Application of an unsupervised artificial neural network technique to multivariant surface water quality data

Surface water contamination from agricultural and urban runoff and wastewater discharges from industrial and municipal activities is of major concern to people worldwide. Classical models can be insufficient to visualise the results because the water quality variables used to describe dynamic pollution sources are complex, multivariable, and nonlinearly related. Artificial intelligence techniques with the ability to analyse multivariant water quality data by means of a sophisticated visualisation capacity can offer an alternative to current models. In this study, the Kohonen self-organising feature maps (SOM) neural network was initially applied to analyse the complex nonlinear relationships among multivariable surface water quality variables using the component planes of the variables to determine the complex behaviour of water quality parameters. The dependencies between water quality variables were extracted and interpreted using the pattern analysis visualised in component planes. For further investigation, the k-means clustering algorithm was used to determine the optimal number of clusters by partitioning the maps and utilising the Davies–Bouldin clustering index, leading to seven groups or clusters corresponding to water quality variables. The results reveal that the concentrations of Na, K, Cl, NH₄-N, NO₂-N, o-PO₄, component planes of organic matter (pV), and dissolved oxygen (DO) were significantly affected by seasonal changes, and that the SOM technique is an efficient tool with which to analyse and determine the complex behaviour of multidimensional surface water quality data. These results suggest that this technique could also be applied to other environmentally sensitive areas such as air and groundwater pollution.     

Luminance Information Is Required for the Accurate Estimation of Contrast in Rapidly Changing Visual Contexts

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Identification of new candidate drugs for lung cancer using chemical–chemical interactions,chemical–protein interactions and a K-means clustering algorithm

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical–chemical interactions and chemical–protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.     

Fitting models predicting dates of flowering of temperate-zone trees using simulated annealing

The aim of the present study was to test the four commonly used models to predict the dates of flowering of temperate-zone trees, the spring warming, sequential, parallel and alternating models. Previous studies concerning the performance of these models have shown that they were unable to make accurate predictions based on external data. One of the reasons for such inaccuracy may be wrong estimations of the parameters of each model due to the non-convergence of the optimization algorithm towards their maximum likelihood. We proposed to fit these four models using a simulated annealing method which is known to avoid local extrema of any kind of function, and thus is particularly well adapted to fit budburst models, as their likelihood function presents many local maxima. We tested this method using a phenological dataset deduced from aeropalynological data. Annual pollen spectra were used to estimate the dates of flowering of the populations around the sampling station. The results show that simulated annealing provides a better fit than traditional methods. Despite this improvement, classical models still failed to predict external data. We expect the simulated annealing method to allow reliable comparisons among models, leading to a selection of biologically relevant ones.     

Fluorescence detection of serum albumin with a turnover-based sensor utilizing Kemp elimination reaction

The Kemp elimination reaction is a well-known chemical reaction that is facilitated on a protein surface microenvironment, and in particular is highly accelerated in a unique binding pocket of serum albumin. We have designed and synthesized a fluorescently activatable coumarin derivative with a benzisoxazole scaffold to enable monitoring of the Kemp elimination reaction in terms of fluorescence change for the first time. We show that this fluorescent sensor can sensitively and selectively quantitate serum albumin in blood samples. It also works in a dry-chemistry format.     

Genomic alignment

As sequencing techniques become increasingly efficient, the average length of a sequence is bound to grow. Traditional sequence-comparison algorithms can either compare DNA or protein, but not a mixture, which is actually a common situation. Most obtained DNA sequences contain coding regions, and it is more reliable to compare the coding regions as protein than just as DNA.A heuristic algorithm is presented that can compare DNA with both coding and noncoding regions, but that also can compare multiple reading frames and determine which exons are homologous.A program, GenAl (Genomic Alignment), was developed that implements the algorithm. Its use is demonstrated on two retroviruses. Correspondence to: J. Hein     

Adaptive Quantile Estimation and its Application in Analysis of Biological Signals

Quantiles and their estimations are the basis for solving numerous problems of statistics. The paper presents adaptive recursive estimation methods for this statistical parameter. Its specifical properties are investigated and the possibilities of applications in computer assisted analysis of biological signals are demonstrated even satisfying real time requirements.     

外源NO对镉胁迫下番茄活性氧代谢及光合特性的影响

采用水培方法,研究了外源一氧化氮（NO）对镉（Cd）胁迫下番茄幼苗活性氧代谢及光合特性的影响.结果表明：在Cd胁迫下,外施100 μmol·L^-1 硝普钠（SNP）显著增强了番茄超氧化物岐化酶（SOD）、过氧化氢酶（CAT）等抗氧化酶活性,提高了叶片、根系中Ca、Fe等元素含量,提高了叶片叶绿素含量、净光合速率（P_n）、蒸腾速率（T_r）和气孔导度（G_s）,降低了过氧化氢（H₂O₂）、丙二醛（MDA）含量和胞间CO₂浓度（C_i）.但SNP对Cd胁迫下的缓解效应可被牛血红蛋白（Hb,NO的清除剂）消除.在Cd处理液中加入100 μmol·L^-1 NO_x^-（NO的分解产物）或100 μmol·L^-1亚铁氰化钠（SNP的相似物或分解产物）,对Cd胁迫无显著改善.表明外源NO可通过提高活性氧清除能力,维持矿质营养元素平衡,缓解Cd胁迫对番茄幼苗叶片光合机构的破坏,从而维持番茄光合效率.     

基于Mel倒谱系数和矢量量化的昆虫声音自动鉴别

为了给生产单位害虫管理的普通技术人员提供简便易操作的昆虫种类鉴别方法, 本研究把人类语音识别领域的先进技术应用于昆虫识别, 提出了一种新颖的昆虫声音自动鉴别方法, 用声音参数化技术为昆虫声纹识别设计了一种简单易行的方案。声音信号经过预处理、分段得到一系列的声音样本, 从声音样本提取Mel倒谱系数（MFCC）, 并用Linde-Buzo-Gray（LBG）算法对提取的MFCC进行矢量量化(VQ), 所得码字作为声音样本的特征模型。特征参数之间的匹配用搜索最近邻的方法实现。本文方法在包含70种昆虫声音的库中进行了试验, 取得了超过96%的识别率和理想的时间性能。试验结果证明了该方法的有效性。