PPARγ/UCP2在甲醛致学习记忆障碍中的作用

为探究过氧化物酶体增殖物激活受体γ/解偶联蛋白2（PPARγ/UCP2）在甲醛（FA）诱导的学习记忆障碍中的作用,本文将C57BL/6小鼠随机分为：对照组、T0070907组（抑制剂组）、3mg/m³ FA组、3mg/m³ FA+T0070907组,进行连续21d的实验暴露,在第22d取脑组织测定脑组织脏体比并匀浆,检测活性氧（ROS）、谷胱甘肽（GSH）、丙二醛（MDA）、核因子κB （NF-κB）、白细胞介素6（IL-6）、PPARγ、UCP2等生化指标,通过Nissl染色观察脑组织的病理学变化.结果发现,与对照组相比,T0070907组和3mg/m³ FA组小鼠大脑皮层神经元受损,GSH含量下降,ROS、MDA、NF-κB、IL-6含量有所上升,而3mg/m³ FA+T0070907组小鼠上述现象更严重.此外,与对照组相比,T0070907组小鼠脑组织中的PPARγ和UCP2含量下降,但3mg/m³ FA组小鼠脑组织中的PPARγ和UCP2含量上升;与3mg/m³ FA组相比,加入抑制剂的3mg/m³ FA+T0070907组小鼠脑组织中的PPARγ和UCP2含量下降.研究结果表明,在加入PPARγ抑制剂后,PPARγ/UCP2含量下降,加重了FA所致小鼠的学习记忆障碍,故PPARγ/UCP2在FA致学习记忆障碍中可能起保护作用.     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

Uncertainty propagation in vegetation distribution models based on ensemble classifiers

Ensemble learning techniques are increasingly applied for species and vegetation distribution modelling, often resulting in more accurate predictions. At the same time, uncertainty assessment of distribution models is gaining attention. In this study, Random Forests, an ensemble learning technique, is selected for vegetation distribution modelling based on environmental variables. The impact of two important sources of uncertainty, that is the uncertainty on spatial interpolation of environmental variables and the uncertainty on species clustering into vegetation types, is quantified based on sequential Gaussian simulation and pseudo-randomization tests, respectively. An empirical assessment of the uncertainty propagation to the distribution modelling results indicated a gradual decrease in performance with increasing input uncertainty. The test set error ranged from 30.83% to 52.63% and from 30.83% to 83.62%, when the uncertainty ranges on spatial interpolation and on vegetation clustering, respectively, were fully covered. Shannon’s entropy, which is proposed as a measure for uncertainty of ensemble predictions, revealed a similar increasing trend in prediction uncertainty. The implications of these results in an empirical distribution modelling framework are further discussed with respect to monitoring setup, spatial interpolation and species clustering.     

Monitoring and adaptive management: resolving social and organisational issues to improve information sharing in natural resource management

Adaptive, or 'learning by doing', approaches are often advocated as a means of providing increased understanding within natural resource management. However, a number of organisational and social issues need to be resolved if these approaches are to be used successfully. A case study in the South Island high country of New Zealand is used to review what is needed to support an ongoing community-based monitoring and adaptive management programme. First, the case study is described, paying attention to the social context of the resource management problem. The results of a workshop that explored this problem are then outlined, along with a proposed information flow suggested by participants. Requirements for future steps to resolve these problems (such as information protocols and a multi-stakeholder information system) are discussed. Finally, some broad lessons are drawn from this exercise that could help others developing similar approaches.     

Finding Hope in the Millennium Ecosystem Assessment

Abstract:  Over the past quarter century, a new scientific activity has emerged: collective assessments by large numbers of scientists from different disciplines combining their expertise to better understand human interrelations with nature and to inform policy. The Millennium Ecosystem Assessment exceeded all such assessments before it in both the breadth of its coverage and the depth of its analysis of socioecological system dynamics. The findings are not encouraging. Nearly all ecosystems are being degraded and will continue to be degraded for decades to come even if policy changes are initiated now. For scientists participating in the assessment, the MA had another disconcerting aspect. It clearly shows that our fragmented, disciplinary knowledges cannot simply be combined to form an understanding of a whole complex system. Counterbalancing the despair of the findings and scientific difficulties of aggregating specialized knowledges, the MA demonstrated the potential of a deliberative democratic approach to grappling with complex problems .     

基于机器学习分类算法解析EIS数据的有机涂层性能评价方法

目的 基于机器学习分类算法快速评估有机涂层的防腐性能。方法 通过实验室加速试验模拟涂层真实的退化过程,并根据测得的电化学数据,分析不同退化阶段的等效电路元件参数。随后,采用随机抽样方法获取大量数据,用于机器学习模型训练。通过对比支持向量机(SVM)、k最近邻(k-NN)和随机森林(RF)3种不同的机器学习算法,以及多种输入特征集训练的涂层性能分类器模型的准确率,分析最适合用于涂层性能快速评估的机器学习算法和电化学特征。结果 根据不同输入特征训练的k-NN和RF模型均表现出良好的预测效果,而SVM模型的预测效果相对较差。根据不同频率范围训练的分类器模型中,在低频区表现最佳,而在高频区表现较差。结论 基于阻抗虚部、虚部+实部和阻抗模值3种输入特征训练的RF分类器模型的预测效果最准确。不同频率区间内,低频区的阻抗特征更能准确表征涂层性能。     

基于链式多重插补的WOA-ELM煤与瓦斯突出预测模型

为了提高缺失数据下煤与瓦斯突出预测准确率,提出1种基于链式支持向量机多重插补(MICE＿SVM)的鲸鱼优化算法(WOA)-极限学习机(ELM)预测模型,以淮南朱集矿区为例,选取5个煤与瓦斯突出影响指标作为模型特征,采用提出的MICE＿SVM算法插补突出事故数据中缺失值,利用WOA优选ELM输入层权值及隐含层阈值,构建煤与瓦斯突出预测模型,将插补后数据用于WOA-ELM模型的训练与测试,并与其他模型的预测效果对比。研究结果表明：MICE＿SVM插补前、后的有突出数据预测准确率分别为83.02%,90.41%,MICE＿SVM显著提高了有突出预测准确率,对无突出和整体的预测准确率提高不明显;数据插补后WOA优化ELM对无突出、有突出和整体的预测准确率分别为97.94%,96.25%,96.48%,较优化前分别提高了5.79%,5.84%,5.55%,数据插补后WOA-ELM为最佳预测模型。     

基于SVR的含缺陷管道剩余强度研究

针对采用标准预测含缺陷管道剩余强度误差较大这一问题,在Matlab中建立基于SVR的含缺陷管道剩余强度预测模型,并基于60组含缺陷管道爆破试验数据进行训练测试,以验证模型的实际性能.结果表明:SVR模型预测测试集结果的最小相对误差为0.55％,最大相对误差为10.35％,平均相对误差为2.63％,预测结果的R2高达0....     

YOLOv4目标检测算法在煤矿工人口罩佩戴监测工作中的应用研究*

为防止煤矿工人吸入过量粉尘而导致职业性尘肺病,基于Keras框架利用YOLOv4 (you only look once)目标检测算法对井下人员佩戴防尘口罩情况进行高精度且快速的检测与识别,并与MTCNN(Multi-task convolutional neural network)和FaceNet构成的人脸识别算法相结合,进行煤矿工人口罩佩戴监测的研究。结果表明:模型对井下人员口罩佩戴有较高的检测精度,识别已佩戴口罩的矿井下作业人员的平均精度达到92.78%,识别未佩戴防尘口罩检测的平均精度为91.63%,与其他主流算法相比算法具有更好的鲁棒性和检测效果。研究结果为预防煤矿工人职业性尘肺病提供1种有效的技术手段。