Coding mode decision algorithm for binary descriptor coding

In visual sensor networks, local feature descriptors can be computed at the sensing nodes, which work collaboratively on the data obtained to make an efficient visual analysis. In fact, with a minimal amount of computational effort, the detection and extraction of local features, such as binary descriptors, can provide a reliable and compact image representation. In this paper, it is proposed to extract and code binary descriptors to meet the energy and bandwidth constraints at each sensing node. The major contribution is a binary descriptor coding technique that exploits the correlation using two different coding modes: Intra, which exploits the correlation between the elements that compose a descriptor; and Inter, which exploits the correlation between descriptors of the same image. The experimental results show bitrate savings up to 35% without any impact in the performance efficiency of the image retrieval task. © 2014 EURASIP.

Mach bands and multiscale models of spatial vision:the role of first, second, and third derivative operators in encoding bars and edges

Ernst Mach observed that light or dark bands could be seen at abrupt changes of luminance gradient in the absence of peaks or troughs in luminance. Many models of feature detection share the idea that bars, lines, and Mach bands are found at peaks and troughs in the output of even-symmetric spatial filters. Our experiments assessed the appearance of Mach bands (position and width) and the probability of seeing them on a novel set of generalized Gaussian edges. Mach band probability was mainly determined by the shape of the luminance profile and increased with the sharpness of its corners, controlled by a single parameter (n). Doubling or halving the size of the images had no significant effect. Variations in contrast (20%-80%) and duration (50-300 ms) had relatively minor effects. These results rule out the idea that Mach bands depend simply on the amplitude of the second derivative, but a multiscale model, based on Gaussian-smoothed first- and second-derivative filtering, can account accurately for the probability and perceived spatial layout of the bands. A key idea is that Mach band visibility depends on the ratio of second- to first-derivative responses at peaks in the second-derivative scale-space map. This ratio is approximately scale-invariant and increases with the sharpness of the corners of the luminance ramp, as observed. The edges of Mach bands pose a surprisingly difficult challenge for models of edge detection, but a nonlinear third-derivative operation is shown to predict the locations of Mach band edges strikingly well. Mach bands thus shed new light on the role of multiscale filtering systems in feature coding. © 2012 ARVO.

A single theoretical framework for circular features processing in humans: orientation and direction of motion compared

Common computational principles underlie processing of various visual features in the cortex. They are considered to create similar patterns of contextual modulations in behavioral studies for different features as orientation and direction of motion. Here, I studied the possibility that a single theoretical framework, implemented in different visual areas, of circular feature coding and processing could explain these similarities in observations. Stimuli were created that allowed direct comparison of the contextual effects on orientation and motion direction with two different psychophysical probes: changes in weak and strong signal perception. One unique simplified theoretical model of circular feature coding including only inhibitory interactions, and decoding through standard vector average, successfully predicted the similarities in the two domains, while different feature population characteristics explained well the differences in modulation on both experimental probes. These results demonstrate how a single computational principle underlies processing of various features across the cortices.

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

A feed-forward spiking model of shape-coding by IT cells

The ability to recognize a shape is linked to figure-ground (FG) organization. Cell preferences appear to be correlated across contrast-polarity reversals and mirror reversals of polygon displays, but not so much across FG reversals. Here we present a network structure which explains both shape-coding by simulated IT cells and suppression of responses to FG reversed stimuli. In our model FG segregation is achieved before shape discrimination, which is itself evidenced by the difference in spiking onsets of a pair of output cells. The studied example also includes feature extraction and illustrates a classification of binary images depending on the dominance of vertical or horizontal borders.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance.

Identification of the coding region for a second poly(A) polymerase in Escherichia coli.

We had earlier identified the pcnB locus as the gene for the major Escherichia coli poly(A) polymerase (PAP I). In this report, we describe the disruption and identification of a candidate gene for a second poly(A) polymerase (PAP II) by an experimental strategy which was based on the assumption that the viability of E. coli depends on the presence of either PAP I or PAP II. The coding region thus identified is the open reading frame f310, located at about 87 min on the E. coli chromosome. The following lines of evidence support f310 as the gene for PAP II: (i) the deduced peptide encoded by f310 has a molecular weight of 36,300, similar to the molecular weight of 35,000 estimated by gel filtration of PAP II; (ii) the deduced f310 product is a relatively hydrophobic polypeptide with a pI of 9.4, consistent with the properties of partially purified PAP II; (iii) overexpression of f310 leads to the formation of inclusion bodies whose solubilization and renaturation yields poly(A) polymerase activity that corresponds to a 35-kDa protein as shown by enzyme blotting; and (iv) expression of a f310 fusion construct with hexahistidine at the N-terminus of the coding region allowed purification of a poly(A) polymerase fraction whose major component is a 36-kDa protein. E. coli PAP II has no significant sequence homology either to PAP I or to the viral and eukaryotic poly(A) polymerases, suggesting that the bacterial poly(A) polymerases have evolved independently. An interesting feature of the PAP II sequence is the presence of sets of two paired cysteine and histidine residues that resemble the RNA binding motifs seen in some other proteins.

Casemix accounting systems and medical coding:organisational actors balanced on "leaky black boxes"

The adoption of DRG coding may be seen as a central feature of the mechanisms of the health reforms in New Zealand. This paper presents a story of the use of DRG coding by describing the experience of one major health provider. The conventional literature portrays casemix accounting and medical coding systems as rational techniques for the collection and provision of information for management and contracting decisions/negotiations. Presents a different perspective on the implications and effects of the adoption of DRG technology, in particular the part played by DRG coding technology as a part of a casemix system is explicated from an actor network theory perspective. Medical coding and the DRG methodology will be argued to represent ``black boxes''. Such technological ``knowledge objects'' provide strong points in the networks which are so important to the processes of change in contemporary organisations.

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

Non-coding RNAs in schistosomes: an unexplored world

Non-coding RNAs (ncRNAs) were recently given much higher attention due to technical advances in sequencing which expanded the characterization of transcriptomes in different organisms. ncRNAs have different lengths (22 nt to >1, 000 nt) and mechanisms of action that essentially comprise a sophisticated gene expression regulation network. Recent publication of schistosome genomes and transcriptomes has increased the description and characterization of a large number of parasite genes. Here we review the number of predicted genes and the coverage of genomic bases in face of the public ESTs dataset available, including a critical appraisal of the evidence and characterization of ncRNAs in schistosomes. We show expression data for ncRNAs in Schistosoma mansoni. We analyze three different microarray experiment datasets: (1) adult worms' large-scale expression measurements; (2) differentially expressed S. mansoni genes regulated by a human cytokine (TNF-α) in a parasite culture; and (3) a stage-specific expression of ncRNAs. All these data point to ncRNAs involved in different biological processes and physiological responses that suggest functionality of these new players in the parasite's biology. Exploring this world is a challenge for the scientists under a new molecular perspective of host-parasite interactions and parasite development.

The expression of genes coding for distinct types of glycine-rich proteins varies according to the biology of three metastriate ticks, Rhipicephalus (Boophilus) microplus, Rhipicephalus sanguineus and Amblyomma cajennense

Background: Ticks secrete a cement cone composed of many salivary proteins, some of which are rich in the amino acid glycine in order to attach to their hosts' skin. Glycine-rich proteins (GRPs) are a large family of heterogeneous proteins that have different functions and features; noteworthy are their adhesive and tensile characteristics. These properties may be essential for successful attachment of the metastriate ticks to the host and the prolonged feeding necessary for engorgement. In this work, we analyzed Expressed Sequence Tags (ESTs) similar to GRPs from cDNA libraries constructed from salivary glands of adult female ticks representing three hard, metastriate species in order to verify if their expression correlated with biological differences such as the numbers of hosts ticks feed on during their parasitic life cycle, whether one (monoxenous parasite) or two or more (heteroxenous parasite), and the anatomy of their mouthparts, whether short (Brevirostrata) or long (Longirostrata). These ticks were the monoxenous Brevirostrata tick, Rhipicephalus (Boophilus) microplus, a heteroxenous Brevirostrata tick, Rhipicephalus sanguineus, and a heteroxenous Longirostrata tick, Amblyomma cajennense. To further investigate this relationship, we conducted phylogenetic analyses using sequences of GRPs from these ticks as well as from other species of Brevirostrata and Longirostrata ticks. Results: cDNA libraries from salivary glands of the monoxenous tick, R. microplus, contained more contigs of glycine-rich proteins than the two representatives of heteroxenous ticks, R. sanguineus and A. cajennense (33 versus, respectively, 16 and 11). Transcripts of ESTs encoding GRPs were significantly more numerous in the salivary glands of the two Brevirostrata species when compared to the number of transcripts in the Longirostrata tick. The salivary gland libraries from Brevirostrata ticks contained numerous contigs significantly similar to silks of true spiders (17 and 8 in, respectively, R. microplus and R. sanguineus), whereas the Longirostrata tick contained only 4 contigs. The phylogenetic analyses of GRPs from various species of ticks showed that distinct clades encoding proteins with different biochemical properties are represented among species according to their biology. Conclusions: We found that different species of ticks rely on different types and amounts of GRPs in order to attach and feed on their hosts. Metastriate ticks with short mouthparts express more transcripts of GRPs than a tick with long mouthparts and the tick that feeds on a single host during its life cycle contain a greater variety of these proteins than ticks that feed on several hosts.

Report of a chimeric origin of transposable elements in a bovine-coding gene

Despite the wide distribution of transposable elements (TEs) in mammalian genomes, part of their evolutionary significance remains to be discovered. Today there is a substantial amount of evidence showing that TEs are involved in the generation of new exons in different species. In the present study, we searched 22,805 genes and reported the occurrence of TE-cassettes in coding sequences of 542 cow genes using the RepeatMasker program. Despite the significant number (542) of genes with TE insertions in exons only 14 (2.6%) of them were translated into protein, which we characterized as chimeric genes. From these chimeric genes, only the FAST kinase domains 3 (FASTKD3) gene, present on chromosome BTA 20, is a functional gene and showed evidence of the exaptation event. The genome sequence analysis showed that the last exon coding sequence of bovine FASTKD3 is similar to 85% similar to the ART2A retrotransposon sequence. In addition, comparison among FASTKD3 proteins shows that the last exon is very divergent from those of Homo sapiens, Pan troglodytes and Canis familiares. We suggest that the gene structure of bovine FASTKD3 gene could have originated by several ectopic recombinations between TE copies. Additionally, the absence of TE sequences in all other species analyzed suggests that the TE insertion is clade-specific, mainly in the ruminant lineage.

Singing in the Rain Forest: How a Tropical Bird Song Transfers Information

How information transmission processes between individuals are shaped by natural selection is a key question for the understanding of the evolution of acoustic communication systems. Environmental acoustics predict that signal structure will differ depending on general features of the habitat. Social features, like individual spacing and mating behavior, may also be important for the design of communication. Here we present the first experimental study investigating how a tropical rainforest bird, the white-browed warbler Basileuterus leucoblepharus, extracts various information from a received song: species-specific identity, individual identity and location of the sender. Species-specific information is encoded in a resistant acoustic feature and is thus a public signal helping males to reach a wide audience. Conversely, individual identity is supported by song features susceptible to propagation: this private signal is reserved for neighbors. Finally, the receivers can locate the singers by using propagation-induced song modifications. Thus, this communication system is well matched to the acoustic constraints of the rain forest and to the ecological requirements of the species. Our results emphasize that, in a constraining acoustic environment, the efficiency of a sound communication system results from a coding/decoding process particularly well tuned to the acoustic properties of this environment.

Feature selection environment for genomic applications

Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e. g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application. Results: The intent of this work is to provide an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes ( targets or predictors) is also implemented in the system. Conclusion: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.