气候变化和经济发展对肾综合征出血热发生的影响

肾综合征出血热(hemorrhagic fever with renal syndrome, HFRS)是一种啮齿动物传播的自然疫源性疾病, 危害严重, 已成为全球重要的公共卫生问题。本研究采用数理统计模型及小波分析方法, 对陕西省西安市鄠邑区1984-2016年HFRS的发生与鼠类、气候和经济因素的关系进行分析, 探讨气候和经济因素对HFRS发生的影响。小波分析结果表明, 该地区的HFRS暴发史可能分为两个时期, 推测每个时期具有不同的主要宿主, 在2002年褐家鼠(Rattus norvegicus)可能取代黑线姬鼠(Apodemus agrarius)成为HFRS疫源地的主要宿主。广义可加模型模拟结果表明, HFRS的发生与1984-2001年黑线姬鼠密度间存在极显著非线性效应(F_2.06,9.02 = 102.415, P < 0.01), 两者间显现为正相关; 与2002-2016年的褐家鼠密度间呈正相关(F_1.67,9.02 = 73.929, P < 0.01); HFRS主要宿主的这种变化可能与当地气候变化和经济发展有关: HFRS的发生与年平均温度存在极显著的非线性效应(F_2.93,9.02 = 12.164, P < 0.01), 两者间呈负相关; 同样, HFRS的发生与上一年的国内生产总值(GDP)也存在显著非线性效应(F_1.70,9.02 = 2.917, P < 0.05), 两者间也呈负相关。结构方程模型通过直接和间接的影响途径证明了这种转移机制, 发现温度对HFRS发生有显著的直接负向影响以及通过褐家鼠的间接正向影响; GDP对HFRS发生有直接的负向影响。本研究表明HFRS的发生与气候变化和经济发展相关, 两者均能影响HFRS的暴发, 该结论有助于今后更好地对HFRS疾病进行预防和控制。     

Myocardial Infarction Detection and Localization Using Optimal Features Based Lead Specific Approach

ObjectivesObjective of this paper is to present a reliable and accurate technique for Myocardial Infarction (MI) detection and localization.Material and methodsStationary wavelet transform has been used to decompose the ECG signal. Energy, entropy and slope based features were extracted at specific wavelet bands from selected lead of ECG. k-Nearest Neighbors (kNN) with Mahalanobis distance function has been used for classification. Sensitivity (Se), specificity (Sp), positive predictivity (+P), accuracy (Acc), and area under the receiver operating characteristics curve (AUC) analyzed over 200 subjects (52 health control, 148 with MI) from Physikalisch-Technische Bundesanstalt (PTB) database has been used for performance analysis. To handle the imbalanced data adaptive synthetic (ADASYN) sampling approach has been adopted.ResultsFor detection of MI, the proposed technique has shown an AUC = 0.99, Se = 98.62%, Sp = 99.40%, PPR = 99.41% and Acc = 99.00% using 12 top ranked features, extracted from multiple leads of ECG and AUC = 0.99, Se = 98.34%, Sp = 99.77%, PPR = 99.77% and Acc = 99.05% using 12 features extracted from a single ECG lead (i.e. lead V5). For localization of MI, the proposed technique has an AUC = 0.99, Se = 98.78%, Sp = 99.86%, PPR = 98.80%, and Acc = 99.76% using 5 top ranked features from multiple leads of ECG and AUC = 0.98, Se = 96.47%, Sp = 99.60%, PPR = 96.49% and Acc = 99.28% using 8 features extracted from a single ECG lead (i.e. lead V3).ConclusionThus for MI detection and localization, the proposed technique is independent of time-domain ECG fiducial markers and can work using specific leads of ECG.     

Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals

ObjectivesElectromyography (EMG) is recording of the electrical activity produced by skeletal muscles. The classification of the EMG signals for different physical actions can be useful in restoring some or all of the lost motor functionalities in these individuals. Accuracy in classifying the EMG signal indicates efficient control of prosthesis.Material and methodsThe flexible analytic wavelet transform (FAWT) is used for classification of surface electromyography (sEMG) signals for identification of physical actions. FAWT is an efficient method for decomposition of sEMG signal into eight sub-bands, features namely neg-entropy, mean absolute value (MAV), variance (VAR), modified mean absolute value type 1 (MAV1), waveform length (WL), simple square integral (SSI), Tsallis entropy, integrated EMG (IEMG) are extracted from the sub-bands. Extracted features are fed into an extreme learning machine (ELM) classifier with sigmoid activation function.ResultsComprehensive experiments are conducted on the input sEMG signals and the accuracy, sensitivity and specificity scores are used for performance measurement. Experiments showed that among all sub-bands, the seventh sub-band provided the best performance where the recorded accuracy, sensitivity and specificity values were 99.36%, 99.36% and 99.93%, respectively. The comparison results showed best efficiency of proposed method as compared to other methods on the same dataset.ConclusionThis paper investigates the usage of the FAWT and ELM on sEMG signal classification. The results show that the proposed method is quite efficient in classification of the sEMG signals. It is also observed that the seventh sub-band of the FAWT provides the best discrimination property. In the future works, recent wavelet transform methods will be used for improving the classification performance.     

北京松山落叶阔叶林生态系统水热通量对环境因子的响应

温带森林生态系统水热通量在多时间尺度上受各种生物物理因子的影响。该研究假设这些因子对水热通量的影响机制具有时间尺度分异性, 通过涡度相关法(EC)于2019年全年对北京松山典型天然落叶阔叶林生态系统蒸散发(ET)、显热通量(H)、潜热通量(LE)、土壤热通量(G)、饱和水汽压差(VPD)、空气温度(T_a)、光合有效辐射(PAR)、归一化植被指数(NDVI)及10 cm深度土壤水分(VWC)等要素进行原位连续监测, 使用小波分析的方法分析了日、季节尺度上生物与非生物因子对生态系统能量分配与水汽交换的调控机制。主要研究结果: 2019年松山天然落叶阔叶林生态系统年均波文比(β)为1.53。ET具有明显的季节变化特征, 从第100天开始逐渐增加, 7月达到峰值, 第300天下降到最低水平。ET最大日累计值为5.01 mm·d^-1, 年累计值为476.2 mm, 年降水量为503.3 mm。在日尺度上水热通量与VPD间滞后时间最短, 为3.36 h。在季节尺度上与PAR间滞后时间最短, 为8天。季节尺度上PAR通过VPD来对ET造成间接影响, 而对β造成直接影响。该研究发现不同时间尺度上水热通量与环境因子间的时滞关系, 为选择模型在不同时间尺度下北方温带落叶阔叶林生态系统过程的最佳输入参数提供科学支持。     

Eutrophic status influences the impact of pesticide mixtures and predation on Daphnia pulex populations

Pesticides, nutrients, and ecological stressors such as competition or predation co‐occur in freshwater ecosystems impacted by agriculture. The extent to which combinations of these stressors affect aquatic populations and the role of nutrients availability in modulating these responses requires further understanding. In this study, we assessed how pesticides affecting different taxonomic groups and predation influence the response of Daphnia pulex populations under different trophic conditions. An outdoor experiment was designed following a factorial design, with the insecticide chlorpyrifos, the herbicide diuron, and the predation by Notonecta sp. individuals as key stressors. The single impact of each of these stressors, and their binary and tertiary combinations, was evaluated on D. pulex abundance and population structure under mesotrophic and eutrophic conditions for 21 days. Data were analyzed using generalized linear mixed models estimated by means of a novel Bayesian shrinkage technique. Our study shows a significant influence of each of the evaluated stressors on D. pulex abundance; however, the impacts of the herbicide and predation were lower under eutrophic conditions as compared to the mesotrophic ones. We found that binary stressor interactions were generally additive in the mesotrophic scenario, except for the herbicide–predation combination, which resulted in synergistic effects. The impacts of the binary stressor combinations in the eutrophic scenario were classified as antagonistic, except for the insecticide–herbicide combination, which was additive. The tertiary interaction resulted in significant effects on some sampling dates; however, these were rather antagonistic and resembled the most important binary stressor combination in each trophic scenario. Our study shows that the impact of pesticides on freshwater populations depends on the predation pressure, and demonstrates that the combined effect of pesticides and ecological stressors is influenced by the food availability and organism fitness related to the trophic status of freshwater ecosystems.     

QT interval measurement using RMED curve; a novel approach based on wavelet techniques

Hypothesis/objective: Prolonged QT interval is an index of propensity for dangerous ventricular tachyarrhythmias. The aim of this article is to establish an automatic algorithm for QT interval measurement.

Method: The proposed method is based on the continuous wavelet transform. In this method, the concepts of the rescaled wavelet coefficients and dominant scales of the electrocardiogram (ECG) components are used to perform detection of ECG characteristic points. A new concept of rescaled maximum energy density is introduced so as to perform the estimation of the QT interval.

Results and conclusion: We have applied the algorithm to the PTB database of the Physiobank?Physionet in lead II. Then, the results were evaluated using pertinent reference QT. The criterion used for evaluation of the method's performance is the root mean square (RMS) error. The method approached the RMS error of 27.89 ms for 549 subjects. The proposed method is fast, simple and is applicable to a wide range of ECG cardio cycle morphologies.     

A time–frequency approach to estimate critical time intervals in postural control

The critical time interval (CTI) is a parameter that has been used to distinguish open-loop from closed-loop control during upright stance. The aim of this study was to develop a new method to determine CTIs. The new approach, termed the intermittent critical time interval (ICTI) method, was motivated from evidence that upright standing is an intermittent rather than an asymptotic stability control process. For this ICTI method, center-of-pressure time series are first transformed to the time–frequency domain with a wavelet method. Subsequently, the CTI is assumed equal to the time span between two local maxima in the time–frequency domain within a distinct frequency band (i.e., 0.5–1.1 Hz). This new method may help facilitate better estimates of the transition time interval between open and closed-loop control during upright stance and can also be applied in future work such as in simulating postural control. In addition, this method can be used in future work to assess temporal changes in CTIs.     

Identification of novel prognostic alternative splicing signature in papillary renal cell carcinoma

Papillary renal cell carcinoma (pRCC) is a heterogeneous disease containing multifocal or solitary tumors with an aggressive phenotype. Increasing evidence has indicated the involvement of aberrant splicing variants in renal cell cancer, while systematic profiling of aberrant alternative splicing (AS) in pRCC was lacking and largely unknown. In the current study, comprehensive profiling of AS events were performed based on the integration of pRCC cohort from the Cancer Genome Atlas database and SpliceSeq software. With rigorous screening and univariate Cox analysis, a total of 2077 prognoses AS events from 1642 parent genes were identified. Then, stepwise least absolute shrinkage and selection operator method-penalized Cox regression analyses with 10-fold cross-validation followed by multivariate Cox regression were used to construct the prognostic AS signatures within each AS type. And a final 21 AS event-based signature was proposed which showed potent prognostic capability in stratifying patients into low- and high-risk subgroups (P < .0001). Furthermore, time-dependent receiver operating characteristics curves confirmed that the final AS signature was effective and robust in predicting overall survival for pRCC patients with the area under the curve above 0.9 from 1 to 5 years. In addition, splicing correlation network was built to uncover the potential regulatory pattern among prognostic splicing factors and candidate AS events. Besides, gene set enrichment analysis revealed the involvement of these candidates AS events in tumor-related pathways including extracellular matrix organization, oxidative phosphorylation, and P53 signaling pathways. Taken together, our results could contribute to elucidating the underlying mechanism of AS in the oncogenesis process and broaden the novel field of prognostic and clinical application of molecule-targeted approaches in pRCC.     

Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper features and remove the redundancy from the primary feature vector. Finally, the extracted features are applied to the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers separately to determine the normal image or disease type. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs less number of features for classification.