for atmospheric methane (CH4), a potent greenhouse gas that is responsible for ∼ 20 % of the human-driven increase in radiative forcing since pre-industrial times. Soil methanotrophy is controlled by a plethora of factors, including temperature, soil texture, moisture and nitrogen content, resulting in spatially and temporally heterogeneous rates of soil methanotrophy. As a consequence, the exact magnitude of the global soil sink, as well as its temporal and spatial variability, remains poorly constrained. We developed a process-based model (Methanotrophy Model; MeMo v1.0) to simulate and quantify the uptake of atmospheric CH4 by soils at the global scale. MeMo builds on previous models by Ridgwell et al. (1999) and Curry (2007) by introducing several advances, including (1) a general analytical solution of the one-dimensional diffusion–reaction equation in porous media, (2) a refined representation of nitrogen inhibition on soil methanotrophy, (3) updated factors governing the influence of soil moisture and temperature on CH4 oxidation rates and (4) the ability to evaluate the impact of autochthonous soil CH4 sources on uptake of atmosphericCH4. We show that the improved structural and parametric representation of key drivers of soil methanotrophy in MeMo results in a better fit to observational data. A global simulation of soil methanotrophy for the period 1990–2009 using MeMo yielded an average annual sink of 33.5 ± 0.6 Tg CH4 yr-1. Warm and semi-arid regions (tropical deciduous forest and open shrubland) had the highest CH4 uptake rates of 602 and 518 mg CH4 m-2 yr-1, respectively. In these regions, favourable annual soil moisture content (∼ 20 % saturation) and low seasonal temperature variations (variations < ∼ 6 ∘C) provided optimal conditions for soil methanotrophy and soil–atmosphere gas exchange. In contrast to previous model analyses, but in agreement with recent observational data, MeMo predicted low fluxes in wet tropical regions because of refinements in formulation of the influence of excess soil moisture on methanotrophy. Tundra and mixed forest had the lowest simulated CH4 uptake rates of 176 and 182 mg CH4 m-2 yr-1, respectively, due to their marked seasonality driven by temperature. Global soil uptake of atmospheric CH4 was decreased by 4 % by the effect of nitrogen inputs to the system; however, the direct addition of fertilizers attenuated the flux by 72 % in regions with high agricultural intensity (i.e. China, India and Europe) and by 4–10 % in agriculture areas receiving low rates of N input (e.g. South America). Globally, nitrogen inputs reduced soil uptake of atmosphericCH4 by 1.38 Tg yr-1, which is 2–5 times smaller than reported previously. In addition to improved characterization of the contemporary soil sink for atmospheric CH4, MeMo provides an opportunity to quantify more accurately the relative importance of soil methanotrophy in the global CH4 cycle in the past and its capacity to contribute to reduction of atmospheric CH4 levels under future global change scenarios.

PurposeThe aims: (1) to investigate the role of the in situ weathering of bedrock in providing substrate for soil formation; (2) to evaluate the aeolian contribution to the mountainous soils in the vicinity of thick loess cover; and (3) to determine the influence of aeolian silt on further soil development.Materials and methodsThe sampled sites were arranged along the slope toposequence, where an aeolian/silt admixture possibly occurred. Each soil catena started at the top of a hill and ended at its foot. Such an arrangement of the soil profiles ensured the tracking of loess thickness variations and detection of the depth of the residuum-derived materials. One reference soil profile, consisting of aeolian silt deposits, was made. The following soil properties were determined: pH, organic carbon content, soil texture, exchangeable acidity, exchangeable ions and geochemistry. In addition, thin sections were prepared from rock samples to confirm the type of bedrock present.Results and discussionThe soils in the studied area were classified as Cambisols, Luvisols and Stagnosols, characterised by silt loam texture and a high content of elements indicating an aeolian silt contribution—Hf (7.4 to 14.8 ppm) and Zr (274.4 to 549.0 ppm). These values differ strongly from the residues typical of weathered quartzite, greywacke or catalasite substrates, which generally have low concentrations of Hf and Zr (0.7 to 7.0 ppm and 26.0 to 263 ppm, respectively). Based on the morphological, textural and geochemical data of the studied soils, three layers were distinguished, which show different inputs of aeolian silt: (1) an aeolian silt mantle; (2) a mixed zone in which loess was incorporated into the local material; and (3) a basal zone, free of the influence of aeolian silt. Based on the obtained results, a hypothetical pathway for soil formation in mountainous areas, influenced by aeolian silt admixing, was proposed.ConclusionsOur study demonstrates that the soils developed in the Opawskie Mountains are characterised by an aeolian silt influence. This differentiates them from weakly developed soils, which comprise materials formed during in situ weathering only. Materials originating from bedrock weathering did not play an independent role as the parent material for the studied pedons. Aeolian silt was admixed with already existing autochthonous substrates, or completely replaced them. This influence on the soil formation resulted in the occurrence of Luvisols, Stagnosols and Cambisols. Such soils cannot be formed from the weathering of quartzites and greywackes, which contribute to a less structure-forming medium.

This study investigated the impacts of a drying process under low temperature and reduced pressure (non-thermal drying) on the final dehydrated products characteristics. This process is based on the retention of water on molecular sieves with a good selectivity against these molecules. In this study, drying experiments of 7mm thick apple slices (AS) were performed and compared to apple slices pretreated by freezing. It was concluded that the dehydrated apple slices were depleted of the maximum amount of water after 12 hours of drying, with a final water content equal to 12 ± 1.75%, whereas after freezing pretreatment, a decrease in drying time to 7 hours was observed, as well as a decrease in water content to 10 ± 0.5%. This explains the effect of freezing pretreatment on accelerating water transfer. In addition, a convective drying was performed on the apple slices at 60°C, which allows comparison with the slices dried by our non-thermal drying process. In order to characterize the obtained fruits, characteristic analyses such as water activity (Aw), color, texture (hardness), and dimensions (diameter and thickness) were performed before and after each drying experiment. Thus, continuous measurements of temperature, humidity, and pressure, within the enclosure, were determined during the experiments using a wireless sensor system controlled by a programming Arduino. Finally, mathematical modeling by various models (Newton, Page, Midilli, etc.) was performed to determine the most suitable model describing the non-thermal and convective drying of apple slices.