Extrachromosomal microDNAs and chromosomal microdeletions in normal tissues

We have identified tens of thousands of short extrachromosomal circular DNAs (microDNA) in mouse tissues as well as mouse and human cell lines. These microDNAs are 200 to 400 base pairs long, are derived from unique nonrepetitive sequence, and are enriched in the 5'-untranslated regions of genes, exons, and CpG islands. Chromosomal loci that are enriched sources of microDNA in the adult brain are somatically mosaic for microdeletions that appear to arise from the excision of microDNAs. Germline microdeletions identified by the "Thousand Genomes" project may also arise from the excision of microDNAs in the germline lineage. We have thus identified a previously unknown DNA entity in mammalian cells and provide evidence that their generation leaves behind deletions in different genomic loci.     

Engineering design of woven fabrics using non-traditional optimization methods: A comparative study

This work aims to design woven fabrics with desired quality at optimum manufacturing cost by choice of suitable weaving parameters such as count, crimp and thread spacing of warp and weft yarns. To fulfill this goal, we endeavor to devise search based non-traditional optimization methods such as genetic algorithm, particle swarm optimization and simulated annealing for efficiently finding the appropriate combination of weave parameters. The quick response capability of the non-traditional optimization methods would benefit the fabric manufacturers for efficient determination of the required weaving parameters to produce the engineered fabrics. The experimental validation confirms that the particle swarm optimization is most suitable technique for engineering design of woven fabrics.     

Analgesic, anti-inflammatory and antipyretic activities of the ethanolic extract and its fractions of Cleome rutidosperma

Oral administration of the ethanolic extract (200 and 400 mg/kg, p.o) and its fractions (200 mg/kg each) of the aerial parts of Cleome rutidosperma produced significant analgesic activity in acetic acid-induced writhing and tail immersion tests, anti-inflammatory effect against carrageenin induced inflammation and adjuvant induced polyarthritis and antipyretic activity against yeast-induced pyrexia. Fractionation of the ethanolic extract potentiated the activities.     

Classification of yarn interlacement pattern in fabrics using least square support vector machines

The purpose of this paper is to suggest an effective and reliable tool that can read through fabric images in the quest of deciphering yarn interlacement patterns by means of Least-Square Support Vector Machines (LS-SVM). A LS-SVM based binary pattern recognition system is formulated for identifying two modes of yarn interlacements viz., warp over weft or warp under weft and accuracy of the classifier was assessed by k-fold cross validation techniques. A comparative study establishes that LS-SVM shows better result than the standard SVM while classifying yarn interlacement patterns in fabrics. The proposed method has the potential to classify yarn interlacement patterns with possibility of extending it to designdecoding of diverse fabrics.     

Empirical modelling of tensile strength of woven fabrics

Aesthetic properties of fabrics have been considered as the most important fabric attribute for years. However, recently there has been a paradigm shift in the domain of textile material applications and consequently more emphasis is now being given on the mechanical and functional properties of fabrics rather than its aesthetic appeal. Moreover, in certain woven fabrics used for technical applications, strength is a decisive quality parameter. In this work, tensile strength of plain woven fabrics has been predicted by using two empirical modelling methods namely artificial neural network (ANN) and linear regression. Warp yarn strength, warp yarn elongation, ends per inch (EPI), picks per inch (PPI) and weft count (Ne) were used as input parameters. Both the models were able to predict the fabric strength with reasonably good precision although ANN model demonstrated higher prediction accuracy and generalization ability than the regression model. The warp yarn strength and EPI were found to be the two most significant factors influencing fabric strength in warp direction.     

Yarn engineering using hybrid artificial neural network-genetic algorithm model

This work aims to manufacture cotton yarns with requisite quality by choice of suitable raw materials for a given spinning system. To fulfill this aim, a hybrid model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) has been developed which captures both the high prediction power of ANN and global solution searching ability of GA. In an attempt to achieve a yarn having predefined tenacity and evenness, a constrained optimization problem is formulated with the ANN input-output relation between fibre and yarn properties. GA has been used to solve the optimization problem by searching the best combination of fibre properties that can translate into reality a yarn with the desired quality. The model is capable in identifying the set of fibre properties that gives requisite yarn quality with reasonable degree of accuracy.     

Prediction of cotton yarn properties using support vector machine

This paper presents a support vector machine (SVM) regression approach to forecast the properties of cotton yarns produced on ring and rotor spinning technologies from the fibre properties measured by HVI and AFIS. Prediction performance of SVM models have been compared against those of the artificial neural network (ANN) models. A k-fold cross validation technique is applied to assess the expected generalization accuracies of both SVM and ANN models. The investigation indicates that the yarn properties can be predicted with a very high degree of accuracy using SVM models and the prediction performance of SVM models are better than that of ANN models.     

A technique of cotton bale laydown using clustering algorithm

A new technique of cotton bale management using clustering algorithm has been proposed. The method is based on the grouping cotton bales of similar kind into respective categories using k-mean square clustering algorithm. A set of 500 cotton bales were clustered into 5 categories by minimizing the total within-group squared Euclidean distance around the 5 centroids. In order to cluster bales of different categories, 8 fibre properties, viz., strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered. Once the bales are clustered into different categories, it is possible to prepare consistent bale mix for consecutive laydowns on the basis of frequency relative picking method.     

Investigation of inter fiber cohesion in yarns. II. Influence of fiber and process parameters on the cohesion in man made and blended yarns

This paper discusses the inter fiber cohesion in man made and blended yarns. The fiber parameters such as fiber length and fineness influence the cohesion. Studies have been focused on polyester and viscose spun yarns. Though polyester and viscose yarns show similar trend in cohesion, viscose yarns exhibit better cohesion due to their serrated cross section. Studies on the effect of blend proportion of polyester cotton and polyester viscose yarns reveal that increase of polyester and viscose in the respective blends improve the inter fiber cohesion.     

Investigation of inter fiber cohesion in yarns. I. Influence of certain spinning parameters on the cohesion in cotton yarns

This paper investigates the influence of raw material and process parameters in spinning that affect the inter fiber cohesion in yarns. An instrument has been developed for measuring the minimum twist of cohesion. With regard to the raw material parameters, the influence of different cotton fiber mixings for a given count of yarn is investigated. Also the effect of spinning to varying counts for a given cotton variety is studied. With regard to the process parameters, studies have been carried out to investigate the influence of noil extraction in comber, number of draw frame passages, draft pressure in ring frame and direction of twist. Cohesion improved with increase in the noil extraction percentage in the comber. Increase in the number of draw frame passages also improved the cohesion. Draft pressure in ring frame improved the fiber cohesion in yarn up to a pressure of 2.1 kg/cm². Direction of twist had no effect on the cohesion.