Discriminative training for Convolved Multiple-Output Gaussian processes

Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class classification. A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued random fields in the context of pattern recognition. As a generative model, the multi-output GP is able to handle vector-valued functions with continuous inputs, as opposed, for example, to hidden Markov models. It also offers the ability to model multivariate random functions with high dimensional inputs. In this report, we use a discriminative training criteria known as Minimum Classification Error to fit the parameters of a multi-output Gaussian process. We compare the performance of generative training and discriminative training of MOGP in emotion recognition, activity recognition, and face recognition. We also compare the proposed methodology against hidden Markov models trained in a generative and in a discriminative way

Parameter estimation for robust HMM analysis of ChIP-chip data

Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. Results: Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. Conclusion: We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.13 page(s

Poly I:C enhances cycloheximide-induced apoptosis of tumor cells through TLR3 pathway

<p>Abstract</p> <p>Background</p> <p>Toll-like receptor 3 (TLR3) is a critical component of the innate immune response to dsRNA viruses, which was considered to be mainly expressed in immune cells and some endothelial cells. In this study, we investigated the expression and proapoptotic activity of TLR3 in human and murine tumor cell lines.</p> <p>Methods</p> <p>RT-PCR and FACS analysis were used to detect expression of TLR3 in various human and murine tumor cell lines. All tumor cell lines were cultured with poly I:C, CHX, or both for 12 h, 24 h, 72 h, and then the cell viability was analyzed with CellTiter 96^®AQueous One Solution, the apoptosis was measured by FACS with Annexin V and PI staining. Production of Type I IFN in poly I:C/CHX mediated apoptosis were detected through western blotting. TLR3 antibodies and IFN-β antibodies were used in Blockade and Neutralization Assay.</p> <p>Results</p> <p>We show that TLR3 are widely expressed on human and murine tumor cell lines, and activation of TLR3 signaling in cancerous cells by poly I:C made Hela cells (human cervical cancer) and MCA38 cells (murine colon cancer) become dose-dependently sensitive to protein synthesis inhibitor cycloheximide (CHX)-induced apoptosis. Blockade of TLR3 recognition with anti-TLR3 antibody greatly attenuated the proapoptotic effects of poly I:C on tumor cells cultured with CHX. IFN-β production was induced after poly I:C/CHX treatment and neutralization of IFN-β slightly reduced poly I:C/CHX -induced apoptosis.</p> <p>Conclusion</p> <p>Our study demonstrated the proapoptotic activity of TLR3 expressed by various tumor cells, which may open a new range of clinical applications for TLR3 agonists as an adjuvant of certain cancer chemotherapy.</p

The Influences of H2Plasma Pretreatment on the Growth of Vertically Aligned Carbon Nanotubes by Microwave Plasma Chemical Vapor Deposition

The effects of H2flow rate during plasma pretreatment on synthesizing the multiwalled carbon nanotubes (MWCNTs) by using the microwave plasma chemical vapor deposition are investigated in this study. A H2and CH4gas mixture with a 9:1 ratio was used as a precursor for the synthesis of MWCNT on Ni-coated TaN/Si(100) substrates. The structure and composition of Ni catalyst nanoparticles were investigated using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The present findings showed that denser Ni catalyst nanoparticles and more vertically aligned MWCNTs could be effectively achieved at higher flow rates. From Raman results, we found that the intensity ratio of G and D bands (ID/IG) decreases with an increasing flow rate. In addition, TEM results suggest that H2plasma pretreatment can effectively reduce the amorphous carbon and carbonaceous particles. As a result, the pretreatment plays a crucial role in modifying the obtained MWCNTs structures

Black-Box Model Explained Through an Assessment of Its Interpretable Features

Algorithms are powerful and necessary tools behind a large part of the information we use every day. However, they may introduce new sources of bias, discrimination and other unfair practices that affect people who are unaware of it. Greater algorithm transparency is indispensable to provide more credible and reliable services. Moreover, requiring developers to design transparent algorithm-driven applications allows them to keep the model accessible and human understandable, increasing the trust of end users. In this paper we present EBAnO, a new engine able to produce prediction-local explanations for a black-box model exploiting interpretable feature perturbations. EBAnO exploits the hypercolumns representation together with the cluster analysis to identify a set of interpretable features of images. Furthermore two indices have been proposed to measure the influence of input features on the final prediction made by a CNN model. EBAnO has been preliminary tested on a set of heterogeneous images. The results highlight the effectiveness of EBAnO in explaining the CNN classification through the evaluation of interpretable features influence