Fixational saccades during grating detection and discrimination

Copyright © 2015 Elsevier Ltd. All rights reserved. This research was funded by French ANR Grant ANR-2010-BLAN-1432-01 (Visafix). The authors would like to thank Dr. Laurent Madelain and Dr. Claudio Simoncini for the helpful discussions during the development of the study.

Fixational saccades during grating detection and discrimination

Copyright © 2015 Elsevier Ltd. All rights reserved. This research was funded by French ANR Grant ANR-2010-BLAN-1432-01 (Visafix). The authors would like to thank Dr. Laurent Madelain and Dr. Claudio Simoncini for the helpful discussions during the development of the study.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Data-Driven Motion Detection and Characterization in PET Brain Scans Using List Mode

Head motion during a Positron Emission Tomography (PET) brain scan can considerably degrade image quality. External motion-tracking devices have proven successful in minimizing this effect, but the associated time, maintenance, and workflow changes inhibit their widespread clinical use. List-mode PET acquisition allows for the retroactive analysis of coincidence events on any time scale throughout a scan, and therefore potentially offers a data-driven motion detection and characterization technique. An algorithm was developed to parse list-mode data, divide the full acquisition into short scan intervals, and calculate the line-of-response (LOR) midpoint average for each interval. These LOR midpoint averages, known as “radioactivity centroids,” were presumed to represent the center of the radioactivity distribution in the scanner, and it was thought that changes in this metric over time would correspond to intra-scan motion.

Several scans were taken of the 3D Hoffman brain phantom on a GE Discovery IQ PET/CT scanner to test the ability of the radioactivity to indicate intra-scan motion. Each scan incrementally surveyed motion in a different degree of freedom (2 translational and 2 rotational). The radioactivity centroids calculated from these scans correlated linearly to phantom positions/orientations. Centroid measurements over 1-second intervals performed on scans with ~1mCi of activity in the center of the field of view had standard deviations of 0.026 cm in the x- and y-dimensions and 0.020 cm in the z-dimension, which demonstrates high precision and repeatability in this metric. Radioactivity centroids are thus shown to successfully represent discrete motions on the submillimeter scale. It is also shown that while the radioactivity centroid can precisely indicate the amount of motion during an acquisition, it fails to distinguish what type of motion occurred.

Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods.

OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.

On covariate factor detection and removal for robust gait recognition

We propose a novel bolt-on module capable of boosting the robustness of various single compact 2D gait representations. Gait recognition is negatively influenced by covariate factors including clothing and time which alter the natural gait appearance and motion. Contrary to traditional gait recognition, our bolt-on module remedies this by a dedicated covariate factor detection and removal procedure which we quantitatively and qualitatively evaluate. The fundamental concept of the bolt-on module is founded on exploiting the pixel-wise composition of covariate factors. Results demonstrate how our bolt-on module is a powerful component leading to significant improvements across gait representations and datasets yielding state-of-the-art results.

Current status of gene therapy for breast cancer: progress and challenges

Breast cancer is characterized by a series of genetic mutations and is therefore ideally placed for gene therapy intervention. The aim of gene therapy is to deliver a nucleic acid-based drug to either correct or destroy the cells harboring the genetic aberration. More recently, cancer gene therapy has evolved to also encompass delivery of RNA interference technologies, as well as cancer DNA vaccines. However, the bottleneck in creating such nucleic acid pharmaceuticals lies in the delivery. Deliverability of DNA is limited as it is prone to circulating nucleases; therefore, numerous strategies have been employed to aid with biological transport. This review will discuss some of the viral and nonviral approaches to breast cancer gene therapy, and present the findings of clinical trials of these therapies in breast cancer patients. Also detailed are some of the most recent developments in nonviral approaches to targeting in breast cancer gene therapy, including transcriptional control, and the development of recombinant, multifunctional bio-inspired systems. Lastly, DNA vaccines for breast cancer are documented, with comment on requirements for successful pharmaceutical product development.

Circulating miRNAs: Potential Novel Biomarkers for Hepatopathology Progression and Diagnosis of Schistosomiasis Japonica in Two Murine Models

BACKGROUND: Schistosomiasis remains a major public health issue, with an estimated 230 million people infected worldwide. Novel tools for early diagnosis and surveillance of schistosomiasis are currently needed. Elevated levels of circulating microRNAs (miRNAs) are commonly associated with the initiation and progression of human disease pathology. Hence, serum miRNAs are emerging as promising biomarkers for the diagnosis of a variety of human diseases. This study investigated circulating host miRNAs commonly associated with liver diseases and schistosome parasite-derived miRNAs during the progression of hepatic schistosomiasis japonica in two murine models.

METHODOLOGY/PRINCIPAL FINDINGS: Two mouse strains (C57BL/6 and BALB/c) were infected with a low dosage of Schistosoma japonicum cercariae. The dynamic patterns of hepatopathology, the serum levels of liver injury-related enzymes and the serum circulating miRNAs (both host and parasite-derived) levels were then assessed in the progression of schistosomiasis japonica. For the first time, an inverse correlation between the severity of hepatocyte necrosis and the level of liver fibrosis was revealed during S. japonicum infection in BALB/c, but not in C57BL/6 mice. The inconsistent levels of the host circulating miRNAs, miR-122, miR-21 and miR-34a in serum were confirmed in the two murine models during infection, which limits their potential value as individual diagnostic biomarkers for schistosomiasis. However, their serum levels in combination may serve as a novel biomarker to mirror the hepatic immune responses induced in the mammalian host during schistosome infection and the degree of hepatopathology. Further, two circulating parasite-specific miRNAs, sja-miR-277 and sja-miR-3479-3p, were shown to have potential as diagnostic markers for schistosomiasis japonica.

CONCLUSIONS/SIGNIFICANCE: We provide the first evidence for the potential of utilizing circulating host miRNAs to indicate different immune responses and the severity of hepatopathology outcomes induced in two murine strains infected with S. japonicum. This study also establishes a basis for the early and cell-free diagnosis of schistosomiasis by targeting circulating schistosome parasite-derived miRNAs.

A Loop-mediated Isothermal Amplification Method for Rapid Direct Detection and Differentiation of Non-pathogenic and Verocytotoxigenic Escherichia coli in Beef and Bovine Faeces

The aim of this study was to develop a multiplex loop-mediated isothermal amplification (LAMP) method capable of detecting Escherichia coli generally and verocytotoxigenic E. coli (VTEC) specifically in beef and bovine faeces. The LAMP assay developed was highly specific (100%) and able to distinguish between E. coli and VTEC based on the amplification of the phoA, and stx1 and/or stx2 genes, respectively. In the absence of an enrichment step, the limit of detection 50% (LOD50) of the LAMP assay was determined to be 2.83, 3.17 and 2.83-3.17 log CFU/g for E. coli with phoA, stx1 and stx2 genes, respectively, when artificially inoculated minced beef and bovine faeces were tested. The LAMP calibration curves generated with pure cultures, and spiked beef and faeces, suggested that the assay had good quantification capability. Validation of the assay, performed using retail beef and bovine faeces samples, demonstrated good correlation between counts obtained by the LAMP assay and by a conventional culture method, but suggested the possibility of false negative LAMP results for 12.5-14.7% of samples tested. The multiplex LAMP assay developed potentially represents a rapid alternative to culture for monitoring E.coli levels in beef or faeces and it would provide additional information on the presence of VTEC. However, some further optimisation is needed to improve detection sensitivity.

The ENCARA system for face detection and normalization

[EN]Facial image processing is becoming widespread in human-computer applications, despite its complexity. High-level processes such as face recognition or gender determination rely on low-level routines that must e ectively detect and normalize the faces that appear in the input image. In this paper, a face detection and normalization system is described. The approach taken is based on a cascade of fast, weak classi ers that together try to determine whether a frontal face is present in the image.

Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

LOPES-DOS-SANTOS, V. , CONDE-OCAZIONEZ, S. ; NICOLELIS, M. A. L. , RIBEIRO, S. T. , TORT, A. B. L. . Neuronal assembly detection and cell membership specification by principal component analysis. Plos One, v. 6, p. e20996, 2011.

Cyber Enigmas? Passive Detection and Pedagogical Agents: Can Students Spot the fake?

This paper presents a study that was undertaken to examine human interaction with a pedagogical agent and the passive and active detection of such agents within a synchronous, online environment. A pedagogical agent is a software application which can provide a human like interaction using a natural language interface. These may be familiar from the smartphone interfaces such as ‘Siri’ or ‘Cortana’, or the virtual online assistants found on some websites, such as ‘Anna’ on the Ikea website. Pedagogical agents are characters on the computer screen with embodied life-like behaviours such as speech, emotions, locomotion, gestures, and movements of the head, the eye, or other parts of the body. The passive detection test is where participants are not primed to the potential presence of a pedagogical agent within the online environment. The active detection test is where participants are primed to the potential presence of a pedagogical agent. The purpose of the study was to examine how people passively detected pedagogical agents that were presenting themselves as humans in an online environment. In order to locate the pedagogical agent in a realistic higher education online environment, problem-based learning online was used. Problem-based learning online provides a focus for discussions and participation, without creating too much artificiality. The findings indicated that the ways in which students positioned the agent tended to influence the interaction between them. One of the key findings was that since the agent was focussed mainly on the pedagogical task this may have hampered interaction with the students, however some of its non-task dialogue did improve students' perceptions of the autonomous agents’ ability to interact with them. It is suggested that future studies explore the differences between the relationships and interactions of learner and pedagogical agent within authentic situations, in order to understand if students' interactions are different between real and virtual mentors in an online setting.