An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

Mining frequent biological sequences based on bitmap without candidate sequence generation

Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability

Horizontal gene transfer contributed to the evolution of extracellular surface structures

The single-cell layered ectoderm of the fresh water polyp Hydra fulfills the function of an epidermis by protecting the animals from the surrounding medium. Its outer surface is covered by a fibrous structure termed the cuticle layer, with similarity to the extracellular surface coats of mammalian epithelia. In this paper we have identified molecular components of the cuticle. We show that its outermost layer contains glycoproteins and glycosaminoglycans and we have identified chondroitin and chondroitin-6-sulfate chains. In a search for proteins that could be involved in organising this structure we found PPOD proteins and several members of a protein family containing only SWT (sweet tooth) domains. Structural analyses indicate that PPODs consist of two tandem β-trefoil domains with similarity to carbohydrate-binding sites found in lectins. Experimental evidence confirmed that PPODs can bind sulfated glycans and are secreted into the cuticle layer from granules localized under the apical surface of the ectodermal epithelial cells. PPODs are taxon-specific proteins which appear to have entered the Hydra genome by horizontal gene transfer from bacteria. Their acquisition at the time Hydra evolved from a marine ancestor may have been critical for the transition to the freshwater environment

“Bird Song Metronomics”: Isochronous Organization of Zebra Finch Song Rhythm

The human capacity for speech and vocal music depends on vocal imitation. Songbirds, in contrast to non-human primates, share this vocal production learning with humans. The process through which birds and humans learn many of their vocalizations as well as the underlying neural system exhibit a number of striking parallels and have been widely researched. In contrast, rhythm, a key feature of language, and music, has received surprisingly little attention in songbirds. Investigating temporal periodicity in bird song has the potential to inform the relationship between neural mechanisms and behavioral output and can also provide insight into the biology and evolution of musicality. Here we present a method to analyze birdsong for an underlying rhythmic regularity. Using the intervals from one note onset to the next as input, we found for each bird an isochronous sequence of time stamps, a “signal-derived pulse,” or pulseS, of which a subset aligned with all note onsets of the bird's song. Fourier analysis corroborated these results. To determine whether this finding was just a byproduct of the duration of notes and intervals typical for zebra finches but not dependent on the individual duration of elements and the sequence in which they are sung, we compared natural songs to models of artificial songs. Note onsets of natural song deviated from the pulseS significantly less than those of artificial songs with randomized note and gap durations. Thus, male zebra finch song has the regularity required for a listener to extract a perceived pulse (pulseP), as yet untested. Strikingly, in our study, pulsesS that best fit note onsets often also coincided with the transitions between sub-note elements within complex notes, corresponding to neuromuscular gestures. Gesture durations often equaled one or more pulseS periods. This suggests that gesture duration constitutes the basic element of the temporal hierarchy of zebra finch song rhythm, an interesting parallel to the hierarchically structured components of regular rhythms in human music

Quantitative genome-wide studies of RNA metabolism in yeast

Gene expression and its regulation are fundamental processes in every living cell and organism. RNA molecules hereby play a central role by translating the genetic information into proteins, by regulating gene activity and by forming structural components. The kinetics of RNA metabolism differ widely between genes and conditions and play an important role for cellular processes, but how this is achieved remains poorly understood. Here, we used a novel experimental protocol that allows profiling of newly transcribed RNAs in conjunction with an advanced computational modeling pipeline to explore the kinetics of RNA metabolism and the underlying genetic determinants.In the first study, we investigated cell cycle regulated gene expression and the contributions of synthesis and degradation to mRNA levels in S.cerevisiae. During the cell cycle, the levels of hundreds of mRNAs change in a periodic manner, but how this is carried out by alterations in the rates of mRNA synthesis and degradation has not been studied systematically. We were able to derive mRNA synthesis and degradation rates every 5 minutes during the cell cycle, and thus provide for the first time a high-resolution time series of RNA metabolism during the cell cycle. A novel statistical model identified 479 genes that show periodic changes in mRNA synthesis and generally also periodic changes in their mRNA degradation rates. Peaks of mRNA degradation follow peaks of mRNA synthesis, resulting in sharp and high peaks of mRNA levels at defined times during the cell cycle. Whereas the timing of mRNA synthesis is set by upstream DNA motifs and their associated transcription factors (TFs), the synthesis rate of a periodically expressed gene is apparently set by its core promoter. In the second study, we developed metabolic labeling with RNA-Seq (4tU-Seq) and novel computational methods to gain further insights into the kinetics of RNA metabolism and its regulation. To decrypt the regulatory code of the genome, sequence elements must be defined that determine RNA turnover and thus gene expression. Here we attempt such decryption in an eukaryotic model organism, the fission yeast S. pombe. We first derived an improved genome annotation that redefines borders of 36% of expressed mRNAs and adds 487 non-coding RNAs (ncRNAs). We then combined RNA labeling in-vivo with mathematical modeling to obtain rates of RNA synthesis and degradation for 5,484 expressed RNAs and splicing rates for 4,958 introns. We identified functional sequence elements in DNA and RNA that control RNA metabolic rates, and quantified the contributions of individual nucleotides to RNA synthesis, splicing, and degradation. Our approach reveals distinct kinetics of mRNA and ncRNA metabolism, separates antisense regulation by transcription interference from RNA interference, and provides a general tool for studying the regulatory code of genomes

GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses

The task of gene identification frequently confronting researchers working with both novel and well studied genomes can be conveniently and reliably solved with the help of the GeneMark web software (). The website provides interfaces to the GeneMark family of programs designed and tuned for gene prediction in prokaryotic, eukaryotic and viral genomic sequences. Currently, the server allows the analysis of nearly 200 prokaryotic and >10 eukaryotic genomes using species-specific versions of the software and pre-computed gene models. In addition, genes in prokaryotic sequences from novel genomes can be identified using models derived on the spot upon sequence submission, either by a relatively simple heuristic approach or by the full-fledged self-training program GeneMarkS. A database of reannotations of >1000 viral genomes by the GeneMarkS program is also available from the web site. The GeneMark website is frequently updated to provide the latest versions of the software and gene models

Musculoskeletal Load Exposure Estimation by Non-supervised Annotation of Events on Motion Data

There is a significant number of work pressures that promote the incidence of musculoskeletal disorders in industrial environments. As, unfortunately, many workplace conditions are subject to these biomechanical hazards, this has become an extensively common health disorder. To properly adjust intervention strategies, an ergonomic assessment through surveillance measurements is required. However, most measurements still depend on subjective assessment tools like self-reporting and expert observation. The ideal approach for this scenario would be to use direct measurements that use sensors to retrieve more precise/accurate information of how workers interact with their work environment. Following this approach, one of the major constraints would be that a systematic retrieval of data from a labor environment would require a tiresome process of analysis and manual annotation, deviating resources and requiring data analysts. Hence, this work proposes an unsupervised methodology able to automatically annotate relevant events from direct acquisitions, with the final intent of promoting this type of analysis. The event detection methodology proposes to detect three different event types: 1) work period transition; 2) work cycle transition; and 3) sub-sequence matching by query. To achieve this, the multivariate time series are represented as a Self-Similarity matrix built with the features extracted. This matrix is analysed for each event needed to be searched. The results were successful in the segmentation of Active and Non-active working periods and in the detection of points of transition between repetitive human motions, i.e. work cycles. A method of search-by-example is also presented, being that it allows for the user to detect specific motions of interest. Although this method could still be further optimized in future work, this approach has a very promising prospect as it proposes a strategy of similarity analysis that has not yet been deeply explored in the context of ergonomic acquisition. These advances are also significant given that the summarization of ergonomic data is still a subject in expansion.Num contexto industrial, são várias as tensões que promovem a incidência de distúrbios musculosqueléticos. Uma vez que a maioria das condições laborais estão sujeitas a estas propensões do foro biomecânico, os distúrbiosmusculosqueléticos tornaram-se patologias amplamente diagnosticadas na população ativa. Para desenhar estratégias de intervenção eficientes, é necessário proceder a uma avaliação ergonómica baseada em metododologias de vigilância. Não obstante o reconhecimento desta necessidade, a maioria das medidas ainda depende de ferramentas subjetivas como a auto-avaliação e a observação externa por parte de especialistas. A abordagem preferencial para esta problemática passaria pela aplicação de medições diretas que recorressem a sensores com vista a extrair informação exata e fidedigna do ambiente laboral. Uma das maiores limitações deste leque de soluções consiste no facto de um sistema de recolha de dados neste ambiente implicar um processo exaustivo de análise e anotação manual, o que consome recursos e requer os serviços de analistas de dados. Assim, este trabalho propõe uma metodologia capaz de anotar automaticamente eventos relevantes provenientes de aquisições diretas, com o objetivo final de promover este tipo de análises mais eficientes. A metodologia de deteção de eventos proposta foca-se em três diferentes tipos de eventos: 1) transições entre tarefas; 2) transições entre ciclos de trabalho; e 3) procura de movimentos-exemplo em amostras segmentadas. Para concretizar este trabalho, realizou-se um estudo de matrizes de auto-semelhança. Os resultados provaram-se, na sua maioria, bem-sucedidos no caso da segmentação de períodos Ativos e Não-ativos e na deteção de momentos de transição entre movimentos repetitivos, isto é, ciclos de trabalho. É ainda apresentado um método de procura-porexemplo que permite ao utilizador detetar movimentos-exemplo do seu interesse. Embora este método possa ainda ser otimizado em trabalhos futuros, reflete uma abordagem promissora uma vez que propõe uma estratégia de análise de similaridade que não foi ainda especialmente explorada no contexto dos estudos ergonómicos. Estes avanços são ainda significantes na perspetiva de que a sumarização de dados ergonómicos é uma linha de investigação ainda em expansão