嫩江流域参考作物蒸散量时空变化及其气候归因

为了解嫩江流域参考作物蒸散量(ET₀)的时空变化特征,明确气候因素对流域ET₀的影响,应用Penman-Monteith公式计算1970—2019年嫩江流域各站点日ET₀,分析ET₀的时间变化趋势和空间分布格局,采用敏感性分析方法定量研究ET₀对气象因子敏感性程度,并进一步探究各气象因子对ET₀变化的贡献。结果表明: 研究期间,嫩江流域年际ET₀整体呈不显著减少趋势,春、夏、秋季ET₀波动减少,冬季波动增加;ET₀整体呈从东南向西北递减趋势。ET₀在时间和空间尺度上均表现为对相对湿度的敏感性最高;平均气温、相对湿度和风速的敏感性系数逐渐增强,日照时数的敏感性系数逐渐减弱。大兴安岭北部和小兴安岭地区ET₀对平均气温较敏感;大兴安岭南部和松嫩平原地区ET₀对风速较敏感。风速是影响全年及春、秋、冬季ET₀变化的主导因素,日照时数是影响夏季ET₀变化的主要因素。大兴安岭北部和小兴安岭地区平均气温和相对湿度对ET₀的贡献率最大,松嫩平原地区风速的贡献率最大。     

Normalized difference vegetation index prediction based on the delta downscaling method and back-propagation artificial neural network under climate change in the Sanjiangyuan region,China

Predicting the response of vegetation to climate change through mathematical methods is an important way to understand ecosystem condition changes in ecologically vulnerable regions. We took the Sanjiangyuan region, one of the most sensitive areas to climate change, as the study area to construct a simpler calculation and higher resolution (suitable for regional scale study) nonlinear method to predict the normalized difference vegetation index (NDVI) under climate change by combining the delta downscaling method and backpropagation artificial neural network. We first used the delta downscaling method to downscale the coarse-resolution climate element data of the Coupled Model Intercomparison Project (Phase 6) (CMIP6) to 0.08333° (regional scale). By analysing the relationship between NDVI and climate elements, we found that NDVI has the highest correlation with annual total precipitation, annual mean temperature, variation range of precipitation and temperature, etc. Then, we used these impact factors to train the back propagation artificial neural network (BP-ANN) and predict the NDVI in 2030 and 2060 under the SSP1–2.6 scenario and SSP5–8.5 scenario. The simulated results show that the BP-ANN can be used to construct the nonlinear relationship between NDVI and the impact factors on different scales. In the future, NDVI will increase under both the SSP1–2.6 scenario and the SSP5–8.5 scenario. The western part of the study area has the highest altitude, the ecosystem is more vulnerable, and the changes will be the most intense. This study is expected to provide a reference for understanding the impact of climate change on vegetation in national parks in plateaus and to provide a simpler NDVI prediction method for the evaluation of environmental quality under the impact of climate change with NDVI as one of the parameters.     

1961-2010年黄土高原地区参考作物蒸散量对气候变化的响应及未来趋势预估

根据1961-2010年我国黄土高原地区67个气象站常规气象资料,基于Penman Monteith公式计算了参考作物蒸散(ET0),并结合各气象因子的多年变化探讨了ET0变化的原因,在此基础上,应用基于分型理论的R/S方法对黄土高原区ET0未来的变化趋势进行了预测.结果表明:平均气温的敏感性虽较低,但因其显著变化,成为引起ET0变化的主导因子,贡献达到6.37％,太阳辐射和风速次之,实际水汽压敏感性较大,但因变化小,贡献仅为1.36％;空间分布上,气温对ET0的变化均为正贡献,风速和太阳辐射多为负贡献,实际水汽压在北部为负贡献,南部多为正贡献;未来一段时间ET0仍然保持与过去相一致的变化趋势.     

华北平原参考作物蒸散量时空变化及其影响因素分析

根据华北平原56个气象站1960—2012年逐日气象数据和Penman-Monteith模型计算了各站及区域整体参考作物蒸散量(ET0),利用样条插值法、气候倾向率、累积距平、敏感性系数等方法对华北平原ET0的时空变化及其影响因素进行了分析。结果表明:(1)华北平原多年平均ET0为1071.37mm,空间上呈现高低值相间分布格局,高值中心分布在冀北、鲁中、豫西,而低值中心分布在冀东、鲁南、豫东及豫南等地;(2)近53年ET0呈减少趋势(-12.8mm/10a),山东半岛北部及冀北等地有缓慢增加趋势,其余地区以减少为主;(3)ET0对气温、平均风速、日照时数为正敏感,而对相对湿度为负敏感。平均气温与日照时数敏感系数呈现下降趋势,相对湿度与风速敏感系数表现出上升趋势。ET0对气温和风速敏感度高的区域同时对日照时数和相对湿度敏感度较低;(4)归因分析表明,华北平原ET0的主导因子是日照时数,平均风速次之,相对湿度、最高温度、最低温度对ET0变化影响较小,日照时数主导区域包括冀北、坝上地区、冀中、豫西、豫南、鲁中及鲁西北,平均风速的主导区域为冀南、河南黄河以北、豫中、鲁西北,温度主导区域零星分布于冀北、豫西、山东半岛等地,相对湿度的主导区域主要分布在鲁南、山东半岛。     

农作物害虫预测模型网络共享平台系统

准确的害虫预测是科学防治害虫的前提.我国已开发了大量害虫预测模型,但未形成方便获取及应用的软件,用户难以理解复杂的数学模型,模型应用受到很大局限.本研究收集了50余种重要害虫,查阅筛选出预测预报模型文献350篇;按害虫名称、发生地点、预测内容对模型进行提取、归纳;利用VBSCRIPT技术对模型进行了程序化表达;设计了预...     

A neural network model for the prediction of membrane-spanning amino acid sequences

The architecture and weights of an artificial neural network model that predicts putative transmembrane sequences have been developed and optimized by the algorithm of structure evolution. The resulting filter is able to classify membrane/nonmembrane transition regions in sequences of integral human membrane proteins with high accuracy. Similar results have been obtained for both training and test set data, indicating that the network has focused on general features of transmembrane sequences rather than specializing on the training data. Seven physicochemical amino acid properties have been used for sequence encoding. The predictions are compared to hydrophobicity plots.     

宁夏沿黄绿洲参考作物蒸散演变特征及其归因

参考作物蒸散(Reference crop evapotranspiration,ET₀)是生态水文过程中的关键因子,研究ET₀在干旱绿洲区的演变,不仅有助于理解气候变化背景下的绿洲水文过程响应,亦对绿洲水土资源高效配置和生态系统稳定性维持有指导意义。以宁夏沿黄绿洲为例,基于1960-2019年的气象资料和Penman-Monteith模型计算ET₀,利用Mann-kendall突变检验、相对敏感系数和Morlet小波分析等方法,对宁夏沿黄绿洲近60 a的ET₀演变特征及其归因进行研究。结果表明:(1)宁夏沿黄绿洲ET₀年内呈单峰形态,ET₀在5-7月间较高,累积ET₀占年总ET₀的43.6%;近60 a的ET₀年均值为1226.38 mm,并以1.66 mm/a(P ＜ 0.01)幅度上升,但年际波动特征明显,其中在1988年突变之前,ET₀无显著变化趋势,而突变之后则以每10 a左右的周期显著增加或降低。(2)年ET₀主要以20-40 a和50-60 a周期振荡,且有多重时间尺度的复杂嵌套现象,不同季节的周期振荡差异较大,夏、秋季振荡幅度较强,其周期接近于年ET₀规律,而冬、春季振荡幅度较弱。(3)虽然ET₀与6种气象因子均存在显著相关性,但ET₀对不同气象因子的敏感性存在差异,其中对最高温度和相对湿度的敏感性较高,敏感系数分别为11.58%和8.40%。(4)宁夏沿黄绿洲ET₀与区域气候变化特征有较强的耦合性,区域气候的持续升温和相对湿度持续降低、以及由此引发的饱和水汽压亏缺持续增强是推动ET₀上升的重要原因,而气候由湿润向干旱的突变和平均风速的异常波动是诱发ET₀突变的原因。     

基于IPSO-BP神经网络模型的山东省碳排放预测及生态经济分析

在低碳经济发展背景下,针对山东省碳排放数据更新迟缓、以往预测模型难以满足现实需求的问题,统计相关数据,根据政府间气候变化专门委员会(IPCC)推荐方法测算山东省2000—2017年碳排放量和排放强度,并借助脱钩分析、碳承载力和碳赤字探究碳排放的动态变化趋势;基于5项最重要的碳排放影响因素,建立改进的粒子群算法(IPSO...     

一种自优化RBF神经网络的叶绿素a浓度时序预测模型

藻类水华发生过程具有复杂性、非线性、时变性等特点,其准确预测一直是一个国际性难题.以天津市于桥水库为研究对象,根据2000年1月至2003年12月常规监测的水生生态数据(采样周期为10 d),提出了一种结合时序方法的可自优化RBF神经网络智能预测模型,对判断藻类水华的重要指标叶绿素a浓度进行预测.研究了训练样本量及RBF神经网络扩展速度SPREAD值的可自优化性能,以及该模型用于于桥水库叶绿素a浓度的短期变化趋势预测的可行性.结果表明,预测性能指标随SPREAD值及样本量不同发生变化,该预测模型能自动寻到最优SPREAD值,并发现至少需要约两年的训练样本量才能达到较好预测效果.当样本量为105,SPREAD值为10时,预测效果最好,精度较高,预测值与实测值的相关系数R达到0.982.该方法对水库的藻类水华预警有一定的参考价值.     

A comparison of models for the long-term prediction of Nilaparvata lugens in South China based on future climate in Indochina

The brown planthopper (BPH), Nilaparvata lugens (Stål) is a migratory rice pest that periodically erupts across China, and mainly come from Indochina Peninsula in spring. The climate change in Indochina Peninsula has caused obvious impacts on the immigration of BPH. To further understand the influence of climate change on BPH immigration, the light-trap data of BPH in southern China from 1980 to 2016 were collected, and the best performance data of each meteorological factor (temperature, humidity, precipitation and wind) in Indochina Peninsula by simulating eight Global Climate Models (GCMs) were chosen. The key predicting factors were screened out by identifying correlations between the number of BPH and the meteorological factors. The support vector machine (SVM), back propagation (BP) neural network, and stepwise regression were used to establish medium long-term prediction models of immigration amount of BPH, and their advantages and disadvantages were compared, and prediction of the occurrence of BPH in the future was carried out by using three models based on future climate data provided by GCMs (under RCP4.5 or RCP8.5 scenario). The results showed that: (1) BNU-CSM1–1 model was the best in temperature simulation; CESM1-CAM5 model was the best in humidity simulation; HadGEM2-AO model was the best in precipitation and meridional wind simulation; BCC-CSM1–1 model was the best in zonal wind simulation. (2) The accuracies of retrospective testing and forward forecasting of three models indicated the SVM model was more suitable for medium long-term predicting the occurrence of BPH than BP neural networkmodel and stepwise regression model. (3) The immigration of BPH in Guangxi, Jiangxi, Hunan and Hubei would be greater than other provinces in southern China in the next 7 years, and should be taken seriously.     

基于Penman-Monteith模型的两个蒸散模型在夏玉米农田的参数修正及性能评价

利用涡度相关系统和小气象系统对2013—2015年夏玉米生长季的蒸散量和气象数据进行实时观测,基于观测数据对以Penman-Monteith模型为基础的FAO-PM模型和KP-PM模型进行分析:首先利用2013和2014年数据对两个模型中的关键参数进行校正,其次利用两个模型对2015年夏玉米农田的日蒸散量进行计算,并与测量值对比,说明两个模型在夏玉米农田的适用性;最后采用分阶段法对KP-PM模型中的经验系数进行修正.结果表明: FAO-PM模型对2015年夏玉米农田日蒸散量的计算值更加接近测量值;利用分阶段法对KP-PM模型进行修正后,模型对日蒸散量的计算效果有了很大提高,且计算值比FAO-PM模型更接近测量值.模型中关键系数与气象条件之间有很大关系,因此利用模型进行蒸散预测时,必须先对模型进行参数校正.该研究可为其他研究人员利用模型估算蒸散量提供方法上的参考.     

Artificial neural network (ANN)‐based prediction of depth filter loading capacity for filter sizing

This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut‐off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R²) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte‐Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436–1443, 2016     

Mammals,freshwater reference states,and the mitigation of climate change

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TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations

Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations to become costly and time-consuming. Computational techniques can improve the efficiency of drug combination screening. Despite recent advances in applying machine learning to synergistic drug combination prediction, several challenges remain. First, the performance of existing methods is suboptimal. There is still much space for improvement. Second, biological knowledge has not been fully incorporated into the model. Finally, many models are lack interpretability, limiting their clinical applications. To address these challenges, we have developed a knowledge-enabled and self-attention transformer boosted deep learning model, TranSynergy, which improves the performance and interpretability of synergistic drug combination prediction. TranSynergy is designed so that the cellular effect of drug actions can be explicitly modeled through cell-line gene dependency, gene-gene interaction, and genome-wide drug-target interaction. A novel Shapley Additive Gene Set Enrichment Analysis (SA-GSEA) method has been developed to deconvolute genes that contribute to the synergistic drug combination and improve model interpretability. Extensive benchmark studies demonstrate that TranSynergy outperforms the state-of-the-art method, suggesting the potential of mechanism-driven machine learning. Novel pathways that are associated with the synergistic combinations are revealed and supported by experimental evidences. They may provide new insights into identifying biomarkers for precision medicine and discovering new anti-cancer therapies. Several new synergistic drug combinations have been predicted with high confidence for ovarian cancer which has few treatment options. The code is available at https://github.com/qiaoliuhub/drug_combination.     

Indicator plant species selection for monitoring the impact of climate change based on prediction uncertainty

To develop a long-term volunteer-based system for monitoring the impacts of climate change on plant distributions, potential indicator plants and monitoring sites were assessed considering habitat prediction uncertainty. We used species distribution models (SDMs) to project potential habitats for 19 popular edible wild plants in Japan. Prediction uncertainties of SDMs were assessed using three high-performance modeling algorithms and 19 simulated future climate data. SDMs were developed using presence/absence records, four climatic variables, and five non-climatic variables. The results showed that prediction uncertainties for future climate simulations were greater than those from the three different modeling algorithms. Among the 19 edible wild plant species, six had highly accurate SDMs and greater changes in occurrence probabilities between current and future climate conditions. The potential habitats of these six plants under future climate simulations tended to shift northward and upward, with predicted losses in potential southern habitats. These results suggest that these six plants are candidate indicators for long-term biological monitoring of the impacts of climate change. If temperature continuously increases as predicted, natural populations of these plants will decline in Kyushu, Chugoku and Shikoku districts, and in low altitudes of Chubu and Tohoku districts. These results also indicate the importance of occurrence probability and prediction uncertainty of SDMs for selecting target species and site locations for monitoring programs. Sasa kurilensis, a very popular and widespread dominant scrub bamboo in the cool-temperate regions of Japan, was found to be the most effective plant for monitoring.     

人工神经网络与遗传算法相结合在作物估产中的应用——以吉林省玉米估产为例

在遗传算法(Genetic Algorithm)与误差反传(Back Propagation)网络结构模型相结合的基础上,设计了用遗传算法训练神经网络权重的新方法,并对吉林省梨树和德惠县的玉米进行了估产研究,同时与BP算法和灰色系统理论模型进行了比较.经检验,计算值与实际值接近,并优于灰色理论模型,具有良好的预测效果,从而为农作物估产提供了新方法.     

Spectrophotometric resolution of ternary mixtures of tryptophan, tyrosine, and histidine with the aid of principal component-artificial neural network models

A simple and sensitive spectrophotometric method to resolve ternary mixtures of tryptophan (Trp), tyrosine (Tyr), and histidine (His) in synthetic and water samples is described. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K(3)Fe(CN)(6)) in alkaline medium. The absorbance data were monitored on the analytical wavelength (420 nm) of K(3)Fe(CN)(6) spectrum. Synthetic mixtures of the three amino acids were analyzed, and the data obtained were processed by principal component-artificial neural network (PC-ANN) models. After reducing the number of spectral data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Tangent and sigmoidal transfer function were used in the hidden and output layers, respectively. The analytical performance of this method was characterized by relative standard error. The method allowed the determination of Trp, Tyr, and His at concentrations between 10 and 55, 10 and 60, and 10 and 40 microg ml(-1), respectively. The results show that the PC-ANN is an efficient method for prediction of the three analytes.     

An assessment of neural network and statistical approaches for prediction of E. coli promoter sites.

We have constructed a perceptron type neural network for E. coli promoter prediction and improved its ability to generalize with a new technique for selecting the sequence features shown during training. We have also reconstructed five previous prediction methods and compared the effectiveness of those methods and our neural network. Surprisingly, the simple statistical method of Mulligan et al. performed the best amongst the previous methods. Our neural network was comparable to Mulligan's method when false positives were kept low and better than Mulligan's method when false negatives were kept low. We also showed the correlation between the prediction rates of neural networks achieved by previous researchers and the information content of their data sets.