Implementation of a dose calculation algorithm based on Monte Carlo simulations for treatment planning towards MRI guided ion beam therapy

Magnetic resonance guidance in particle therapy has the potential to improve the current performance of clinical workflows. However, the presence of magnetic fields challenges the current algorithms for treatment planning. To ensure proper dose calculations, compensation methods are required to guarantee that the maximum deposited energy of deflected beams lies in the target volume. In addition, proper modifications of the intrinsic dose calculation engines, accounting for magnetic fields, are needed. In this work, an algorithm for proton treatment planning in magnetic fields was implemented in a research treatment planning system (TPS), matRad. Setup-specific look up tables were generated using a validated MC model for a clinical proton beamline (62.4 – 215.7 MeV) interacting with a dipole magnet (B = 0–1 T). The algorithm was successfully benchmarked against MC simulations in water, showing gamma index (2%/2mm) global pass rates higher than 96% for different plan configurations. Additionally, absorbed depth doses were compared with experimental measurements in water. Differences within 2% and 3.5% in the Bragg peak and entrance regions, respectively, were found. Finally, treatment plans were generated and optimized for magnetic field strengths of 0 and 1 T to assess the performance of the proposed model. Equivalent treatment plans and dose volume histograms were achieved, independently of the magnetic field strength. Differences lower than 1.5% for plan quality indicators (D_2%, D_50%, D_90%, V_95% and V_105%) in water, a TG119 phantom and an exemplary prostate patient case were obtained. More complex treatment planning studies are foreseen to establish the limits of applicability of the proposed model.     

Total ecosystem carbon stocks at the marine‐terrestrial interface: Blue carbon of the Pacific Northwest Coast,United States

The coastal ecosystems of temperate North America provide a variety of ecosystem services including high rates of carbon sequestration. Yet, little data exist for the carbon stocks of major tidal wetland types in the Pacific Northwest, United States. We quantified the total ecosystem carbon stocks (TECS) in seagrass, emergent marshes, and forested tidal wetlands, occurring along increasing elevation and decreasing salinity gradients. The TECS included the total aboveground carbon stocks and the entire soil profile (to as deep as 3 m). TECS significantly increased along the elevation and salinity gradients: 217 ± 60 Mg C/ha for seagrass (low elevation/high salinity), 417 ± 70 Mg C/ha for low marsh, 551 ± 47 Mg C/ha for high marsh, and 1,064 ± 38 Mg C/ha for tidal forest (high elevation/low salinity). Soil carbon stocks accounted for >98% of TECS in the seagrass and marsh communities and 78% in the tidal forest. Soils in the 0–100 cm portion of the profile accounted for only 48%–53% of the TECS in seagrasses and marshes and 34% of the TECS in tidal forests. Thus, the commonly applied limit defining TECS to a 100 cm depth would greatly underestimate both carbon stocks and potential greenhouse gas emissions from land‐use conversion. The large carbon stocks coupled with other ecosystem services suggest value in the conservation and restoration of temperate zone tidal wetlands through climate change mitigation strategies. However, the findings suggest that long‐term sea‐level rise effects such as tidal inundation and increased porewater salinity will likely decrease ecosystem carbon stocks in the absence of upslope wetland migration buffer zones.     

Human land uses reduce climate connectivity across North America

Climate connectivity, the ability of a landscape to promote or hinder the movement of organisms in response to a changing climate, is contingent on multiple factors including the distance organisms need to move to track suitable climate over time (i.e. climate velocity) and the resistance they experience along such routes. An additional consideration which has received less attention is that human land uses increase resistance to movement or alter movement routes and thus influence climate connectivity. Here we evaluate the influence of human land uses on climate connectivity across North America by comparing two climate connectivity scenarios, one considering climate change in isolation and the other considering climate change and human land uses. In doing so, we introduce a novel metric of climate connectivity, ‘human exposure’, that quantifies the cumulative exposure to human activities that organisms may encounter as they shift their ranges in response to climate change. We also delineate potential movement routes and evaluate whether the protected area network supports movement corridors better than non‐protected lands. We found that when incorporating human land uses, climate connectivity decreased; climate velocity increased on average by 0.3 km/year and cumulative climatic resistance increased for ~83% of the continent. Moreover, ~96% of movement routes in North America must contend with human land uses to some degree. In the scenario that evaluated climate change in isolation, we found that protected areas do not support climate corridors at a higher rate than non‐protected lands across North America. However, variability is evident, as many ecoregions contain protected areas that exhibit both more and less representation of climate corridors compared to non‐protected lands. Overall, our study indicates that previous evaluations of climate connectivity underestimate climate change exposure because they do not account for human impacts.     

国家公园规划制度功能定位与空间属性

我国将构建统一的国土空间规划体系, 整体谋划国土空间开发保护格局, 强化对各类专项规划的指导约束, 这必将对国家公园保护制度产生重大影响。本文分析了国土空间规划视角下国家公园规划制度的定位, 阐述了国家公园规划承载空间规划与发展规划两大体系的基本属性, 应在国土空间规划体系中具有优先权和前置权; 重点分析了各国家公园总体规划在国家规划体系中的功能与属性特征, 作为空间规划应承接好上位空间规划对国家公园的控制指标和管控要求, 作为建设规划应承接好上位或同域国民经济发展规划等对国家公园的目标任务要求, 作为管理规划要明确国家公园顺畅运行的要求。国家公园应在分区规划、控制性详细规划等专项规划方面寻求创新, 探索国家公园用途管制的实现模式。     

基于综合评价法的洞庭湖区绿地生态网络构建

绿地生态网络对于改善区域生态空间破碎化、生物多样性降低、生态系统服务供需不平衡及保障区域生态安全具有重要意义。本研究以洞庭湖区为例,在3S技术支持下,从生态系统服务功能、潜在生物多样性、形态空间格局的角度综合评价和识别生态源地及计算栅格单元的基本生态阻力;利用夜间灯光指数修正基本生态阻力;运用最小累积阻力模型识别生态廊道;构建加权评价模型,对源地的聚合度、离散度及廊道的生态连接贡献度进行评价;利用结构特征指数对综合网络、“源-汇”潜在网络及规划网络进行对比分析和评价。结果表明: 源地空间分布不均衡,林地、灌丛与水域三者面积之和占源地总面积的95.9%,位于研究区中部的洞庭湖湿地生态风险较高;源地离生态网络系统中心位置越近及到其他源地的平均最小累积阻力越小,聚合及离散的优势越强;高生态质量源地周围的中、高生态质量源地分布越密集,其聚合度、离散度越高;廊道离高生态质量的源地越近,表现为生态连接贡献度越大;林地、灌丛,尤其是河道在自然生态系统与人类社会系统之间起着非常重要的生态连接作用;“源-汇”规划绿道对“源-汇”潜在生态廊道形成了良好补充,与“源-汇”潜在网络相比,综合网络的α、β、γ、ρ指数分别提高123.1%、25.8%、26.2%、74.6%;与“源-汇”规划网络相比,α、β、γ、ρ指数分别提高了190.0%、31.1%、32.5%、114.6%。本研究结果能为洞庭湖区绿地生态网络构建、国土空间规划提供参考。     

New Caledonian crows plan for specific future tool use

The ability to plan for future events is one of the defining features of human intelligence. Whether non-human animals can plan for specific future situations remains contentious: despite a sustained research effort over the last two decades, there is still no consensus on this question. Here, we show that New Caledonian crows can use tools to plan for specific future events. Crows learned a temporal sequence where they were (a) shown a baited apparatus, (b) 5 min later given a choice of five objects and (c) 10 min later given access to the apparatus. At test, these crows were presented with one of two tool–apparatus combinations. For each combination, the crows chose the right tool for the right future task, while ignoring previously useful tools and a low-value food item. This study establishes that planning for specific future tool use can evolve via convergent evolution, given that corvids and humans shared a common ancestor over 300 million years ago, and offers a route to mapping the planning capacities of animals.     

Recognizing the enemy: do predator cues influence hatching in Neotropical annual killifish?

Annual fish species have evolved complex adaptations to survive in temporary wetlands. The main adaptation of these fish is the ability to produce embryos that survive dry periods. Embryo development of this fish can show variation at multiple levels influenced by many environmental factors, such as photoperiod and temperature. Predator cues are another factor that can influence the embryonic stage. One way in which annual fish could adapt to predators is by using risk-spreading strategies (through bet-hedging). Nonetheless, this strategy depends on the coevolutionary history between predators and preys and on the degree of environmental unpredictability, resulting in different responses across different species. This study investigated the influence of predator cues on the embryonic development and hatching of two Austrolebias species that inhabit ponds that present differences in hydroperiod and the risk of predator presence. The results confirmed a differentiated response between the two annual fish species tested, corroborating the modulation of hatching against the risk of predation by native predatory fish. The authors further showed that development times varied between the two annual fish species, regardless of the presence of predators. They highlight that the variation in embryonic development is strongly affected by different levels of hydroperiod unpredictability faced by the two species. To unravel finer-scale local adaptations in the annual fish embryo development, future studies should focus on a region with greater spatial gradient.     

三级串联垂直潜流人工湿地的脱氮性能及微生物学特征

探究了3种水力负荷(HLR)下三级串联垂直潜流人工湿地(T-VFCWs)对农村生活污水的处理效果,并解析了系统中的氮素转化机制。结果表明: 当系统HLR由0.10增至0.20 m³·m^-2·d^-1时,T-VFCWs始终保持着对农村生活污水高效的处理效果,系统出水水质满足《城镇污水处理厂污染物排放标准》(GB 18918—2002)一级A标准。T-VFCWs中顺次连接的3个VFCW单元(标记为V-1、V-2和V-3)在限氧环境下因其进水水质的差异可形成各自不同的氮素转化途径,并通过协同作用实现系统的高效脱氮。当T-VFCWs在试验期间连续运行时,V-1、V-2和V-3中主要的脱氮途径分别为短程硝化/反硝化作用、基于亚硝化的全程自养脱氮(CANON)作用和反硝化作用,上述3单元对进水中总氮(TN)和NH₄⁺-N去除的贡献率分别为(51.3±4.4)%和(63.7±2.6)%、(30.9±4.8)%和(35.5±4.5)%、(17.8±5.0)%和(0.8±0.1)%。该研究可为组合式人工湿地的研发及工程化应用提供科学依据和技术支撑。     

Prediction of multi-criteria optimization (MCO) parameter efficiency in volumetric modulated arc therapy (VMAT) treatment planning using machine learning (ML)

PurposeTo predict the impact of optimization parameter changes on dosimetric plan quality criteria in multi-criteria optimized volumetric-modulated-arc therapy (VMAT) planning prior to optimization using machine learning (ML).MethodsA data base comprising a total of 21,266 VMAT treatment plans for 44 cranial and 18 spinal patient geometries was generated. The underlying optimization algorithm is governed by three highly composite parameters which model a combination of important aspects of the solution. Patient geometries were parametrized via volume- and shape properties of the voxel objects and overlap-volume histograms (OVH) of the planning-target-volume (PTV) and a relevant organ-at-risk (OAR). The impact of changes in one of the three optimization parameters on the maximally achievable value range of five dosimetric properties of the resulting dose distributions was studied. To predict the extent of this impact based on patient geometry, treatment site, and current parameter settings prior to optimization, three different ML-models were trained and tested. Precision-recall curves, as well as the area-under-curve (AUC) of the resulting receiver-operator-characteristic (ROC) curves were analyzed for model assessment.ResultsSuccessful identification of parameter regions resulting in a high variability of dosimetric plan properties depended on the choice of geometry features, the treatment indication and the plan property under investigation. AUC values between 0.82 and 0.99 could be achieved. The best average-precision (AP) values obtained from the corresponding precision/recall curves ranged from 0.71 to 0.99.ConclusionsMachine learning models trained on a database of pre-optimized treatment plans can help finding relevant optimization parameter ranges prior to optimization.