High Resolution Detection and Analysis of CpG Dinucleotides Methylation Using MBD-Seq Technology

Methyl-CpG binding domain protein sequencing (MBD-seq) is widely used to survey DNA methylation patterns. However, the optimal experimental parameters for MBD-seq remain unclear and the data analysis remains challenging. In this study, we generated high depth MBD-seq data in MCF-7 cell and developed a bi-asymmetric-Laplace model (BALM) to perform data analysis. We found that optimal efficiency of MBD-seq experiments was achieved by sequencing ∼100 million unique mapped tags from a combination of 500 mM and 1000 mM salt concentration elution in MCF-7 cells. Clonal bisulfite sequencing results showed that the methylation status of each CpG dinucleotides in the tested regions was accurately detected with high resolution using the proposed model. These results demonstrated the combination of MBD-seq and BALM could serve as a useful tool to investigate DNA methylome due to its low cost, high specificity, efficiency and resolution

Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data

In many fields, and especially in the medical and social sciences and in recommender systems, data are gathered through clinical studies or targeted surveys. Participants are generally reluctant to respond to all questions in a survey or they may lack information to respond adequately to some questions. The data collected from these studies tend to lead to linear regression models where the regression vectors are only known partially: some of their entries are either missing completely or replaced randomly by noisy values. In this work, assuming missing positions are replaced by noisy values, we examine how a connected network of agents, with each one of them subjected to a stream of data with incomplete regression information, can cooperate with each other through local interactions to estimate the underlying model parameters in the presence of missing data. We explain how to adjust the distributed diffusion strategy through (de)regularization in order to eliminate the bias introduced by the incomplete model. We also propose a technique to recursively estimate the (de)regularization parameter and examine the performance of the resulting strategy. We illustrate the results by considering two applications: one dealing with a mental health survey and the other dealing with a household consumption survey

Bimodality of intratumor Ki67 expression is an independent prognostic factor of overall survival in patients with invasive breast carcinoma

Proliferative activity, assessed by Ki67 immunohistochemistry (IHC), is an established prognostic and predictive biomarker of breast cancer (BC). However, it remains under-utilized due to lack of standardized robust measurement methodologies and significant intratumor heterogeneity of expression. A recently proposed methodology for IHC biomarker assessment in whole slide images (WSI), based on systematic subsampling of tissue information extracted by digital image analysis (DIA) into hexagonal tiling arrays, enables computation of a comprehensive set of Ki67 indicators, including intratumor variability. In this study, the tiling methodology was applied to assess Ki67 expression in WSI of 152 surgically removed Ki67-stained (on full-face sections) BC specimens and to test which, if any, Ki67 indicators can predict overall survival (OS). Visual Ki67 IHC estimates and conventional clinico-pathologic parameters were also included in the study. Analysis revealed linearly independent intrinsic factors of the Ki67 IHC variance: proliferation (level of expression), disordered texture (entropy), tumor size and Nottingham Prognostic Index, bimodality, and correlation. All visual and DIA-generated indicators of the level of Ki67 expression provided significant cutoff values as single predictors of OS. However, only bimodality indicators (Ashman’s D, in particular) were independent predictors of OS in the context of hormone receptor and HER2 status. From this, we conclude that spatial heterogeneity of proliferative tumor activity, measured by DIA of Ki67 IHC expression and analyzed by the hexagonal tiling approach, can serve as an independent prognostic indicator of OS in BC patients that outperforms the prognostic power of the level of proliferative activity

Fusion for Audio-Visual Laughter Detection

Laughter is a highly variable signal, and can express a spectrum of emotions. This makes the automatic detection of laughter a challenging but interesting task. We perform automatic laughter detection using audio-visual data from the AMI Meeting Corpus. Audio-visual laughter detection is performed by combining (fusing) the results of a separate audio and video classifier on the decision level. The video-classifier uses features based on the principal components of 20 tracked facial points, for audio we use the commonly used PLP and RASTA-PLP features. Our results indicate that RASTA-PLP features outperform PLP features for laughter detection in audio. We compared hidden Markov models (HMMs), Gaussian mixture models (GMMs) and support vector machines (SVM) based classifiers, and found that RASTA-PLP combined with a GMM resulted in the best performance for the audio modality. The video features classified using a SVM resulted in the best single-modality performance. Fusion on the decision-level resulted in laughter detection with a significantly better performance than single-modality classification

Single 5-nm quantum dot detection via microtoroid optical resonator photothermal microscopy

Label-free detection techniques for single particles and molecules play an important role in basic science, disease diagnostics, and nanomaterial investigations. While traditional fluorescence-based methods offer powerful tools for single molecule detection and imaging, they are limited by a narrow range of molecular probes and issues such as photoblinking and photobleaching. Photothermal microscopy has emerged as a label-free imaging technique capable of detecting individual nanoabsorbers with high sensitivity. Whispering gallery mode microresonators can confine light in a small volume for enhanced light-matter interaction and thus are a promising ultra-sensitive photothermal microscopy platform. Previously microtoroid optical resonators were combined with photothermal microscopy to detect 250 nm long gold nanorods. Here, we combine whispering gallery mode microtoroid optical resonators with photothermal microscopy to spatially detect 5 nm diameter quantum dots (QDs) with a signal-to-noise ratio (SNR) exceeding $10^4$. To achieve this, we integrated our microtoroid based photothermal microscopy setup with a low amplitude modulated pump laser and utilized the proportional-integral-derivative (PID) controller output as the photothermal signal source to reduce noise and enhance signal stability. The measured heat dissipation of these 5 nm QDs is below the detectable level from single dye molecules, showcasing the high sensitivity and discrimination capabilities of this platform. We anticipate that our work will have application in a wide variety of fields, including the biological sciences, nanotechnology, materials science, chemistry, and medicine

Monitoring and Managing Interaction Patterns in Human-Robot Interaction

Nowadays, one of the most challenging problems in Human-Robot Interaction (HRI) is to make robots able to understand humans to successfully accomplish tasks in human environments. HRI has a very different role in all the robotics fields. While autonomous robots do not require a complex HRI system, it is of vital importance for service robots. The goal of this thesis is to study if behavioural patterns that users unconsciously apply when interacting with a robot can be useful to recognise the users' intentions in a particular situation. To carry out this study a prototype has been developed to test in an automatic and objective way, if those interaction patterns performed by several users in the area of service robots are useful to recognise their intentions and disambiguate unclear situations.By using verbal and non-verbal communication that the user unconsciously applies when interacting with a robot, we want to determine automatically what the user is trying to present