Auditory perceptual performance of children in the identification of contrasts between stressed vowels

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Collective screening tools for early identification of dyslexia

Current response to intervention models (RTIs) favor a three-tier system. In general, Tier 1 consists of evidence-based, effective reading instruction in the classroom and universal screening of all students at the beginning of the grade level to identify children for early intervention. Non-responders to Tier 1 receive small-group tutoring in Tier 2. Nonresponders to Tier 2 are given still more intensive, individual intervention in Tier 3. Limited time, personnel and financial resources derail RTI's implementation in Brazilian schools because this approach involves procedures that require extra time and extra personnel in all three tiers, including screening tools which normally consist of tasks administered individually. We explored the accuracy of collectively and easily administered screening tools for the early identification of second graders at risk for dyslexia in a two-stage screening model. A first-stage universal screening based on collectively administered curriculum-based measurements was used in 45 7 years old early Portuguese readers from 4 second-grade classrooms at the beginning of the school year and identified an at-risk group of 13 academic low-achievers. Collectively administered tasks based on phonological judgments by matching figures and figures to spoken words [alternative tools for educators (ATE)] and a comprehensive cognitive-linguistic battery of collective and individual assessments were both administered to all children and constituted the second-stage screening. Low-achievement on ATE tasks and on collectively administered writing tasks (scores at the 25th percentile) showed good sensitivity (true positives) and specificity (true negatives) to poor literacy status defined as scores <= 1 SD below the mean on literacy abilities at the end of fifth grade. These results provide implications for the use of a collectively administered screening tool for the early identification of children at risk for dyslexia in a classroom setting.

Effect of cell cycle synchronization on the accuracy of murine and bovine embryo sex determination

Different cell cycle synchronization methods were used to increase the mitotic index and accuracy of sex determination in murine and bovine embryos. For sexing purposes, colchicine treatment for 2, 4, 6 and 8 h and the FdU-thymidine-colchicine combination were tested in murine embryos. The best results were obtained with colchicine treatment for 8 h (96.88% accuracy) and with FdU-thymidine-colchicine (97.22% accuracy). Mitotic indexes differed significantly between the 2 treatments (21.71% for colchicine and 32.95% for FdU-thymidine-colchicine). For sex identification of murine and bovine demi-embryos, both treatments were demonstrated to be equally effective (nearly 90%). The mitotic index for the FdU-treated murine demi-embryos (19.04%) was higher than the one obtained for the 8-h colchicine treatment (15.62%).

The accuracy of pathological data for the prediction of insignificant prostate cancer

Introduction: The widespread screening programs prompted a decrease in prostate cancer stage at diagnosis, and active surveillance is an option for patients who may harbor clinically insignificant prostate cancer (IPC). Pathologists include the possibility of an IPC in their reports based on the Gleason score and tumor volume. This study determined the accuracy of pathological data in the identification of IPC in radical prostatectomy (RP) specimens. Materials and Methods: Of 592 radical prostatectomy specimens examined in our laboratory from 2001 to 2010, 20 patients harbored IPC and exhibited biopsy findings suggestive of IPC. These biopsy features served as the criteria to define patients with potentially insignificant tumor in this population. The results of the prostate biopsies and surgical specimens of the 592 patients were compared. Results: The twenty patients who had IPC in both biopsy and RP were considered real positive cases. All patients were divided into groups based on their diagnoses following RP: true positives (n = 20), false positives (n = 149), true negatives (n = 421), false negatives (n = 2). The accuracy of the pathological data alone for the prediction of IPC was 91.4%, the sensitivity was 91% and the specificity was 74%. Conclusion: The identification of IPC using pathological data exclusively is accurate, and pathologists should suggest this in their reports to aid surgeons, urologists and radiotherapists to decide the best treatment for their patients.

Comparison of screening methods for conductive hearing loss identification in children: low-cost proposal

Objective To evaluate the effectiveness of two screening methods (imitanciometry screening and questionnaire) to identify children at risk for conductive hearing loss, comparing this data with complete audiologic evaluation. Methods Of 507 children aged between three and six, 111 completed all procedures. The observational methods used were: imitanciometry screening, a questionnaire to identify risk factors for hearing loss and complete audiologic evaluation. Results obtained in the first two instruments were compared with results from complete audiologic evaluation (gold standard). From these comparisons, sensitivity and specificity, accuracy, positive and negative predictive values, and odds ratio were determined for the two screening methods and for the combination of both methods. Results The two methods applied in series (questionnaire and after imitanciometry screening) showed a greater odds ratio and better correlation between sensitivity and proportion of false-positives (ROC curve). Conclusion Combining the two tests in series improved screening accuracy. This combination was the best tool for identifying children at risk for conductive hearing loss.

Use of Large-volume Cone-Beam Computed Tomography in Identification and Localization of Horizontal Root Fracture in the Presence and Absence of Intracanal Metallic Post

Introduction: The objective of the study was to evaluate the ability of large-volume cone-beam computed tomography (CBCT) to detect horizontal root fracture and to test the influence of a metallic post. Methods: Through the examination of 40 teeth by large-volume CBCT (20-cm height and 15-cm diameter cylinder) at 0.2-mm voxel resolution, 2 observers analyzed the samples for the presence and localization of horizontal root fracture. Results: The values of accuracy in the groups that had no metallic post ranged from 33%-68%, whereas for the samples with the metallic post, values showed a wide variation (38%-83%). Intraobserver agreement showed no statistically significant difference between the groups with/without metallic post; both ranged from very weak to weak (kappa, 0.09-0.369). Conclusions: The low accuracy and low intraobserver and interobserver agreement reflect the difficulty in performing an adequate diagnosis of horizontal root fractures through a large-volume CBCT by using a small voxel reconstruction. (J Endod 2012;38:856-859)

Identification of adequate neurally adjusted ventilatory assist (NAVA) during systematic increases in the NAVA level

Neurally adjusted ventilatory assist (NAVA) delivers airway pressure (P(aw)) in proportion to the electrical activity of the diaphragm (EAdi) using an adjustable proportionality constant (NAVA level, cm·H(2)O/μV). During systematic increases in the NAVA level, feedback-controlled down-regulation of the EAdi results in a characteristic two-phased response in P(aw) and tidal volume (Vt). The transition from the 1st to the 2nd response phase allows identification of adequate unloading of the respiratory muscles with NAVA (NAVA(AL)). We aimed to develop and validate a mathematical algorithm to identify NAVA(AL). P(aw), Vt, and EAdi were recorded while systematically increasing the NAVA level in 19 adult patients. In a multistep approach, inspiratory P(aw) peaks were first identified by dividing the EAdi into inspiratory portions using Gaussian mixture modeling. Two polynomials were then fitted onto the curves of both P(aw) peaks and Vt. The beginning of the P(aw) and Vt plateaus, and thus NAVA(AL), was identified at the minimum of squared polynomial derivative and polynomial fitting errors. A graphical user interface was developed in the Matlab computing environment. Median NAVA(AL) visually estimated by 18 independent physicians was 2.7 (range 0.4 to 5.8) cm·H(2)O/μV and identified by our model was 2.6 (range 0.6 to 5.0) cm·H(2)O/μV. NAVA(AL) identified by our model was below the range of visually estimated NAVA(AL) in two instances and was above in one instance. We conclude that our model identifies NAVA(AL) in most instances with acceptable accuracy for application in clinical routine and research.

Tandem mass spectrometry identification and LC-MS quantification of intact cytokinin nucleotides in K-562 human leukemia cells

We describe here a new reversed-phase high-performance liquid chromatography with mass spectrometry detection method for quantifying intact cytokinin nucleotides in human K-562 leukemia cells. Tandem mass spectrometry was used to identify the intracellular metabolites (cytokinin monophosphorylated, diphosphorylated, and triphosphorylated nucleotides) in riboside-treated cells. For the protein precipitation and sample preparation, a trichloroacetic acid extraction method is used. Samples are then back-extracted with diethyl ether, lyophilized, reconstituted, and injected into the LC system. Analytes were quantified in negative selected ion monitoring mode using a single quadrupole mass spectrometer. The method was validated in terms of retention time stabilities, limits of detection, linearity, recovery, and analytical accuracy. The developed method was linear in the range of 1-1,000 pmol for all studied compounds. The limits of detection for the analytes vary from 0.2 to 0.6 pmol.

Time course based artifact identification for independent components of resting-state FMRI

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

Multi-node TOA-DOA cooperative LOS-NLOS localization : enabling high accuracy and reliability

This dissertation investigates high performance cooperative localization in wireless environments based on multi-node time-of-arrival (TOA) and direction-of-arrival (DOA) estimations in line-of-sight (LOS) and non-LOS (NLOS) scenarios. Here, two categories of nodes are assumed: base nodes (BNs) and target nodes (TNs). BNs are equipped with antenna arrays and capable of estimating TOA (range) and DOA (angle). TNs are equipped with Omni-directional antennas and communicate with BNs to allow BNs to localize TNs; thus, the proposed localization is maintained by BNs and TNs cooperation. First, a LOS localization method is proposed, which is based on semi-distributed multi-node TOA-DOA fusion. The proposed technique is applicable to mobile ad-hoc networks (MANETs). We assume LOS is available between BNs and TNs. One BN is selected as the reference BN, and other nodes are localized in the coordinates of the reference BN. Each BN can localize TNs located in its coverage area independently. In addition, a TN might be localized by multiple BNs. High performance localization is attainable via multi-node TOA-DOA fusion. The complexity of the semi-distributed multi-node TOA-DOA fusion is low because the total computational load is distributed across all BNs. To evaluate the localization accuracy of the proposed method, we compare the proposed method with global positioning system (GPS) aided TOA (DOA) fusion, which are applicable to MANETs. The comparison criterion is the localization circular error probability (CEP). The results confirm that the proposed method is suitable for moderate scale MANETs, while GPS-aided TOA fusion is suitable for large scale MANETs. Usually, TOA and DOA of TNs are periodically estimated by BNs. Thus, Kalman filter (KF) is integrated with multi-node TOA-DOA fusion to further improve its performance. The integration of KF and multi-node TOA-DOA fusion is compared with extended-KF (EKF) when it is applied to multiple TOA-DOA estimations made by multiple BNs. The comparison depicts that it is stable (no divergence takes place) and its accuracy is slightly lower than that of the EKF, if the EKF converges. However, the EKF may diverge while the integration of KF and multi-node TOA-DOA fusion does not; thus, the reliability of the proposed method is higher. In addition, the computational complexity of the integration of KF and multi-node TOA-DOA fusion is much lower than that of EKF. In wireless environments, LOS might be obstructed. This degrades the localization reliability. Antenna arrays installed at each BN is incorporated to allow each BN to identify NLOS scenarios independently. Here, a single BN measures the phase difference across two antenna elements using a synchronized bi-receiver system, and maps it into wireless channel’s K-factor. The larger K is, the more likely the channel would be a LOS one. Next, the K-factor is incorporated to identify NLOS scenarios. The performance of this system is characterized in terms of probability of LOS and NLOS identification. The latency of the method is small. Finally, a multi-node NLOS identification and localization method is proposed to improve localization reliability. In this case, multiple BNs engage in the process of NLOS identification, shared reflectors determination and localization, and NLOS TN localization. In NLOS scenarios, when there are three or more shared reflectors, those reflectors are localized via DOA fusion, and then a TN is localized via TOA fusion based on the localization of shared reflectors.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

High-Resolution Ice Cores from US ITASE (West Antarctica): Development and Validation of Chronologies and Determination of Precision and Accuracy

Shallow ice cores were obtained from widely distributed sites across the West Antarctic ice sheet, as part of the United States portion of the International Trans-Antarctic Scientific Expedition (US ITASE) program. The US ITASE cores have been dated by annual-layer counting, primarily through the identification of summer peaks in non-sea-salt sulfate (nssSO(4)(2-)) concentration. Absolute dating accuracy of better than 2 years and relative dating accuracy better than 1 year is demonstrated by the identification of multiple volcanic marker horizons in each of the cores, Tambora, Indonesia (1815), being the most prominent. Independent validation is provided by the tracing of isochronal layers from site to site using high-frequency ice-penetrating radar observations, and by the timing of mid-winter warming events in stable-isotope ratios, which demonstrate significantly better than 1 year accuracy in the last 20 years. Dating precision to 1 month is demonstrated by the occurrence of summer nitrate peaks and stable-isotope ratios in phase with nssSO(4)(2-), and winter-time sea-salt peaks out of phase, with phase variation of < 1 month. Dating precision and accuracy are uniform with depth, for at least the last 100 years.

Electron reconstruction and identification efficiency measurements with the ATLAS detector using the 2011 LHC proton-proton collision data

Many of the interesting physics processes to be measured at the LHC have a signature involving one or more isolated electrons. The electron reconstruction and identification efficiencies of the ATLAS detector at the LHC have been evaluated using proton–proton collision data collected in 2011 at √s = 7 TeV and corresponding to an integrated luminosity of 4.7 fb−1. Tag-and-probe methods using events with leptonic decays of W and Z bosons and J/ψ mesons are employed to benchmark these performance parameters. The combination of all measurements results in identification efficiencies determined with an accuracy at the few per mil level for electron transverse energy greater than 30 GeV.

Automatic identification of discourse markers in dialogues: An in-depth study of like and well

The lexical items like and well can serve as discourse markers (DMs), but can also play numerous other roles, such as verb or adverb. Identifying the occurrences that function as DMs is an important step for language understanding by computers. In this study, automatic classifiers using lexical, prosodic/positional and sociolinguistic features are trained over transcribed dialogues, manually annotated with DM information. The resulting classifiers improve state-of-the-art performance of DM identification, at about 90% recall and 79% precision for like (84.5% accuracy, κ = 0.69), and 99% recall and 98% precision for well (97.5% accuracy, κ = 0.88). Automatic feature analysis shows that lexical collocations are the most reliable indicators, followed by prosodic/positional features, while sociolinguistic features are marginally useful for the identification of DM like and not useful for well. The differentiated processing of each type of DM improves classification accuracy, suggesting that these types should be treated individually.

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.