黄土高原生物结皮对SCS-CN模型初损率的影响

水文模型是水文过程研究的有效工具,初损率(λ)是径流模型SCS-CN模型的参数,对模拟流域水文过程具有重要意义。为了确定生物结皮对λ的影响,提高该模型在黄土高原生物结皮广泛分布的退耕地的预测精度,本研究以陕西省定边县鹰窝山涧流域不同盖度的生物结皮坡面为对象,采用模拟降雨试验,分析土壤潜在最大入渗量(S)与实际入渗量(F)的关系,以及生物结皮盖度对λ的影响,并修订了λ;在此基础上,采用陕西省安塞县纸坊沟流域生物结皮径流小区的模拟降雨试验数据校验了参数修订后的模型。结果表明: 生物结皮坡面S与F的关系式为: S/F=2.5×60/T(其中T为降雨历时);模型参数λ与生物结皮盖度(C_BSC)呈极显著负相关关系,二者关系式为: λ=0.0791×e^{(-0.015×C_BSC}),R²=0.60;较λ取标准值,依生物结皮盖度修订λ后,SCS-CN模型Nash效率系数提高338.7%,合格率提升16.1%。研究结果为黄土高原生物结皮坡面λ的确定提供了科学依据,对准确评估黄土高原退耕还林(草)工程的水文效应具有重要意义。     

基于遥感信息的黄土高原主要灌溉农业分布区生态安全特征

生态安全是区域经济社会可持续发展的重要保证。本研究基于2000—2018年间的遥感数据,系统分析了黄土高原4个主要灌溉农业分布区(银川平原、河套平原、汾河谷地和渭河平原)植被覆盖度(FVC)和遥感生态指数(RSEI)的变化特征。结果表明: 2000—2018年间,研究区FVC整体呈下降趋势,但4个不同灌溉农业分布区FVC变化趋势各异。研究区RSEI整体呈下降趋势,其中,银川平原和渭河平原的RSEI下降明显,分别下降0.06和0.07,河套平原的RSEI无明显变化,而汾河谷地的RSEI整体呈上升趋势。4个区域中,银川平原、汾河谷地的生态稳定性较差,河套平原的生态环境较稳定,而渭河平原的生态环境持续退化。研究结果对区域生态环境保护和农业可持续发展具有重要意义。     

基于地形梯度的黄河流域中段植被覆盖时空分异特征——以延安市为例

植被恢复是黄河流域生态保护和高质量发展的关键,深入研究植被的时空分异特征具有重要的现实意义。本研究以4期Landsat TM/OLI为遥感数据源,采用像元二分模型估算植被覆盖度,运用转移矩阵、地学信息图谱和重心迁移模型分析1988—2018年黄河流域中段延安市植被覆盖的时空演变特征;并结合地形数据,利用地形分布指数分析高程、坡度上植被覆盖的空间响应规律。结果表明: 研究期间,延安市植被覆盖呈北低南高的空间分布特征,植被覆盖受政策影响而大幅增高;1988—2018年,延安市植被变化模式以持续向好和稳定不变为主导,有50%的区域植被覆盖情况改善,83%的高植被覆盖区域保持稳定。在各高程和坡度等级上,高植被覆盖的分布优势度随时间变化而增大;在各坡度等级上,植被增加百分比和植被稳定性随坡度增加而增大。延安不同等级植被覆盖的迁移方向与植被覆盖整体的迁移趋势基本一致,总体向北偏西转移。延安植被建设已取得显著成效,但北部植被覆盖状况仍待提高,优化植被类型和结构是未来植被建设的重要方向。     

Automatic VMAT technique to treat glioblastoma: A two years’ experience

PurposeThe present work aims to guide the physicist in order to start automated planning for the VMAT treatment of glioblastoma multiforme (GBM) by giving a recipe that was set up and tested during a long-term (two years) evaluation.MethodsAn automatic technique in AutoPlanning module of the Pinnacle3 (Philips Medical Systems, Fitchburg, WI) treatment planning system was created and validated by comparing dose distributions of automatic plans (APs) and manual plans (MPs) and by performing a blind AP-MP comparison on a cohort of 20 patients. Automatic technique was then applied to 145 patients and failures were recorded i.e. the number of times for which dose distributions produced by the automatic module were not suitable for treatment.ResultsEach of the 20 APs considered in the validation step was clinically acceptable and proved to be better (15 cases) or equal (5 cases) respect to MPs. A statistically significant improvement in brain stem, optic pathways, cochleae, pituitary gland and scalp sparing was observed for APs, while no statistically significant differences were recorded in target coverage or plan parameters. For only 5 cases out of the 145 plans the operator intervention was needed in order to obtain a clinical acceptable plan, while for the remaining 140 plans the automatic created solution was suitable.ConclusionsA straightforward automatic procedure has been created and tested in our clinic. The AutoPlanning technique proposed represents a reliable tool to improve treatment planning efficiency and the recipe, here presented, could be simply imported to every radiotherapy center.     

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.     

Comparison of the RayStation photon Monte Carlo dose calculation algorithm against measured data under homogeneous and heterogeneous irradiation geometries

PurposeThis work compares Monte Carlo dose calculations performed using the RayStation treatment planning system against data measured on a Varian Truebeam linear accelerator with 6 MV and 10 MV FFF photon beams.MethodsThe dosimetric performance of the RayStation Monte Carlo calculations was evaluated in a variety of irradiation geometries employing homogeneous and heterogeneous phantoms. Profile and depth dose comparisons against measurement were carried out in relative mode using the gamma index as a quantitative measure of similarity within the central high dose regions.ResultsThe results demonstrate that the treatment planning system dose calculation engine agrees with measurement to within 2%/1 mm for more than 95% of the data points in the high dose regions for all test cases. A systematic underestimation was observed at the tail of the profile penumbra and out of field, with mean differences generally <0.5 mm or 1% of curve dose maximum respectively. Out of field agreement varied between evaluated beam models.ConclusionsThe RayStation implementation of photon Monte Carlo dose calculations show good agreement with measured data for the range of scenarios considered in this work and is deemed sufficiently accurate for introduction into clinical use.     

Making family planning accessible in resource-poor settings

It is imperative to make family planning more accessible in low resource settings. The poorest couples have the highest fertility, the lowest contraceptive use and the highest unmet need for contraception. It is also in the low resource settings where maternal and child mortality is the highest. Family planning can contribute to improvements in maternal and child health, especially in low resource settings where overall access to health services is limited. Four critical steps should be taken to increase access to family planning in resource-poor settings: (i) increase knowledge about the safety of family planning methods; (ii) ensure contraception is genuinely affordable to the poorest families; (iii) ensure supply of contraceptives by making family planning a permanent line item in healthcare system''s budgets and (iv) take immediate action to remove barriers hindering access to family planning methods. In Africa, there are more women with an unmet need for family planning than women currently using modern methods. Making family planning accessible in low resource settings will help decrease the existing inequities in achieving desired fertility at individual and country level. In addition, it could help slow population growth within a human rights framework. The United Nations Population Division projections for the year 2050 vary between a high of 10.6 and a low of 7.4 billion. Given that most of the growth is expected to come from today''s resource-poor settings, easy access to family planning could make a difference of billions in the world in 2050.     

Conservation planning on a budget: a “resource light” method for mapping priorities at a landscape scale?

Conservation projects may be reluctant to attempt Systematic Conservation Planning because existing methods are often prohibitive in the time, money, data, and expertise they require. We tried to develop a “resource light” method for Systematic Conservation Planning and applied it to the Ewaso Ngiro Landscape of central Kenya. Over a 6-month preparation period and 1-week participatory workshop, we used expert assessments to select focal biodiversity features, set quantitative targets for these, map their current distribution, vulnerability, potential for recovery, and conservation costs, and, finally, map cross-feature conservation priorities. Preparation for and facilitation of the workshop required time investment by one part-time workshop coordinator, eight workshop committee members, six ecosystem experts, and two GIS technicians. Total time investment was approximately 56.5 person-weeks spread over facilitators and 40 workshop participants. Monetary costs for the workshop were approximately $US 42,000, excluding investments made by researchers previous to this project. Costs for a similar workshop could vary substantially, depending on need to cover salaries, international travel, food and lodging, and the number of participants. To stay within our resource constraints, we completed the exercise for only four of nine focal biodiversity features and did not negotiate trade-offs between conservation and human land-uses or use planning software to identify “optimal networks” of conservation areas. These were not considered critical for conservationists to try Systematic Conservation Planning, introduce landscape-scale conservation concepts to stakeholders, and begin implementing landscape conservation strategies. Participants agreed that further work would be needed to complete and update the planning process. Due to the lack of comparative cost data from similar planning exercises, we cannot definitively conclude that our approach was “resource light”, although we suspect it is within the constraints of most site-based conservation projects.     

Review of the protected area network in Guinea, West Africa, and recommendations for new sites for biodiversity conservation

With only five protected areas dedicated to the conservation of biodiversity (two national parks, one strict nature reserve and two faunal reserves), Guinea has one of the smallest protected area networks in West Africa. As a result, two of the five ecoregions of the country and six of the 14 globally threatened large and medium-sized mammals occurring in Guinea are not found in the national protected area network. To identify areas with high biodiversity that could be included in the national protected area network, we used the Key Biodiversity Areas (KBA) methodology. We devised a scoring system to rank the identified KBAs according to their relative conservation significance. We identified a total of 16 KBAs throughout the country. Their proclamation as protected areas would result in the protection of all ecoregions and all but one of Guinea’s globally threatened large and medium-sized mammals. Twelve of the 16 KBAs have the legal status of classified forest, a status that should facilitate the change into formal biodiversity protected areas (IUCN category I–IV). Our analysis indicates that even if only the two areas with the highest conservation significance score, the Ziama and Diécké forests, become formal protected areas, this would provide protection to both the western Guinean lowland forests, one of the most threatened ecoregions in Africa, and to 11 of the 14 threatened large and medium-sized mammals occurring in Guinea.     

The effects of interpolation error and location quality on animal track reconstruction

The Global Positioning System (GPS) gives precise estimates of location. However, the investigation of animal movement and behavior often requires interpolation to examine events between such fixes. We obtained 6,288 GPS locations from an electronic tag deployed for 170 d on an adult male gray seal ( Halichoerus grypus ) that ranged freely off the east coast of Scotland, and interpolated between subsamples of these data to investigate the growth of uncertainty within the intervals between observations. Average uncertainty over the path increased linearly as the interval between interpolating locations increased, reaching 12 km in longitude and 6 km in latitude at 2-d separation. The decrease in precision caused by duty-cycling, only collecting data in part of the day, was demonstrated. Adding noise to the GPS locations to simulate data from the ARGOS satellite system had little effect on the total errors for observations separated by more than 12 h. While the rate of growth in interpolation error is likely to vary between species, these results suggest that frequent, and preferably evenly spaced, location fixes are required to take full advantage of the precision of GPS data in the reconstruction of animal tracks.