Development of porous structure for broadening Bragg-peak in scanning carbon-ion radiotherapy: Monte Carlo simulation and experimental validation.

PURPOSE: The present study aimed to develop a porous structure with plug-ins (PSP) to broaden the Bragg peak width (BPW, defined as the distance in water between the proximal and distal 80% dose) of the carbon ion beam while maintaining a sharp distal falloff width (DFW, defined as the distance along the beam axis where the dose in water reduces from 80% to 20%). METHODS: The binary voxel models of porous structure (PS) and PSP were established in the Monte Carlo code FLUKA and the corresponding physical models were manufactured by 3D printing. Both experiment and simulation were performed for evaluating the modulation capacity of PS and PSP. BPWs and DFWs derived from each integral depth dose curves were compared. Fluence homogeneity of 430 MeV/u carbon-ion beam passing through the PSP was recorded by analyzing radiochromic films at six different locations downstream the PSP in the experiment. Additionally, by changing the beam spot size and incident position on the PSP, totally 48 different carbon-ion beams were simulated and corresponding deviations of beam metrics were evaluated to test the modulating stability of PSP. RESULTS: According to the measurement data, the use of PSP resulted in an average increase of 0.63 mm in BPW and a decrease of 0.74 mm in DFW compared to PS. The 2D radiation field inhomogeneities were lower than 3 % when the beam passing through a ≥ 10 cm PMMA medium. Furthermore, employing a spot size of ≥ 6 mm ensures that beam metric deviations, including BPW, DFW, and range, remain within a deviation of 0.1 mm across various incident positions. CONCLUSION: The developed PSP demonstrated its capability to effectively broaden the BPW of carbon ion beams while maintaining a sharp DFW comparing to PS. The superior performance of PSP, indicates its potential for clinical use in the future.

Botulinum Toxin to Improve the Prognosis of Skin Transplantation: A Short Narrative Review.

Skin transplantation is a traditional and well-established method of repairing skin loss, especially deep second-degree postburn wounds. Complications often happen amid the healing process, including necrosis and skin contracture, which has raised widespread concern from patients and doctors. Since the first recorded medical application of botulinum toxin for strabismus, accumulating evidence has enclosed all-round potential of botulinum toxin, more than aesthetic management. In recent decades, botulinum toxin also has been revealed to improve the prognosis of skin grafts. This literature review aims to briefly summarize the history and latest advances of its use for skin transplantation.

Inter-fractional portability of deep learning models for lung target tracking on cine imaging acquired in MRI-guided radiotherapy.

MRI-guided radiotherapy systems enable beam gating by tracking the target on planar, two-dimensional cine images acquired during treatment. This study aims to evaluate how deep-learning (DL) models for target tracking that are trained on data from one fraction can be translated to subsequent fractions. Cine images were acquired for six patients treated on an MRI-guided radiotherapy platform (MRIdian, Viewray Inc.) with an onboard 0.35 T MRI scanner. Three DL models (U-net, attention U-net and nested U-net) for target tracking were trained using two training strategies: (1) uniform training using data obtained only from the first fraction with testing performed on data from subsequent fractions and (2) adaptive training in which training was updated each fraction by adding 20 samples from the current fraction with testing performed on the remaining images from that fraction. Tracking performance was compared between algorithms, models and training strategies by evaluating the Dice similarity coefficient (DSC) and 95% Hausdorff Distance (HD95) between automatically generated and manually specified contours. The mean DSC for all six patients in comparing manual contours and contours generated by the onboard algorithm (OBT) were 0.68 ± 0.16. Compared to OBT, the DSC values improved 17.0 - 19.3% for the three DL models with uniform training, and 24.7 - 25.7% for the models based on adaptive training. The HD95 values improved 50.6 - 54.5% for the models based on adaptive training. DL-based techniques achieved better tracking performance than the onboard, registration-based tracking approach. DL-based tracking performance improved when implementing an adaptive strategy that augments training data fraction-by-fraction.

Interplay between edaphic and climatic factors unravels plant and microbial diversity along an altitudinal gradient.

Altitude influences biodiversity and physiochemical soil attributes in terrestrial ecosystems. It is of immense importance to know the patterns of how interactions among climatic and edaphic factors influence plant and microbial diversity in various ecosystems, particularly along the gradients. We hypothesize that altitudinal variation determines the distribution of plant and microbial species as well as their interactions. To test the hypothesis, different sites with variable altitudes were selected. Analyses of edaphic factors revealed significant (p < 0.001) effects of the altitude. Soil ammonium and nitrate were strongly affected by it contrary to potassium (K), soil organic matter and carbon. The response patterns of individual taxonomic groups differed across the altitudinal gradient. Plant species and soil fungal diversity increased with increasing altitude, while soil archaeal and bacterial diversity decreased with increasing altitude. Plant species richness showed significant positive and negative interactions with edaphic and climatic factors. Fungal species richness was also significantly influenced by the soil ammonium, nitrate, available phosphorus, available potassium, electrical conductivity, and the pH of the soil, but showed non-significant interactions with other edaphic factors. Similarly, soil variables had limited impact on soil bacterial and archaeal species richness along the altitude gradient. Proteobacteria, Ascomycota, and Thaumarchaeota dominate soil bacterial, fungal, and archaeal communities, with relative abundance of 27.4%, 70.56%, and 81.55%, respectively. Additionally, Cynodon dactylon is most abundant plant species, comprising 22.33% of the recorded plant taxa in various study sites. RDA revealed that these communities influenced by certain edaphic and climatic factors, e.g., Actinobacteria strongly respond to MAT, EC, and C/N ratio, Ascomycota and Basidiomycota show strong associations with EC and MAP, respectively. Thaumarcheota are linked to pH, and OM, while Cyperus rotundus are sensitive to AI and EC. In conclusion, the observed variations in microbial as well as plant species richness and changes in soil properties at different elevations provide valuable insights into the factors determining ecosystem stability and multifunctionality in different regions.

Heavy metal contamination impacts the structure and co-occurrence patterns of bacterial communities in agricultural soils.

Heavy metal (HM) contamination caused by mining and smelting activities can be harmful to soil microbiota, which are highly sensitive to HM stress. Here, we explore the effects of HM contamination on the taxonomic composition, predicted function, and co-occurrence patterns of soil bacterial communities in two agricultural fields with contrasting levels of soil HMs (i.e., contaminated and uncontaminated natural areas). Our results indicate that HM contamination does not significantly influence soil bacterial α diversity but changes the bacterial community composition by enriching the phyla Gemmatimonadetes, Planctomycetes, and Parcubacteria and reducing the relative abundance of Actinobacteria. Our results further demonstrate that HM contamination can strengthen the complexity and modularity of the bacterial co-occurrence network but weaken positive interactions between keystone taxa, leading to the gradual disappearance of some taxa that originally played an important role in healthy soil, thereby possibly reducing the resistance of bacterial communities to HM toxicity. The predicted functions of bacterial communities are related to membrane transport, amino acid metabolism, energy metabolism, and carbohydrate metabolism. Among these, functions related to HM detoxification and antioxidation are enriched in uncontaminated soils, while HM contamination enriches functions related to metal resistance. This study demonstrated that microorganisms adapt to the stress of HM pollution by adjusting their composition and enhancing their network complexity and potential ecological functions.

Effects of long-term metal exposure on the structure and co-occurrence patterns of the oral microbiota of residents around a mining area.

Objectives: The aim of our study was to investigate the impact of long-term exposure to heavy metals on the microbiome of the buccal mucosa, to unveil the link between environmental contamination and the oral microbial ecosystem, and to comprehend its potential health implications. Methods: Subjects were divided into two groups: the exposure group and the control group. We collected samples of buccal mucosa, soil, and blood, and conducted microbial diversity analysis on both groups of oral samples using 16S rRNA gene sequencing. The concentrations of heavy metals in blood and soil samples were also determined. Additionally, microbial networks were constructed for the purpose of topological analysis. Results: Due to long-term exposure to heavy metals, the relative abundance of Rhodococcus, Delftia, Fusobacterium, and Peptostreptococcus increased, while the abundance of Streptococcus, Gemella, Prevotella, Granulicatella, and Porphyromonas decreased. The concentrations of heavy metals in the blood (Pb, Cd, Hg, and Mo) were associated with the growth of Rhodococcus, Delftia, Porphyromonas, and Gemella. In addition, the relative abundances of some pathogenic bacteria, such as Streptococcus anginosus, S. gordonii, and S. mutans, were found to be enriched in the exposure group. Compared to the exposure group network, the control group network had a greater number of nodes, modules, interactive species, and keystone taxa. Module hubs and connectors in the control group converted into peripherals in the exposure group, indicating that keystone taxa changed. Metals in the blood (Pb, Cd, Hg, and Mo) were drivers of the microbial network of the buccal mucosa, which can have adverse effects on the network, thus providing conditions for the occurrence of certain diseases. Conclusion: Long-term exposure to multiple metals perturbs normal bacterial communities in the buccal mucosa of residents in contaminated areas. This exposure reduces the complexity and stability of the microbial network and increases the risk of developing various diseases.

Implementation and evaluation of an iterative-based algorithm for automatic beam angle optimization in breast cancer treatment planning.

INTRODUCTION: A beam angle optimization (BAO) algorithm was developed to evaluate its clinical feasibility and investigate the impact of varying BAO constraints on breast cancer treatment plans. MATERIALS AND METHODS: A two-part study was designed. In part 1, we retrospectively selected 20 patients treated with radiotherapy after breast-conserving surgery. For each patient, BAO plans were designed using beam angles optimized by the BAO algorithm and the same optimization constraints as manual plans. Dosimetric indices were compared between BAO and manual plans. In part 2, fifteen patients with left breast cancer were included. For each patient, three distinct cardiac constraints (mean heart dose < 5 Gy, 3 Gy or 1 Gy) were established during the BAO process to obtain three optimized beam sets which were marked as BAO_H1, BAO_H3, BAO_H5, respectively. These sets of beams were then utilized under identical IMRT constraints for planning. Comparative analysis was conducted among the three groups of plans. RESULTS: For part 1, no significant differences were observed between BAO plans and manual plans in all dosimetric indices, except for ipsilateral lung V5, where BAO plans performed slightly better than manual plans (35.5% ± 5.6% vs 36.9% ± 4.3%, p = 0.034). For part 2, Stricter BAO heart constraints resulted in more perpendicular beams. However, there was no significant difference between BAO_H1, BAO_H3 and BAO_H5 with the same IMRT constraint in the heart dose. Meanwhile, the left lung dose was increased while the right breast and lung doses were decreased with stricter heart constraints in BAO. When mean heart dose < 5 Gy in IMRT constraint, the mean dose to the right lung was decreased from 0.46 Gy for BAO_H5 to 0.33 Gy for BAO_H1 (p = 0.027). CONCLUSIONS: The BAO algorithm can achieve quality plans comparable to manual plans. IMRT constraints dominate the final plan dose, while varying BAO constraints alter the trade-off among structures, providing an additional degree of freedom in planning design.

A simple method to import CAD mesh format models in FLUKA.

BACKGROUND: Monte Carlo (MC) code FLUKA possesses widespread usage and accuracy in the simulation of particle beam radiotherapy. However, the conversion from computer-aided design (CAD) mesh format models to FLUKA readable geometries could not be implemented directly and conveniently. A simple method was required to be developed. PURPOSE: The present study proposed a simple method to voxelize CAD mesh format files by using a Python-based script and establishing geometric models in FLUKA. METHODS: Five geometric models including cube, sphere, cone, ridge filter (RGF), and 1D-Ripple Filter (1D-RiFi) were created and exported as CAD mesh format files (.stl). An open-source Python-based script was used to convert them into voxels by endowing X, Y, and Z (following the Cartesian coordinates system) of solid materials in the three-dimensional (3D) grid. A FLUKA (4-2.2, CERN) predefined routine was used to establish the voxelized geometry model (VGM), while Flair (3.2-1, CERN) was used to build the direct geometry model (DGM) in FLUKA for comparison purposes. Uniform carbon ion radiation fields 8×8 cm3 and 4×4 cm3 were generated to transport through the five pairs of models, 2D and 3D dose distributions were compared. The integral depth dose (IDD) in water of three different energy levels of carbon ion beams transported through 1D-RiFis were also simulated and compared. Moreover, the volume between CAD mesh and VGMs, as well as the computing speed between FLUKA DGMs and VGMs were simultaneously recorded. RESULTS: The volume differences between VGMs and CAD mesh models were not more than 0.6%. The maximum mean point-to-point deviation of IDD distribution was 0.7% ± 0.51% (mean ± standard deviation). The 3D dose Gamma-index passing rates were never lower than 97% with criteria of 1%-1 mm. The difference in computing CPU time was 2.89% ± 0.22 on average. CONCLUSIONS: The present study proposed and verified a Python-based method for converting CAD mesh format files into VGMs and establishing them in FLUKA simply as well as accurately.

Variations and driving factors of leaf functional traits in the dominant desert plant species along an environmental gradient in the drylands of China.

Leaf functional traits (LFTs) of desert plants are responsive, adaptable and highly plastic to their environment. However, the macroscale variation in LFTs and driving factors underlying this variation remain unclear, especially for desert plants. Here, we measured eight LFTs, including leaf carbon concentration (LCC), leaf nitrogen concentration (LNC), leaf phosphorus concentration (LPC), specific leaf area (SLA), leaf dry matter content (LDMC), leaf mass per area (LMA), leaf thickness (LTH) and leaf tissue density (LTD) across 114 sites along environmental gradient in the drylands of China and in Guazhou Common Garden and evaluated the effect of environment and phylogeny on the LFTs. We noted that for all species, the mean values of LCC, LNC, LPC, SLA, LDMC, LMA, LTH and LTD were 384.62 mg g-1, 19.91 mg g-1, 1.12 mg g-1, 79.62 cm2 g-1, 0.74 g g-1, 237.39 g m-2, 0.38 mm and 0.91 g cm-3, respectively. LFTs exhibited significant geographical variations and the LNC, LMA and LTH in the plants of Guazhou Common Garden were significantly higher than the field sites in the drylands of China. LDMC and LTD of plants in Guazhou Common Garden were, however, considerably lower than those in the drylands of China. LCC, LPC, LTH and LTD differed significantly among different plant lifeforms, while LNC, SLA, LDMC and LMA didn't show significant variations. We found that the environmental variables explained higher spatial variations (3.6-66.3 %) in LFTs than the phylogeny (1.8-54.2 %). The LCC significantly increased, while LDMC and LTD decreased with increased temperature and reduced precipitation. LPC, LDMC, LMA, and LTD significantly increased, while SLA and LTH decreased with increased aridity. However, leaf elements were not significantly correlated with soil nutrients. The mean annual precipitation was a key factor controlling variations in LFTs at the macroscale in the drylands of China. These findings will provide new insights to better understand the response of LFTs and plants adaptation along environmental gradient in drylands, and will serve as a reference for studying biogeographic patterns of leaf traits.

Plant trait networks reveal adaptation strategies in the drylands of China.

BACKGROUND: Plants accomplish multiple functions by the interrelationships between functional traits. Clarifying the complex relationships between plant traits would enable us to better understand how plants employ different strategies to adapt to the environment. Although increasing attention is being paid to plant traits, few studies focused on the adaptation to aridity through the relationship among multiple traits. We established plant trait networks (PTNs) to explore the interdependence of sixteen plant traits across drylands. RESULTS: Our results revealed significant differences in PTNs among different plant life-forms and different levels of aridity. Trait relationships for woody plants were weaker, but were more modularized than for herbs. Woody plants were more connected in economic traits, whereas herbs were more connected in structural traits to reduce damage caused by drought. Furthermore, the correlations between traits were tighter with higher edge density in semi-arid than in arid regions, suggesting that resource sharing and trait coordination are more advantageous under low drought conditions. Importantly, our results demonstrated that stem phosphorus concentration (SPC) was a hub trait correlated with other traits across drylands. CONCLUSIONS: The results demonstrate that plants exhibited adaptations to the arid environment by adjusting trait modules through alternative strategies. PTNs provide a new insight into understanding the adaptation strategies of plants to drought stress based on the interdependence among plant functional traits.

A Functionalized Heterogeneous Catalyst from Atomically Precise Pd1 Au8 Clusters Facilitates Carbon-Carbon Bond Construction.

It has become possible to establish a connection between homogeneous and heterogeneous catalysis with atomically precise metal clusters. Due to their defined coordination geometry, in this work, atomically precise Pd1 Au8 (PPh3 )8 2+ clusters are exploited to identify the crucial factor that can impact the catalytic efficiency for the Suzuki-Miyaura cross-coupling process and further gain valuable insight into the exclusive cooperative effect of the central Pd atom and the peripheral Au atoms of the Pd1 Au8 (PPh3 )8 2+ cluster on controlling the cross-coupling reaction. Specifically, a heterogeneous catalyst, namely Pd1 Au8 @Resin, is designed by exchanging positively charged Pd1 Au8 (PPh3 )8 2+ clusters into the porous resin, thereby not only facilitating catalyst recyclability when performed in a batch reactor, but also realizing time-on-stream performance for the Suzuki-Miyaura cross-coupling reaction carried out in a fixed-bed reactor. The integrated advantages of homogeneous complexes and heterogeneous catalysts are expected to advance the usability of atomically precise metal clusters as heterogeneous catalysts for important bond constructions in homogeneous systems.

Concentrations and bioconcentration factors of leaf microelements in response to environmental gradients in drylands of China.

Determining response patterns of plant leaf elements to environmental variables would be beneficial in understanding plant adaptive strategies and in predicting ecosystem biogeochemistry processes. Despite the vital role of microelements in life chemistry and ecosystem functioning, little is known about how plant microelement concentrations, especially their bioconcentration factors (BCFs, the ratio of plant to soil concentration of elements), respond to large-scale environmental gradients, such as aridity, soil properties and anthropogenic activities, in drylands. The aim of the present study was to fill this important gap. We determined leaf microelement BCFs by measuring the concentrations of Mn, Fe, Ni, Cu and Zn in soils from 33 sites and leaves of 111 plants from 67 species across the drylands of China. Leaf microelement concentrations were maintained within normal ranges to satisfy the basic requirements of plants, even in nutrient-poor soil. Aridity, soil organic carbon (SOC) and electrical conductivity (EC) had positive effects, while soil pH had a negative effect on leaf microelement concentrations. Except for Fe, aridity affected leaf microelement BCFs negatively and indirectly by increasing soil pH and SOC. Anthropogenic activities and soil clay contents had relatively weak impacts on both leaf microelement concentrations and BCFs. Moreover, leaf microelement concentrations and BCFs shifted with thresholds at 0.89 for aridity and 7.9 and 8.9 for soil pH. Woody plants were positive indicator species and herbaceous plants were mainly negative indicator species of leaf microelement concentrations and BCFs for aridity and soil pH. Our results suggest that increased aridity limits the absorption of microelements by plant leaves and enhances leaf microelement concentrations. The identification of indicator species for the response of plant microelements to aridity and key soil characteristics revealed that woody species in drylands were more tolerant to environmental changes than herbaceous species.

Geometric and dosimetric evaluation of deep learning based auto-segmentation for clinical target volume on breast cancer.

BACKGROUND: Recently, target auto-segmentation techniques based on deep learning (DL) have shown promising results. However, inaccurate target delineation will directly affect the treatment planning dose distribution and the effect of subsequent radiotherapy work. Evaluation based on geometric metrics alone may not be sufficient for target delineation accuracy assessment. The purpose of this paper is to validate the performance of automatic segmentation with dosimetric metrics and try to construct new evaluation geometric metrics to comprehensively understand the dose-response relationship from the perspective of clinical application. MATERIALS AND METHODS: A DL-based target segmentation model was developed by using 186 manual delineation modified radical mastectomy breast cancer cases. The resulting DL model were used to generate alternative target contours in a new set of 48 patients. The Auto-plan was reoptimized to ensure the same optimized parameters as the reference Manual-plan. To assess the dosimetric impact of target auto-segmentation, not only common geometric metrics but also new spatial parameters with distance and relative volume ( R V ${R}_V$ ) to target were used. Correlations were performed using Spearman's correlation between segmentation evaluation metrics and dosimetric changes. RESULTS: Only strong (|R2 | > 0.6, p < 0.01) or moderate (|R2 | > 0.4, p < 0.01) Pearson correlation was established between the traditional geometric metric and three dosimetric evaluation indices to target (conformity index, homogeneity index, and mean dose). For organs at risk (OARs), inferior or no significant relationship was found between geometric parameters and dosimetric differences. Furthermore, we found that OARs dose distribution was affected by boundary error of target segmentation instead of distance and R V ${R}_V$ to target. CONCLUSIONS: Current geometric metrics could reflect a certain degree of dose effect of target variation. To find target contour variations that do lead to OARs dosimetry changes, clinically oriented metrics that more accurately reflect how segmentation quality affects dosimetry should be constructed.

Toward real-time automatic treatment planning (RTTP) with a one-step 3D fluence map prediction method and (nonorthogonal) convolution technique.

PURPOSE: To establish and evaluate a (quasi) real-time automated treatment planning (RTTP) strategy utilizing a one-step full 3D fluence map prediction model based on a nonorthogonal convolution operation for rectal cancer radiotherapy. METHODS: The RTTP approach directly extracts 3D projections from volumetric CT and anatomical data according to the beam incident direction. A 3D deep learning model with a nonorthogonal convolution operation was established that takes projections in cone beam space as input, extracts the features along and around the ray-trace path, and outputs a predicted fluence map (PFM) for each beam. The PFM is then converted to the MLC sequence with deliverable MUs to generate the final treatment plan. A total of 314 rectal adenocarcinoma patients with 2198 projection data samples were used in model training and validation. An extra 20 patients were used to test the feasibility of the RTTP method by comparing the plan quality, efficiency, deliverability performance, and physician blinded review results with the manual plans. RESULTS: Overall, the RTTP plans met the clinical dose criteria for target coverage, conformity, homogeneity, and organ-at-risk dose sparing. Compared to manual plans, the RTTP plans showed increases in PTV D1% by only 2.33% (p < 0.001) and a decrease in PTV D99% by 0.45% (p < 0.05). The RTTP plans showed a dose increase in the bladder, with a V50 of 14.01 ± 11.75% vs. 10.74 ± 8.51%, respectively, and no significant increases in the femoral head with the mean dose. The planning efficiency was improved in RTTP planning, with 39 s vs. 944 s in fluence map generation; the deliverability performance was saved by 1.91% (p < 0.001) in total MU. According to the blinded plan review by our physician, 55% of RTTP plans can be directly used in clinical radiotherapy treatment. CONCLUSION: The quasi RTTP method improves the planning efficiency and deliverability performance while maintaining a plan quality close to that of the optimized manual plans in rectal radiotherapy.

[Dosimetric Impact of Titanium Alloy Implant in Spinal IMRT Plan].

The purpose of this study is to establish and apply a correction method for titanium alloy implant in spinal IMRT plan, a corrected CT-density table was revised from normal CT-density table to include the density of titanium alloy implant. Dose distribution after and before correction were calculated and compared to evaluate the dose deviation. Plans were also copied to a spinal cancer simulation phantom. A titanium alloy fixation system for spine was implanted in this phantom. Plans were recalculated and compared with the measurement result. The result of this study shows that the max dose of spinal cord showed significant difference after correction, and the deviation between calculation results and measurement results was reduced after correction. The method for expanding the range CT-density table, which means that the density of titanium alloy was included, can reduce the error in calculation.

Technical note: First implementation of a one-stop solution of radiotherapy with full-workflow automation based on CT-linac combination.

BACKGROUND: Radiotherapy initiation is a laborious and time-consuming process that involves multiple steps and units. Workflow automation is in demand to improve the work efficiency and patient experience. PURPOSE: The purposes of this study are to describe the technical characteristics and clinical performance of an AI-powered one-stop radiotherapy workflow for initial treatment based on CT-linac combination, and provide insight into the behavior of full-workflow automation in radiotherapy. METHODS: Based on a CT-integrated linear accelerator and AI model implementation, the so-called "All-in-One" workflow incorporates routine procedures from simulation, autosegmentation, autoplanning, image guidance, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch. Clinical outcomes of the new workflow were evaluated for 10 enrolled patients with rectal cancer. RESULTS: For the enrolled patients, manual modifications of the autosegmented target volumes were necessary. The Dice similarity coefficient and 95% Hausdorff distance before and after the modifications were 0.892 ± 0.061 and 18.2 ± 13.0 mm, respectively. The autosegmented normal tissues and automatic plans were clinically acceptable without any modifications or reoptimization. The pretreatment IGRT corrections were within 2 mm in all directions, and the EPID-based in vivo QA showed γ passing rate of above 97% (3%/3 mm/10% threshold) at all the checkpoints, better than the results of rectal patients who followed a routine workflow. The duration of the whole process was 23.2 ± 3.5 minutes for the enrolled patients, depending mostly on the time required for manual modification and plan evaluation. CONCLUSION: The All-in-One workflow enables full-process automation of radiotherapy via seamless procedure integration. Compared to the routine workflow, the one-stop solution shortens the time scale it takes to ready the first treatment from days to minutes, significantly improving the patient experience and the workflow efficiency, and it also shows potential to facilitate clinical application of online adaptive replanning.

Plant-soil-microbe interactions in maintaining ecosystem stability and coordinated turnover under changing environmental conditions.

Ecosystem functions directly depend upon biophysical as well as biogeochemical reactions occurring at the soil-microbe-plant interface. Environment is considered as a major driver of any ecosystem and for the distributions of living organisms. Any changes in climate may potentially alter the composition of communities i.e., plants, soil microbes and the interactions between them. Since the impacts of global climate change are not short-term, it is indispensable to appraise its effects on different life forms including soil-microbe-plant interactions. This article highlights the crucial role that microbial communities play in interacting with plants under environmental disturbances, especially thermal and water stress. We reviewed that in response to the environmental changes, actions and reactions of plants and microbes vary markedly within an ecosystem. Changes in environment and climate like warming, CO2 elevation, and moisture deficiency impact plant and microbial performance, their diversity and ultimately community structure. Plant and soil feedbacks also affect interacting species and modify community composition. The interactive relationship between plants and soil microbes is critically important for structuring terrestrial ecosystems. The anticipated climate change is aggravating the living conditions for soil microbes and plants. The environmental insecurity and complications are not short-term and limited to any particular type of organism. We have appraised effects of climate change on the soil inhabiting microbes and plants in a broader prospect. This article highlights the unique qualities of tripartite interaction between plant-soil-microbe under climate change.

Contrasting relationships between plant-soil microbial diversity are driven by geographic and experimental precipitation changes.

Soil microbe diversity plays a key role in dryland ecosystem function under global climate change, yet little is known about how plant-soil microbe relationships respond to climate change. Altered precipitation patterns strongly shape plant community composition in deserts and steppes, but little research has demonstrated whether plant biodiversity attributes mediate the response of soil microbial diversity to long- and short-term precipitation changes. Here we used a comparative study to explore how altered precipitation along the natural and experimental gradients affected associations of soil bacterial and fungal diversity with plant biodiversity attributes (species, functional and phylogenetic diversity) and soil properties in desert-shrub and steppe-grass communities. We found that along both gradients, increasing precipitation increased soil bacterial and fungal richness in the desert and soil fungal richness in the steppe. Soil bacterial richness in the steppe was also increased by increasing precipitation in the experiment but was decreased along the natural gradient. Plant biodiversity and soil properties explained the variations in soil bacterial and fungal richness from 43 % to 96 % along the natural gradient and from 19 to 46 % in the experiment. Overall, precipitation effects on soil bacterial or fungal richness were mediated by plant biodiversity attributes (species richness and plant height) or soil properties (soil water content) along the natural gradient but were mediated by plant biodiversity attributes (functional or phylogenetic diversity) in the experiment. These results suggest that different mechanisms are responsible for the responses of soil bacterial and fungal diversity to long- and short-term precipitation changes. Long- and short-term precipitation changes may modify plant biodiversity attribute effects on soil microbial diversity in deserts and steppes, highlighting the importance of precipitation changes in shaping relationships between plant and soil microbial diversity in water-limited areas.

Reactivity and Recyclability of Ligand-Protected Metal Cluster Catalysts for CO2 Transformation.

It remains a significant challenge to construct an integrated catalyst that combines advantages of homogeneous and heterogeneous catalysis with clarified mechanism and high performance. Here we show atomically precise CuAg cluster catalysts for CO2 capture and utilization, where two functional units are combined into the clusters: metal and ligand. Due to atomic resolution on total and local structures of such catalysts to be achieved, which disentangles heterogeneous imprecise systems and permits tracing the reaction processes via experiments coupled with theory, site-specific catalysis induced by metal-ligand synergy can be accurately elucidated. The CuAg cluster catalysts exhibit excellent reactivity and recyclability to forge the C-N bonding from CO2 formylation with secondary amines that can make the cluster catalysts more unique compared with typically homogeneous complexes.