Success Rate Of Identification Research Articles

Can local avatars satisfy a global audience? A case study of high-fidelity 3D facial avatar animation in subject identification and emotion perception by US and international groups

This study investigates effectiveness of a local high-fidelity 3D facial avatar for a global audience by observing how US and International student groups differed in identifying subjects and perceiving emotions while viewing nonverbal high-fidelity 3D facial avatar animations embedded with the motion data of three US individuals. To synthesize the animated 3D avatars to convey highly believable facial expressions, a 3D scanned facial model was mapped with high-fidelity motion-capture data of three native US subjects as they spoke designated English sentences with specified emotions. Simple animations in conjunction with actual footage of the subjects speaking during the facial motion-capture sessions were shown several times to both native US and international students in similar settings. After a familiarization process, we showed the students randomly arranged talking avatars without voices and asked them to identify the corresponding identities and emotional types of the subjects whose facial expressions were utilized in the creation of the avatars, and to rate their confidence in their selections. We found that the US group had higher success rates in subject identification, although the related difference in confidence ratings between two groups was not significant. The differences in the success rates and confidence ratings on the perception of emotion between the two groups were not significant either. The results of our study provide interesting insights into avatar-based interaction where the national and/or cultural background of a person impacts the perception of identity while having little effect on the perception of emotion. However, we observed that dynamics (e.g., head motion) could offset the disadvantage of cultural unfamiliarity in subject identification. We observed that both groups performed at a nearly identical level in subject identification and emotion perception when they were shown the avatar animation with heightened expression and dynamic intensities. In addition, we observed that the confidence ratings were correlated to accuracy in identifying the subject but not to accuracy in perceiving emotion.

Infrared target-flare discrimination using a ZISC hardware neural network

The last generation of infrared imaging aircraft seekers and trackers uses pattern recognition algorithms to find and keep a lock on an aircraft in the presence of decoy flares. These algorithms identify targets, based on the features of the various objects in the missile’s field of view. Because modern both aircrafts and missiles fly faster than sound, speed of operation of the target identifier is critical. In this article, we propose a target recognition system that respects this time constraint. It is based on an artificial neural network implemented in hardware, as a set of parallel processors on a commercially available silicon chip called a ZISC, for zero instruction set computer. This chip would be integrated in the infrared missile seeker and tracker. We describe the characteristics of the images that the image processing module of this seeker and tracker extracts from the infrared video frames and show how to construct from these translation and rotation invariant features that can be used as input to the neural network. We determine the individual discriminating power of these features by constructing their histograms, which allows us to eliminate some as not being useful for our purpose. Finally, by testing our system on real data, we show that it has a 90% success rate in aircraft-flare identification, and a processing time that during this time, the aircrafts and missiles will have traveled only a few millimeters. Most of the images on which the neural network makes its mistakes are seen to be hard to recognize even by a human expert.

Breast Cancer: Sentinel Node Identification and Classification after Neoadjuvant Chemotherapy—Systematic Review and Meta Analysis

Breast cancer is the leading cause of mortality in women worldwide. Lymphatic mapping with sentinel node biopsy has the potential to reduce the morbidity associated with breast cancer staging in women after neoadjuvant therapy. A systematic search of world literature between 1996 and 2007 of sentinel node mapping in patients with early-stage breast carcinoma after chemotherapy was undertaken. Potentially eligible studies were identified using database-specific search strategies incorporating appropriate Boolean combinations of the keywords sentinel node biopsy or sentinel node localization or lymphatic mapping; breast cancer or malignancy or neoplasm; and preoperative or neoadjuvant chemotherapy. The electronic searches were augmented with a manual search of reference lists from identified articles. Successful lymph node mapping, defined as successful identification rate (SIR), and false-negative rate (FNR) was summarized using a bivariate random effects mixed model. The extent of heterogeneity was assessed using the inconsistency statistic. The effect of study level covariates, such as use of immunohistochemistry or dual mapping technique, and individual quality criteria, such as study design or multi-institution participation, on SIR and FNR were analyzed using metaregression. A total of 24 trials of 1799 subjects were reported that met eligibility criteria. All studies identified were published between 2000 and 2007. Lymph node involvement was found in 758 patients (37%) and ranged from 25% to 96% across studies. The proportion of patients who had successful lymph node mapping ranged from 63% to 100%, with 79% of studies reporting a rate of less than 95%. The summary successful identification rate was 0.896 (95% confidence interval [CI] 0.860-0.923) with moderate heterogeneity. The summary FNR was 0.084 (95% CI 0.064-0.109) with no significant heterogeneity. Increasing prevalence of lymph node involvement and same-day mapping and lymph node dissection both significantly reduced the FNR. The present systematic review demonstrates robust estimates of successful identification rate and false-negative rates of sentinel lymph node mapping and biopsy after neoadjuvant therapy for early-stage breast cancer patients. With a 90% SIR and 8% FNR, this technique is a reliable tool for planning treatment in this population as an alternative to completion axillary lymph node dissection.

Cauda Equina Repair in the Rat: Part 1. Stimulus-Evoked EMG for Identifying Spinal Nerves Innervating Intrinsic Tail Muscles

Cauda equina injuries may produce severe leg and pelvic floor dysfunction, for which no effective treatments exist. We are developing a rat cauda equina injury model to allow nerve root identification and surgical repair. One possible difficulty in implementing any repair strategy after trauma in humans involves the correct identification of proximal and distal ends of nerve roots separated by the injury. Two series of studies were carried out. In Series 1, we electrically stimulated segmental contributors to the dorsal and ventral caudales nerves in order to characterize the recruitment patterns of muscles controlling rat tail movements. In Series 2, we attempted to identify individual nerve roots forming the cauda equina by both level of origin and function (i.e., dorsal or ventral), based solely upon the recruitment patterns in response to electrical stimulation. For Series 1 studies, electrical stimulation of the segmental contributors showed that all nerve roots-from the sixth lumbar to the first coccygeal-contributed to recruitment of muscles found at the base of the tail. Intrinsic tail muscles lying more distally in the tail showed a more root-specific pattern of innervation. For Series 2, the rate of successful identification of an unknown nerve root as being ventral was very high (>95%), and only somewhat lower (approximately 80%) for dorsal roots. Correctly identifying the level of origin of that root was more difficult, but for ventral roots this rate still exceeded 90%. Using the rat cauda equina model, we have shown that stimulus-evoked EMG can be used to identify ventral nerve roots innervating tail muscles with a high degree of accuracy. These findings support the feasibility of using this conceptual approach for identifying and repairing damaged human cauda equina nerve roots based on stimulus-evoked recruitment of muscles in the leg and pelvic floor.

The kinematic signature of damped Lyman alpha systems: using theD-index to screen for high column density H i absorbers★

Using a sample of 21 damped Lyman alpha systems (DLAs) and 35 sub-DLAs, we evaluate the D-index from high-resolution spectra of the Mg iiλ 2796 profile. This sample represents an increase in the sub-DLA statistics by a factor of 4 over the original D-index sample. We investigate various techniques to define the velocity spread (Δv) of the Mg ii line to determine an optimal D-index for the identification of DLAs. The success rate of DLA identification is 50–55 per cent, depending on the velocity limits used, improving by a few per cent when the column density of Fe ii is included in the D-index calculation. We recommend the set of parameters that are judged to be most robust, have a combination of high DLA identification rate (57 per cent) and low DLA miss rate (6 per cent) and most cleanly separate the DLAs and sub-DLAs (Kolmogorov–Smirnov probability 0.5 per cent). These statistics demonstrate that the D-index is the most efficient technique for selecting low-redshift DLA candidates: 65 per cent more efficient than selecting DLAs based on the equivalent widths of Mg ii and Fe ii alone. We also investigate the effect of resolution on determining the N(H i) of sub-DLAs. We convolve echelle spectra of sub-DLA Lyα profiles with Gaussians typical of the spectral resolution of instruments on the Hubble Space Telescope and compare the best-fitting N(H i) values at both the resolutions. We find that the fitted H i column density is systematically overestimated by ∼0.1 dex in the moderate-resolution spectra compared to the best fits to the original echelle spectra. This offset is due to blending of nearby Lyα clouds that are included in the damping wing fit at low resolution.

Is there any benefit from sentinel lymph node biopsy using the combined radioisotope/dye technique in breast cancer patients with clinically negative axilla?

We evaluated the eventual benefits from sentinel lymph node biopsy (SLNB) in comparison with axillary lymph node dissection (ALND) using a combined radioisotope/dye technique versus dye alone in breast cancer patients. SLNB was performed in 501 breast cancer patients (250 patients with dye alone and 251 with combined technique). Patients were divided in three groups: (A) clinical stage T1,2N0 (SLNB followed by ALND only in cases with positive histology), (B) clinical stage T1,2N0 (SLNB followed by ALND), and (C): advanced clinical stage (SLNB immediately followed by ALND). The incidence of recurrences and surgery morbidity was comparatively evaluated. The overall successful identification rate in patients of groups A and B was 97.7% (95.3% with dye and 99.3% with dye and isotope, P = 0.04) and in patients of group C 96.1% (93.3% with dye and 1000% with the combined technique, P = 0.02). The false-negative rate did not reach statistical significance between groups. Although locoregional recurrence rate was similar in groups A and B (less than 1.88%) the distant metastasis rate was significantly lower in group A (0.9 vs. 6.6%, P = 0.04). Arm edema was significantly more frequent in group B (0 vs. 5.3%, P = 0.02). The combined technique, improves the ID rate of SLNs in patients with breast cancer. The recurrence rate in the axilla was negligible and the metastasis-free rate was better in patients treated with SLNB alone, which further supports the concept that ALND has no clinical relevance and adds nothing more than morbidity to breast cancer patients with clinically node-negative disease.

Listening to the noise: random fluctuations reveal gene network parameters

The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations show cell-to-cell variability that can manifest significant phenotypic differences. Noise-induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We show that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.

Discovery and Validation of Novel Peptide Agonists for G-protein-coupled Receptors

G-protein-coupled receptors (GPCRs) represent an important group of targets for pharmaceutical therapeutics. The completion of the human genome revealed a large number of putative GPCRs. However, the identification of their natural ligands, and especially peptides, suffers from low discovery rates, thus impeding development of therapeutics based on these potential drug targets. We describe the discovery of novel GPCR ligands encrypted in the human proteome. Hundreds of potential peptide ligands were predicted by machine learning algorithms. In vitro screening of selected 33 peptides on a set of 152 GPCRs, including a group of designated orphan receptors, was conducted by intracellular calcium measurements and cAMP assays. The screening revealed eight novel peptides as potential agonists that specifically activated six different receptors in a dose-dependent manner. Most of the peptides showed distinct stimulatory patterns targeted at designated and orphan GPCRs. Further analysis demonstrated a significant in vivo effect for one of the peptides in a mouse inflammation model.

A Functional Proteomic Approach to the Identification and Characterization of Protein Composition in Wheat Leaf

Proteomics and bioinformatics approach were applied for the analyzing of wheat leaf proteins composition and function. Wheat proteins were precipitated by ammonium sulfate and analyzed by two-dimensional gel electrophoresis and mass spectrometry. A total of 200 wheat proteins were selected to identify based on reproducibility and relative quantity, and 123 proteins were identified with an identification success rate of 61.5 ;. The classifications of these proteins by BGSSJ (bioinformatic software) were mainly classified by their molecular, biological and cellular function. Proteins grouped under the molecular function were involved in catalytic, binding and antioxidant activity. The catalytic activity of identified wheat proteins included oxidoreductase, transferase, hydrolase, lyase and isomerase. Only 10.6 % of the wheat protein identifications lacked ascertainable functions. These results provided the information to investigate the composition and function of proteins found in wheat leaf, and enhanced the feasibility of future research on wheat. Keywords: Proteomics, 2D-PAGE, wheat leaf, protein composition, mass spectrometry

Species identification using the cytochrome b gene of commercial turtle shells

Turtle shells and their gelled products are familiar in some countries as foods, tonics and medicines. These shells may come from endangered and protected species, requiring the identification of the species present to enforce national and international legislation. We report on the design of five combinations of primer pairs for the identification of turtle shells and shell fragments used as ornaments, food products and medicines. The types of samples used are those encountered frequently and will typically contain highly degraded DNA. The success rate for species identification using the test described is dependent upon the choice of primer sets used and the length of the expected amplification product. Gelled products were simulated by the process of decoction for up to 12 h, after which all the turtle species could be identified from the liquid samples. This study establishes a method for the identification of commercial turtle shells and illustrates a simulated case using gelled products.

Prediction of Peptidase Category Based on Functional Domain Composition

Peptidases play pivotal regulatory roles in conception, birth, digestion, growth, maturation, aging, and death of all organisms. These regulatory roles include activation, synthesis and turnover of proteins. In the proteomics era, computational methods to identify peptidases and catalog the peptidases into six different major classes-aspartic peptidases, cysteine peptidases, glutamic peptidases, metallo peptidases, serine peptidases and threonine peptidases can give an instant glance at the biological functions of a newly identified protein. In this contribution, by combining the nearest neighbor algorithm and the functional domain composition, we introduce both an automatic peptidase identifier and an automatic peptidase classier. The successful identification and classification rates are 93.7% and 96.5% for our peptidase identifier and peptidase classifier, respectively. Free online peptidase identifier and peptidase classifier are provided on our Web page http://pcal.biosino.org/protease_classification.html.

The Accuracy of Sentinel Lymph Node Biopsy in the Treatment of Multicentric Invasive Breast Cancer Using a Subareolar Injection of Tracer

The aim of this study was to evaluate the feasibility and the accuracy of sentinel lymph node biopsy in multicentric breast cancer (MBC) performed by means of a subareolar (SA) injection of both 99Tc-labeled human albumin colloid and lymphazurin. Between January 2002 and October 2007, 34 patients with MBC with clinically negative axilla underwent sentinel lymph node biopsy (SLNB) followed by total axillary node dissection (AD). Overall successful identification rate of SLN was 100%; there were no false negatives and overall accuracy rate was 100%. The mean number of sentinel lymph nodes (SLNs) identified was 1.8 +/- 0.88 (range = 1-4); the mean number of axillary lymph nodes examined was 21.4 +/- 5.76 (range = 8-36). The authors conclude that SA injection of the tracer is feasible and efficacious in the identification of the SLN. The accuracy of SLNB in MBC is comparable to that obtained in unifocal disease.

Proteome analysis of secondary somatic embryogenesis in cassava ( Manihot esculenta)

Using histological analysis of the induction of secondary somatic embryogenesis (SSE) in cassava ( Manihot esculenta Crantz) as a guide, we performed 2-DE for protein separation and matrix-assisted laser desorption ionization-time of flight-tandem mass spectrometry (MALDI-TOF-TOF-MS) for protein identification in cotyledons of cassava somatic embryos undergoing SSE. Reference map obtained by 2-DE within a pH range of 3–10 and a size range of 6–97 kDa revealed approximately 410 eletrophoretically resolved spots populated primarily by acidic (p I < 7) proteins with molecular masses between 30 and 75 kDa. Tryptic digestion of 163 of the most abundant spots led to the identification of 86 proteins with a protein identification success rate of 53%. In total, 43% of the identified proteins were involved in metabolism and energy and 11.6% in protein destination and storage. Others are, disease/defense (11.6%), transcription and protein synthesis (7%), signal transduction (5.8%), cell growth/division (3.5%), transporters (3.5%), cell structure (2.3%), secondary metabolism (1.2%) and other functional classes (10.5%). Our studies demonstrate that 2-DE-based proteomic approaches combined with histological studies can serve as tools for identifying protein markers for the developmental stages of cassava SE while providing clues on the underlying causes of the low rate of conversion of cassava somatic embryos into mature plants.

Flame imaging as a diagnostic tool for industrial combustion

Different approaches for condition monitoring of combustion systems based on the capture and processing of flame images are presented and discussed. The objective is to devise methods capable of converting geometrical and luminous data into reliable information on the state of practical combustion systems. Suitable processing methods are needed to extract representative information from flame images, as they contain large amounts of data whose physical interpretation is not, in general, straightforward. A thorough experimental programme has been conducted in a model industrial burner, in order to create a database of flame images for subsequent analysis. One of the options is the extraction and analysis of representative image features; even though the parametric studies revealed good sensitivity to changes in combustion regimes, this approach may entail some information loss and requires adaptation to the specific system. The other two methods explored are designed as flame identification techniques based on the whole image. One of them use self-organising feature maps and yields as output the most probable combustion regime, among those previously characterised. The other one is an adaptation of a speech recognition method and informs about the probability of an unknown state to correspond with the different combustion regimes. Their performance was tested in terms of their success rate for identification as well as on their capabilities to estimate NO x emission, as a representative outcome of the combustion process. Results in other combustion situations (oil burner and premixed combustor) are also reported. The good results obtained in all cases are thought to support the potential of the methods described for flame monitoring.

Ivory identification by DNA profiling of cytochrome b gene

Ivory can be visually identified in its native form as coming from an elephant species; however, determining from which of the three extant elephant species a section of ivory originates is more problematic. We report on a method that will identify and distinguish the protected and endangered elephant species, Elephas maximus or Loxodonta sp. To identify the species of elephant from ivory products, we developed three groups of nested PCR amplifications within the cytochrome b gene that generate amplification products using highly degraded DNA isolated from confiscated ivory samples dating from 1995. DNA from a total of 382 out of 453 ivory samples were successfully isolated and amplified leading to species identification. All sequences were searched against GenBank and found to match with E. maximus and Loxodonta sp. with at least 99% similarity. The samples that were tested came from eight Asian elephants, 14 African forest elephants (Loxodonta cyclotis), and 360 African savannah elephants (Loxodonta africana). This study demonstrates a high success rate in species identification of ivory by a nested PCR approach within the cytochrome b gene which provides the necessary information for the protection of endangered species conservation.

Ground Validation of the Third Generation JPL Electronic Nose

The Third Generation ENose is an air quality monitor designed to operate in the environment of the US Lab on the International Space Station. It detects a selected group of analytes at target concentrations in the ppm regime at an environmental temperature range of 18 30 o C, relative humidity from 25 - 75% and pressure from 530 to 760 torr. The abilities of the device to detect ten analytes, to reject confounders as “unknown” and to deconvolute mixtures of two analytes under varying environmental conditions has been tested extensively in the laboratory. Results of ground testing showed an overall success rate for detection, identification and quantification of analytes of 87% under nominal temperature and humidity conditions and 83% over all conditions.

The combination approach of SVM and ECOC for powerful identification and classification of transcription factor.

BackgroundTranscription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand.ResultsThe support vector machine (SVM) algorithm was utilized to construct an automatic detector for TF identification, where protein domains and functional sites were employed as feature vectors. Error-correcting output coding (ECOC) algorithm, which was originated from information and communication engineering fields, was introduced to combine with support vector machine (SVM) methodology for TF classification. The overall success rates of identification and classification achieved 88.22% and 97.83% respectively. Finally, a web site was constructed to let users access our tools (see Availability and requirements section for URL).ConclusionThe SVM method was a valid and stable means for TFs identification with protein domains and functional sites as feature vectors. Error-correcting output coding (ECOC) algorithm is a powerful method for multi-class classification problem. When combined with SVM method, it can remarkably increase the accuracy of TF classification using protein domains and functional sites as feature vectors. In addition, our work implied that ECOC algorithm may succeed in a broad range of applications in biological data mining.

Fourier Transform Infrared Spectroscopy for Rapid Identification of Nonfermenting Gram-Negative Bacteria Isolated from Sputum Samples from Cystic Fibrosis Patients

The accurate and rapid identification of bacteria isolated from the respiratory tract of patients with cystic fibrosis (CF) is critical in epidemiological studies, during intrahospital outbreaks, for patient treatment, and for determination of therapeutic options. While the most common organisms isolated from sputum samples are Pseudomonas aeruginosa, Staphylococcus aureus, and Haemophilus influenzae, in recent decades an increasing fraction of CF patients has been colonized by other nonfermenting (NF) gram-negative rods, such as Burkholderia cepacia complex (BCC) bacteria, Stenotrophomonas maltophilia, Ralstonia pickettii, Acinetobacter spp., and Achromobacter spp. In the present study, we developed a novel strategy for the rapid identification of NF rods based on Fourier transform infrared spectroscopy (FTIR) in combination with artificial neural networks (ANNs). A total of 15 reference strains and 169 clinical isolates of NF gram-negative bacteria recovered from sputum samples from 150 CF patients were used in this study. The clinical isolates were identified according to the guidelines for clinical microbiology practices for respiratory tract specimens from CF patients; and particularly, BCC bacteria were further identified by recA-based PCR followed by restriction fragment length polymorphism analysis with HaeIII, and their identities were confirmed by recA species-specific PCR. In addition, some strains belonging to genera different from BCC were identified by 16S rRNA gene sequencing. A standardized experimental protocol was established, and an FTIR spectral database containing more than 2,000 infrared spectra was created. The ANN identification system consisted of two hierarchical levels. The top-level network allowed the identification of P. aeruginosa, S. maltophilia, Achromobacter xylosoxidans, Acinetobacter spp., R. pickettii, and BCC bacteria with an identification success rate of 98.1%. The second-level network was developed to differentiate the four most clinically relevant species of BCC, B. cepacia, B. multivorans, B. cenocepacia, and B. stabilis (genomovars I to IV, respectively), with a correct identification rate of 93.8%. Our results demonstrate the high degree of reliability and strong potential of ANN-based FTIR spectrum analysis for the rapid identification of NF rods suitable for use in routine clinical microbiology laboratories.

Differentiating European and South Asian individuals using SNPs and pyrosequencing technology

The population-specific SNP set developed by the SNP forID consortium comprises 34 SNPs enabling accurate differentiation between three major population groups: European, African and East Asian. If a South Asian population (from the Indian subcontinent) was also analyzed, the assignment error rate increased due to a lack of sufficient differentiation between Europeans and South Asians. It was thus necessary to find some alternative SNPs to distinguish these populations. An assay has been designed using pyrosequencing technology for eight candidate SNPs selected from the literature. Using this small SNP panel, a set of samples has been typed from both European and South Asian populations. The frequency data obtained was submitted to a web portal ( http://mathgene.usc.es/snipper/) allowing a differentiation algorithm to be produced. A number of samples were then run through this algorithm resulting in a 100% success rate of population identification.

Imaging simulated smeared fingers with a sensor based on scattered-light detection

Fingerprint identification is susceptible to the surface conditions of a finger. We studied the effect of image degradations with our sensor technology based on scattered-light detection. To simulate a smeared finger, we printed small black dots on a transparent sheet and inserted it between a finger and our sensor. With a green light-emitting diode (LED) as its light source, the rate of successful identification decreased constantly as the number of black dots increased. With a near-infrared LED, the rate remained almost constant when the total area covered by the black dots was less than 1% of the input area. The infrared LED generated a clearer image for oily fingers than the green LED as well. We attribute these findings to the presence of an internal fingerprint pattern, which near-infrared light can probe better.