Principal Component Analysis PCA Research Articles

Low dose dimethyl sulfoxide driven gross molecular changes have the potential to interfere with various cellular processes

Dimethyl sulfoxide (DMSO) is a small molecule with polar, aprotic and amphiphilic properties. It serves as a solvent for many polar and nonpolar molecules and continues to be one of the most used solvents (vehicle) in medical applications and scientific research. To better understand the cellular effects of DMSO within the concentration range commonly used as a vehicle (0.1–1.5%, v/v) for cellular treatments, we applied Attenuated Total Reflectance (ATR) Fourier Transform Infrared (FT-IR) spectroscopy to DMSO treated and untreated epithelial colon cancer cells. Both unsupervised (Principal Component Analysis-PCA) and supervised (Linear Discriminant Analysis-LDA) pattern recognition/modelling algorithms applied to the IR data revealed total segregation and prominent differences between DMSO treated and untreated cells at whole, lipid and nucleic acid regions. Several of these data were supported by other independent techniques. Further IR data analyses of macromolecular profile indicated comprehensive alterations especially in proteins and nucleic acids. Protein secondary structure analysis showed predominance of β-sheet over α-helix in DMSO treated cells. We also observed for the first time, a reduction in nucleic acid level upon DMSO treatment accompanied by the formation of Z-DNA. Molecular docking and binding free energy studies indicated a stabilization of Z-DNA in the presence of DMSO. This alternate DNA form may be related with the specific actions of DMSO on gene expression, differentiation, and epigenetic alterations. Using analytical tools combined with molecular and cellular biology techniques, our data indicate that even at very low concentrations, DMSO induces a number of changes in all macromolecules, which may affect experimental outcomes where DMSO is used as a solvent.

Sexual dimorphism and morphometric characterization of Centris tarsata Smith, 1874, Hymenoptera: Apidae in different environments

Las abejas solitarias Apidae: Centridini son importantes polinizadoras de Malpighia emarginata DC. Malpighiales: Malpighiaceae. No obstante, existen pocos estudios sobre la caracterización de las poblaciones de esta especie, en áreas de plantaciones y su entorno. En este estudio fueron analizadas la variabilidad morfométrica y dimorfismo sexual de Centris tarsata Smith, recolectada en nidos artificiales instalados en cuatro tipos de vegetación. Para el estudio, se disecó el ala anterior derecha de 168 individuos, fotografiadas, y las imágenes fueron transformadas en TpsUtil y llevadas al TpsDig2 versión 2.12 para marcar 18 puntos anatómicos, usando el método de morfometría geométrica. Los datos fueron analizados a través de Análisis de Componentes Principales ACP, Variables Canónicas VC y prueba de validación cruzada, dirigida a una correcta clasificación de cada individuo en VC. Además, el análisis del tamaño de ala fue utilizado mediante el tamaño del centroide y posteriormente se hizo un análisis de varianza, comparando los tratamientos con una prueba de Tukey. Se encontró que las diferentes condiciones ambientales pueden influenciar en el Patrón morfométrico intrassexual de las abejas C. tarsata y promover selección sexual. Fue posible observar el dimorfismo sexual de C. tarsata, lo que puede ser un factor determinante para favorecer características biológicas distintas en un proceso evolutivo, contribuyendo, así, en la comprensión de los factores ecológicos, biológicos, morfogenéticos y de comportamiento, relacionados con su función en un agroecosistema.

Abstract 3732: Insights into the non-coding genome of parathyroid tumors

Abstract Background. Parathyroid tumors are characterized by genetic and epigenetic alterations resulting in aberrant expression of protein coding genes and non-coding RNAs. Although long non-coding RNAs (lncRNAs) play a regulatory role in endocrine cancer pathogenesis, a lncRNAs signature in human parathyroid tumors is still missing. Here we investigated the lncRNAs alterations, both at genomic and transcriptional level, in human non-familial parathyroid tumors. Methods. The expression of 90 lncRNAs was investigated in 4 parathyroid carcinomas (PCas), 12 adenomas (PAds) and 2 normal glands (PaNs) using a commercial array. Both unsupervised (hierarchical clustering-HCL and Principal Component Analysis-PCA) and supervised (Significance Analysis of Microarray, SAM) analyses were performed to identify differences in lncRNAs expression between the 3 tissue types. Significant lncRNAs were validated in a second set of parathyroid tissues including 7 PCas, 26 PAds, 6 atypical PAds (aPAds) and 4 PaNs. Genomic characterization of 21 PAds was performed by array Comparative Genomic Hybridization (aCGH). CDC73 and Multiple Endocrine Neoplasia 1 (MEN1) genes mutations were detected by Sanger sequencing. Results. HCL analysis of lncRNAs expression identified 2 major groups in which PaNs and PCas were distinguished. Nine lncRNAs were differentially expressed in parathyroid tissues. Specifically, KCNQ1OT1 and SNHG6 were enriched in PaNs, HAR1B, MEG3, HOXA3as and NEAT1 expression characterized PAds, whereas BC200, HOXA6as and WT1-AS were significantly upregulated in PCas. Besides confirming previous data, validation analysis highlighted a different lncRNAs expression pattern in PCas and aPAds according to CDC73 mutation status, with mutated tumors overexpressing the majority of the lncRNAs. Interestingly, BACE1-AS, KCNQ1OT1, NEAT1 and SNHG6 levels in PAds were significantly correlated with MEN1 levels while HAR1B upregulation was associated with chromosome 11 loss of heterozygosity (LOH). Conclusions. Overall these findings shed light on lncRNAs deregulation in parathyroid pathobiology. Parathyroid tumors histotypes are characterized by different lncRNAs signatures that are related to chromosome 11 derangements and to MEN1 inactivation. Finally, MEN1 may play an epigenetic role in lncRNAs regulation, supporting the important role of chromosome 11 in parathyroid tumorigenesis. Citation Format: Annamaria Morotti, Irene Forno, Valentina Andrè, Vito Guarnieri, Andrea Terrasi, Rosa Maria Silipigni, Silvana Guerneri, Chiara Verdelli, Alfredo Scillitani, Leonardo Vicentini, Filomena Cetani, Edoardo Beretta, Sabrina Corbetta, Valentina Vaira. Insights into the non-coding genome of parathyroid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3732.

¹H-NMR Profiling and Chemometric Analysis of Selected Honeys from South Africa, Zambia, and Slovakia.

Honey is the natural sweet substance produced by honeybee from nectar or honeydew, exhibiting several nutritional and health benefits. It contains a complex mixture of compounds in different proportions, with sugars being the main component. The physicochemical characteristics of ten honeys were evaluated; represented by five, three, and two from South Africa, Slovakia, and Zambia, respectively. The range of values for the pH (3.75–4.38), electrical conductivity (99–659 µS/cm), and moisture content (14.2–17.7%) are within the recommended limits for quality honeys. 1H-NMR (Nuclear Magnetic Resonance) profiling of the honeys in D2O was determined, and the data were analysed by chemometrics. This method is fast, reproducible, and sample pre-treatment is not necessary. The 1H-NMR fingerprints of various chemical shift regions showed similarity or dissimilarity across geographical origins that are useful for identification, detection of adulteration, and quality control. The principal component analysis PCA and partial linear square discriminant analysis PLS-DA of the 1H-NMR profiles successively categorises the honeys into two chemically related groups. The R2 values are higher than the corresponding Q2 values for all samples, confirming the reliability of the model. Honeys in the same cluster contain similar metabolites and belong to the same botanic or floral origin.

Midpoint-radii principal component analysis -based EWMA and application to air quality monitoring network

Monitoring air quality is crucial for the safety of humans and the environment. Moreover, real world data collected from air quality network is often affected by different types of errors as measurement noise and variability of pollutant concentrations. The uncertainty in the data, which is strictly connected to the above errors, may be treated by considering interval-valued data analysis. In practical cases of measured data, the true value cannot be measured and the collected data on a process are only approximations given by sensors, and are thus imprecise. This is due mainly to the uncertainties induced by measurement errors or determined by specific experimental conditions. Thus, the main aim of this paper is to develop an enhanced monitoring of air quality network by taking into account the uncertainties on the data. To do that, we develop a new monitoring technique that merges the advantages of Midpoint-radii PCA (MRPCA) method with exponentially weighted moving average (EWMA) chart, in order to enhance sensor fault detection technique of air quality monitoring process. MRPCA is the most popular interval multivariate statistical method, able to tackle the issue of uncertainties on the models and one way to improve the fault detection abilities. On the other hand, the EWMA statistic allows an exponential weighted average to successive observations and able to detect small and moderate faults. The developed MRPCA-based EWMA method relies on using MRPCA as a modeling framework for fault detection and EWMA as a detection chart. The proposed MRPCA-based EWMA scheme is illustrated using a simulation example and applied for sensor fault detection of an air quality monitoring network. The monitoring performances of the developed technique are compared to the classical monitoring techniques. MRPCA model performances are compared with the interval PCA models: complete-information principal component analysis (CIPCA) and Centers PCA (CPCA). The MRPCA-based EWMA monitoring performances are compared to MRPCA-based Shewhart, generalized likelihood ratio test (GLRT) and squared prediction error (SPE) techniques.

Hand Geometry and Palmprint Classification System Based on Statistical Analysis

Biometric system is considered of an important type of security systems nowadays, because it relays on the individual traits (physical or behavioral) non-participation between any two people that can't be lost on lifetime and can't be stolen. This paper, will present the individual classification system based on hand geometry and palm texture feature where it is one of the parts of the human body, which has an impressive set of information capable to distinguish and identify individuals. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system consists of three phases: image preprocessing, hand feature extraction and pattern classification. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system utilized complete hand image inside database consists of 600 pictures, include 100 people, each one has six images. Experimental results show that 98.3% is achieved and that illustrate the applicability of the system in the security's average of different environments.

Factors influence on humus forming in Castelporziano Reserve, Mediterranean forest ecosystem

This contribution aimed to underline the relationship between humus forms, vegetation types and wild mammals disturbance in a Mediterranean forest ecosystem: Castelporziano Presidential Reserve- CPR (Rome, Italy). Humus forms were surveyed along a transect from coast to inland in five forest plant associations representative of evergreen and deciduous forests in the Reserve. The organic carbon-OC and other parameters (pH, Ntot, Moisture) were evaluated in the humus form horizons in the five associations to focus differences in the ecosystem functioning. Principal Component Analysis-PCA was performed on a floristic matrix of 113 plant species×32 relevés and Pearson correlation test was applied to the PCA axes coordinates versus 38 factors, among which 36 measured in the soil and two indicators of disturbance.This integrated study between soil and vegetation highlighted four main points: classification and functioning of the humus forms, ecological gradients, wild mammals impact, organic carbon storage.

Screening the Geographical Origin of Brazilian Wines According their Elemental Composition

Background: Multivariate statistical analyses were used to investigate the mineral contents of wines from the mid-São Francisco River valley, located between the northeastern Brazilian States of Bahia and Pernambuco, with those of wines from Rio Grande do Sul State. Methods: Ten elements (Al, Ba, Ca, Fe, K, Mg, Mn, Na, P, and Zn) were determined by inductively coupled plasma optical emission spectrometry (ICP-OES). The results were applied to Principal Components Analysis PCA) and and Hierarchical Cluster Analysis (HCA). Results: Considering the 28 wine samples analyzed, the elements Ca, Mg, K, and P were found in higher concentrations in the northeastern wines, with average contents of 71.6, 85.5, 632.9, and 191.2 mg L-1, respectively, and concentration ranges of 6.7-132.0, 53.7-120.7, 177.8-1071.9, and 79.5-376.1 mg L-1, respectively. Conversely, the elements Al, Ba, Fe, Mn, Na, and Zn were found in lower concentrations in the northeastern wines, with average contents of 0.53, 0.19, 1.31, 1.47, 0.69, and 0.46 mg L-1, respectively, and concentration ranges of 0.17-1.28, 0.03-0.46, 0.26-2.34, 0.41-2.73, 0.29-1.59, and 0.06-0.95 mg L-1, respectively. Conclusion: Principal component analysis and hierarchical cluster analysis revealed a natural distinction between the wines from the mid-Sao Francisco River valley and Rio Grande do Sul. Keywords: Brazilian wines, geographical origin, mineral contents, ICP-OES, PCA, HCA.

Radioecological modelling of Polonium-210 and Caesium-137 in lichen-reindeer-man and top predators

This work deals with analysis and modelling of the radionuclides 210Pb and210Po in the food-chain lichen-reindeer-man in addition to 210Po and 137Cs in top predators. By using the methods of Partial Least Square Regression (PLSR) the atmospheric deposition of 210Pb and 210Po is predicted at the sample locations. Dynamic modelling of the activity concentration with differential equations is fitted to the sample data. Reindeer lichen consumption, gastrointestinal absorption, organ distribution and elimination is derived from information in the literature. Dynamic modelling of transfer of 210Pb and 210Po to reindeer meat, liver and bone from lichen consumption, fitted well with data from Sweden and Finland from 1966 to 1971. The activity concentration of 210Pb in the skeleton in man is modelled by using the results of studying the kinetics of lead in skeleton and blood in lead-workers after end of occupational exposure. The result of modelling 210Pb and 210Po activity in skeleton matched well with concentrations of 210Pb and 210Po in teeth from reindeer-breeders and autopsy bone samples in Finland.The results of 210Po and 137Cs in different tissues of wolf, wolverine and lynx previously published, are analysed with multivariate data processing methods such as Principal Component Analysis PCA, and modelled with the method of Projection to Latent Structures, PLS, or Partial Least Square Regression PLSR.

Relaciones entre componentes morfológicos de palmito y atributos físicos de un inceptisol: una aproximación multivariada

El estudio del potencial agrícola del suelo desde una aproximación multivariada puede ayudar a interpretar las interacciones de sus atributos en relación con la producción de los cultivos. El objetivo de este estudio fue evaluar, mediante análisis multivariados, las relaciones entre atributos físicos del suelo y los componentes morfológicos del cultivo de palmito en un inceptisol eutric, en el municipio de Registro (São Paulo-Brasil). El experimento se realizó en el año agrícola 2013/2014 (para un cultivo que fue implantado en el año 2012), y en él se analizaron los atributos físicos del suelo a una profundidad de -0,10 m, en una grilla con 54 puntos georreferenciados. Se emplearon tres métodos multivariados: agrupación jerárquica, no jerárquica (k-means) y análisis de componentes principales. Los dos primeros componentes principales (CP1 y CP2) explicaron el 68,29 % de las variaciones del conjunto de datos originales, en los que la densidad del suelo y la porosidad total presentaron el mayor poder discriminatorio en CP1, y tan solo el diámetro de tallo presentó poder discriminatorio relevante con CP2. En el análisis de k-means se encontraron mayores valores de diámetro de tallo de palmito en la agrupación de puntos con mayores valores de resistencia mecánica a la penetración y menores valores de humedad gravimétrica. La estadística multivariada se mostró eficiente en la discriminación del conjunto de variables físicas del suelo estudiado que tiene mayor relación con los componentes morfológicos de la planta de palmito.

Effective elastic moduli of a heterogeneous oolitic rock containing 3-D irregularly shaped pores

We have characterized the microstructure of a heterogeneous oolitic rock (limestone from Lavoux, France) with X-ray nano-computed tomography. This rock comprises an assemblage of porous grains (oolites), irregularly shaped three-dimensional pores, and inter-oolitic crystals (cement). To model the effect of this microstructure on the macroscopic behavior of the rock, we approximate the porous oolites by spheres, and the irregularly shaped pores by ellipsoids. This approximation is performed based on the principal component analysis PCA, which provides the geometrical properties such as length of semi-axes and orientation of resulting ellipsoids. The sphericity of the approximated oolites was calculated and the value close to 1 allows us to consider oolites as spheres. To verify the approximation in the case of pores, we evaluated the contribution of these irregularly shaped three-dimensional pores to the overall elastic properties. Thus, compliance contribution tensors for 3D irregular pores and their ellipsoidal approximations are calculated using the finite element method. These tensors were compared to the compliance tensors for ellipsoids obtained using analytical solutions based on Eshelby's theory and a relative error is estimated to evaluate the accuracy of the approximation. This error produces a maximum discrepancy of 4.5% between the two solutions respectively of pores and ellipsoids which verifies the proposed approximation procedure based on principal component analysis. The FEA numerical method is verified by comparing the numerical solution of compliance contribution tensors of the ellipsoids to the known analytical solution of these same shapes based on Eshelby's theory. The difference between these two solutions does not exceed 3%. Compliance contribution tensors are finally used to compare effective elastic parameters of a material containing irregular pores via the Maxwell homogenization scheme. These elastic parameters (bulk modulus and shear coefficient) coincide with a maximum deviation of 5% with ones for a material with ellipsoidal pores.

Spatial Cepstrum as a Spatial Feature Using a Distributed Microphone Array for Acoustic Scene Analysis

In this paper, with the aim of using the spatial information obtained from a distributed microphone array employed for acoustic scene analysis, we propose a robust and efficient method, which is called the spatial cepstrum. In our approach, similarly to the cepstrum, which is widely used as a spectral feature, the logarithm of the amplitude in multichannel observation is converted to a feature vector by a linear orthogonal transformation. This linear orthogonal transformation is achieved by principal component analysis PCA in general. Moreover, we also show that for a circularly symmetric microphone arrangement with an isotropic sound field, PCA is identical to the inverse discrete Fourier transform and the spatial cepstrum exactly corresponds to the cepstrum. The proposed approach does not require the positions of the microphones and is robust against the synchronization mismatch of channels, thus ensuring its suitability for use with a distributed microphone array. Experimental results obtained using actual environmental sounds verify the validity of our approach even when a smaller feature dimension than the original one is used, which is achieved by dimensionality reduction through PCA. Additionally, experimental results also indicate that the robustness of the proposed method is satisfactory for observations that have the synchronization mismatch of channels.

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage.

基于R应用的县级城市综合竞争力比较分析—以新疆维吾尔自治区为例

城市之间的竞争是市场经济的产物，提高城市的综合竞争力已经吸引了来自各个城市的关注。而新疆地域辽阔，资源丰富，但地区间的经济差异十分显著，且新疆城市总体竞争力不高，而如今“一路一带”的构建，使得提高新疆城市的竞争力迫在眉睫。本文选取了6个一级指标和21个二级指标，构建指标体系，基于R软件运用主成分分析法对新疆19个县级市竞争力进行分层评价。在分析新疆城市竞争力综合排名的基础上，找出各县级市之间的差异，最终根据评价的结果提出提高各县级市竞争力的对策建议。 The competition between cities is the product of market economy. Improving the comprehensive competitiveness of the city has attracted attention from each city. Xinjiang region has vast territory and rich resources, but the economic differences between regions are very significant, and the Xinjiang overall urban competitiveness is not high. Now “area” all the way to build and improve the competitiveness of Xinjiang cities is imminent. This paper has chosen six first-level indexes and 21 secondary indexes, constructing index system, using principal component analysis (pca) based on the R software in Xinjiang for layered 19 county-level city competitiveness evaluation. On the basis of the analysis of Xinjiang’s comprehensive competitiveness ranking, we can find out the difference between each county, finally according to the result of evaluation, put forward some suggestions to enhance the competitiveness of each county.

An accurate traffic classification model based on support vector machines

Network traffic classification is a fundamental research topic on high-performance network protocol design and network operation management. Compared with other state-of-the-art studies done on the network traffic classification, machine learning ML methods are more flexible and intelligent, which can automatically search for and describe useful structural patterns in a supplied traffic dataset. As a typical ML method, support vector machines SVMs based on statistical theory has high classification accuracy and stability. However, the performance of SVM classifier can be severely affected by the data scale, feature dimension, and parameters of the classifier. In this paper, a real-time accurate SVM training model named SPP-SVM is proposed. An SPP-SVM is deducted from the scaling dataset and employs principal component analysis PCA to extract data features and verify its relevant traffic features obtained from PCA. By employing PCA algorithm to do the dimension extraction, SPP-SVM confirms the critical component features, reduces the redundancy among them, and lowers the original feature dimension so as to reduce the over fitting and increase its generalization effectively. The optimal working parameters of kernel function used in SPP-SVM are derived automatically from improved particle swarm optimization algorithm, which will optimize the global solution and make its inertia weight coefficient adaptive without searching for the parameters in a wide range, traversing all the parameter points in the grid and adjusting steps gradually. The performance of its two- and multi-class classifiers is proved over 2 sets of traffic traces, coming from different topological points on the Internet. Experiments show that the SPP-SVM's two- and multi-class classifiers are superior to the typical supervised ML algorithms and performs significantly better than traditional SVM in classification accuracy, dimension, and elapsed time.

Clonality and Mixed Mutational Signature in Aged Hematopoietic Stem Cells Via Single Cell Variant Analysis

Genomic stability and integrity in Hematopoietic Stem Cells (HSCs) is maintained via DNA damage checkpoints, DNA proofreading and DNA repair (Moehrle et al., 2015; Cell Rep). Despite these mechanisms, recurring and non-recurring mutations accumulate in HSCs upon aging, which correlate with an elevated incidence of myeloproliferative diseases (Rossi, Bryder, and Weissman, Exp. Gerontol. 2007) as well as changes in clonality (Akunuru and Geiger, 2016; Trends in Mol. Med.). The rate at which such mutations accumulate in individual HSCs and the selection advantage/disadvantage that they provide is unclear and is an active area of investigation. Evolutionary theory supports a strong influence of the aged niche on the selection of HSCs clones upon aging (Rozhok, Salstrom, and DeGregori, 2014; Aging). We hypothesized that variant profiling of single HSCs based on RNA transcripts will reveal mutational signatures adapted to the selection pressure of the aging microenvironment.We performed Single cell RNA-seq of daughter cell pairs from young and aged murine HSCs (LSK, CD34-, flk2-). The Genome Analysis Toolkit (GATK; Broad Institute) RNA-seq variant/mutation calling algorithm pipeline was applied with some modifications. Only variants that were observed in both daughter cells of a given pair were selected, which significantly decreased our false discovery rate (tested by a Monte Carlo simulation). First and most interestingly, we observed no significant difference in the overall number of variants/mutations between young and aged HSCs, further supporting our recently published observations on the frequency of DNA mutations in HSCs upon aging (Moehrle et al., 2015; Cell Rep). We then used an approach that takes into account the 3’ and 5’ bases flanking a variant to generate motifs whose frequencies can be mathematically analyzed to deduce characteristic mutational patterns, termed as mutational signatures (Nik-Zainal et al., 2012; Cell). We employed a non-negative matrix factorization (nmf) and principal component analysis (pca) algorithms to generate 10 mutational signatures that explained > 95% of the variance in the dataset. We then analyzed the signatures in a pairwise fashion and selected two signatures with the highest discrimination score between young and aged HSC. Based on this, cells fell into two major groups: group 1 predominantly contained aged single cells (~90% of the cells in this group) whereas, interestingly, group 2 contained a mix of young and aged HSCs. The segregation of young and aged single HSCs counts between groups 1 and 2 was tested using Fisher’s exact test and was statistically significant (p-value 0.0029). These data indicate that while the overall mutational load is not elevated, majority of aged HSCs acquire a mutational signature distinct from young HSCs, while a proportion of aged HSCs present with a young-like HSC signature. Furthermore, our results show that even those cells that have acquired an aging signature aren’t homogeneous and show sub-clustering tendencies, providing the first hint that they may potentially evolve further into more distinct clones. In conclusion, our results show that individual HSCs reflect a mixed mutational profile reminiscent of a non-uniform accumulation of variants. As such signatures are a reflection of underlying mechanisms by which the mutations accumulate (Nik-Zainal et al., 2012; Cell), the proportion of aged HSCs sharing similar mutational signatures but distinct from the young HSCs reveal an aging signature that indicates specific mutational factors and selection pressure of the aging microenvironment. DisclosuresMulaw:NuGEN: Honoraria.

USING REMOTE SENSING FOR LINEAMENT EXTRACTION IN Al MAGHRABAH AREA - HAJJAH, YEMEN

Abstract. Remote sensing and GIS data have become more and more important for the study of geology, structural geology and extract of lineament which give us an overview of the tectonic events. The main objective of our study is to design a method for extracting and mapping of lineament from Landsat 8_OLI satellite images of the study area. Different processing techniques were used to achieve our objective : MNF Minimum Noise Fraction bands (4,5,6), Color composites (7,5,3), PCA Principal Component Analysis bands (4,5,7), band ratios (7/5,6/4,4/2), directional filters, and image processing applied on the Landsat 8_OLI composite colours. The results of this study indicate that, the area is really affected by several structural trends: N-S, NW–SE, and NE–SW directions. The result of lineament analysis gives us a good interpretation of the main structural geology and tectonic forces.

A Novel Approach to Estimate the Radius Ratio of an Elastic Tube from Its Form Function Using Soft Computing Techniques

A number of researches in different areas has been conducted on using the Principal Component Analysis PCA combined with Artificial Neural Networks ANN. In this current research, we use these two techniques to estimate the radius ratio b/a (a: outer radius, b: inner radius) of an elastic tube based on its form function. The PCA technique is used to estimate the component loadings corresponding to form function. Then, the component loadings it's used in the ANN technique to estimate the radius ratio of the tube. To get the optimal network, several configurations are implemented and tested. The optimal configuration selected is a network with 16 inputs, 2 hidden layers composed of 4 and 1 neurons respectively, and trained by the back-propagation algorithm. This configuration is able to estimate the radius ratio �� /�� with a mean absolute error MAE of about 0.0024 and a mean square error MSE of 0.0008. This study reveals benefits of the combination between PCA and ANN, and also it provides some new ideas for further researches. This current work can be used as a novel approach for the characterization of an elastic tube.

Different Aspects in Partial Least Squares and Artificial Neural Network Models Used for the Analysis of Cefoperazone Sodium in Presence of its Alkaline Degradation Product

Several chemometric models were used for the determination of cefoperazone sodium in presence of its alkaline degradation product. The methods are either traditional (Partial Least Squares) or advanced (Artificial Neural Network). Partial Least Squares method was used with and without variable selection (Genetic Algorithm GA). Artificial Neural Network (ANN) was used with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (principal component analysis PCA). The chemometric methods used are PLS-1, GA-PLS-1, ANN, GA-ANN and PCA-ANN. The methods were used for the determination of cefoperazone sodium in bulk powder and pharmaceutical preparation. A 2-factor 5-level experimental design was built leading to 25 mixtures containing different ratios of cefoperazone sodium and its alkaline degradation product. Thirteen mixtures were used as a training set and the other twelve were used as a validation set.

Is there a convergence of deciduous leaf litter stoichiometry, biochemistry and microbial population during decay?

Litter decomposition is driven by saprotrophic microbial communities. The structure of this community varies over the course of decomposition, which has been linked to the chemistry of litter resources. Here, we test the hypothesis that leaf litter from different tree species becomes more similar in terms of litter stoichiometry, C-biochemistry during the course of decomposition (convergence).We also test if these variations can be linked to shifts in microbial community structure and abundance. For this reason, leaf litter of four deciduous tree species — beech (Fagus sylvatica), oak (Quercus robur), alder (Alnus glutinosa) and ash (Fraxinus excelsior) were sampled from a temperate forest in February, May and September 2010. We measured variations in leaf litter stoichiometry by elemental concentrations, C-biochemistry with Fourier transform infrared (FTIR) spectroscopy and litter microbial community structure and abundance with phospholipid fatty acid (PLFA) analysis. For litter from beech, oak and ash, C:N and C:P ratio converged with time when exposed to the same environmental conditions during degradation. In contrast, C:nutrient ratios increased in alder litter over time. Litter C-biochemistry was to a large extent related to shifts in C:N ratios. Principal component analysis PCA of FTIR bands, revealed that divergence in C-biochemistry over the course of decomposition was dependent on litter type, and hence, with initial C:N ratio. Litter-decomposing bacterial community was highly relevant to C-biochemistry and environmental conditions (temperature and moisture) while fungi community was more nutrients-related.In conclusion, initially wide C:nutrient ratios tend to converge during decay; while nutrient limitation may lead to divergence of stoichiometry. In general, there was a trend towards divergence of C-biochemistry and microbial community structure.