Scanner Invariant Representations for Diffusion MRI Harmonization

Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods: Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data. Results: To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data

Improving the minimum description length inference of phrase-based translation models

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_25We study the application of minimum description length (MDL) inference to estimate pattern recognition models for machine translation. MDL is a theoretically-sound approach whose empirical results are however below those of the state-of-the-art pipeline of training heuristics. We identify potential limitations of current MDL procedures and provide a practical approach to overcome them. Empirical results support the soundness of the proposed approach.Work supported by the EU 7th Framework Programme (FP7/2007–2013) under the CasMaCat project (grant agreement no 287576), by Spanish MICINN under grant TIN2012-31723, and by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/014).Gonzalez Rubio, J.; Casacuberta Nolla, F. (2015). Improving the minimum description length inference of phrase-based translation models. En Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. Springer International Publishing. 219-227. https://doi.org/10.1007/978-3-319-19390-8 25S21922

Insights into the feature selection problem using local optima networks

The binary feature selection problem is investigated in this paper. Feature selection fitness landscape analysis is done, which allows for a better understanding of the behaviour of feature selection algorithms. Local optima networks are employed as a tool to visualise and characterise the fitness landscapes of the feature selection problem in the context of classification. An analysis of the fitness landscape global structure is provided, based on seven real-world datasets with up to 17 features. Formation of neutral global optima plateaus are shown to indicate the existence of irrelevant features in the datasets. Removal of irrelevant features resulted in a reduction of neutrality and the ratio of local optima to the size of the search space, resulting in improved performance of genetic algorithm search in finding the global optimum

Hybrid Qubit gates in circuit QED: A scheme for quantum bit encoding and information processing

Solid state superconducting devices coupled to coplanar transmission lines offer an exquisite architecture for quantum optical phenomena probing as well as for quantum computation implementation, being the object of intense theoretical and experimental investigation lately. In appropriate conditions the transmission line radiation modes can get strongly coupled to a superconducting device with only two levels -for that reason called artificial atom or qubit. Employing this system we propose a hybrid two-quantum bit gate encoding involving quantum electromagnetic field qubit states prepared in a coplanar transmission line capacitively coupled to a single charge qubit. Since dissipative effects are more drastic in the solid state qubit than in the field one, it can be employed for storage of information, whose efficiency against the action of an ohmic bath show that this encoding can be readily implemented with present day technology. We extend the investigation to generate entanglement between several solid state qubits and the field qubit through the action of external classical magnetic pulses.Comment: 9 pages, 10 figure

Synthesis and DFT investigation of new bismuth-containing MAX phases

The M(n + 1)AX(n) phases (M = early transition metal; A = group A element and X = C and N) are materials exhibiting many important metallic and ceramic properties. In the present study powder processing experiments and density functional theory calculations are employed in parallel to examine formation of Zr(2)(Al(1−x)Bi(x))C (0 ≤ x ≤ 1). Here we show that Zr(2)(Al(1−x)Bi(x))C, and particularly with x ≈ 0.58, can be formed from powders even though the end members Zr(2)BiC and Zr(2)AlC seemingly cannot. This represents a significant extension of the MAX phase family, as this is the first report of a bismuth-based MAX phase

Mechanisms of Maximum Information Preservation in the Drosophila Antennal Lobe

We examined the presence of maximum information preservation, which may be a fundamental principle of information transmission in all sensory modalities, in the Drosophila antennal lobe using an experimentally grounded network model and physiological data. Recent studies have shown a nonlinear firing rate transformation between olfactory receptor neurons (ORNs) and second-order projection neurons (PNs). As a result, PNs can use their dynamic range more uniformly than ORNs in response to a diverse set of odors. Although this firing rate transformation is thought to assist the decoder in discriminating between odors, there are no comprehensive, quantitatively supported studies examining this notion. Therefore, we quantitatively investigated the efficiency of this firing rate transformation from the viewpoint of information preservation by computing the mutual information between odor stimuli and PN responses in our network model. In the Drosophila olfactory system, all ORNs and PNs are divided into unique functional processing units called glomeruli. The nonlinear transformation between ORNs and PNs is formed by intraglomerular transformation and interglomerular interaction through local neurons (LNs). By exploring possible nonlinear transformations produced by these two factors in our network model, we found that mutual information is maximized when a weak ORN input is preferentially amplified within a glomerulus and the net LN input to each glomerulus is inhibitory. It is noteworthy that this is the very combination observed experimentally. Furthermore, the shape of the resultant nonlinear transformation is similar to that observed experimentally. These results imply that information related to odor stimuli is almost maximally preserved in the Drosophila olfactory circuit. We also discuss how intraglomerular transformation and interglomerular inhibition combine to maximize mutual information

BRCA1 and BRCA2 as molecular targets for phytochemicals indole-3-carbinol and genistein in breast and prostate cancer cells

Indole-3-carbinol (I3C) and genistein are naturally occurring chemicals derived from cruciferous vegetables and soy, respectively, with potential cancer prevention activity for hormone-responsive tumours (e.g., breast and prostate cancers). Previously, we showed that I3C induces BRCA1 expression and that both I3C and BRCA1 inhibit oestrogen (E2)-stimulated oestrogen receptor (ER-α) activity in human breast cancer cells. We now report that both I3C and genistein induce the expression of both breast cancer susceptibility genes (BRCA1 and BRCA2) in breast (MCF-7 and T47D) and prostate (DU-145 and LNCaP) cancer cell types, in a time- and dose-dependent fashion. Induction of the BRCA genes occurred at low doses of I3C (20 μM) and genistein (0.5–1.0 μM), suggesting potential relevance to cancer prevention. A combination of I3C and genistein gave greater than expected induction of BRCA expression. Studies using small interfering RNAs (siRNAs) and BRCA expression vectors suggest that the phytochemical induction of BRCA2 is due, in part, to BRCA1. Functional studies suggest that I3C-mediated cytoxicity is, in part, dependent upon BRCA1 and BRCA2. Inhibition of E2-stimulated ER-α activity by I3C and genistein was dependent upon BRCA1; and inhibition of ligand-inducible androgen receptor (AR) activity by I3C and genistein was partially reversed by BRCA1-siRNA. Finally, we provide evidence suggesting that the phytochemical induction of BRCA1 expression is due, in part, to endoplasmic reticulum stress response signalling. These findings suggest that the BRCA genes are molecular targets for some of the activities of I3C and genistein

Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue Identification

Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution

Selecting promising classes from generated data for an efficient multi-class nearest neighbor classification

The nearest neighbor rule is one of the most considered algorithms for supervised learning because of its simplicity and fair performance in most cases. However, this technique has a number of disadvantages, being the low computational efficiency the most prominent one. This paper presents a strategy to overcome this obstacle in multi-class classification tasks. This strategy proposes the use of Prototype Reduction algorithms that are capable of generating a new training set from the original one to try to gather the same information with fewer samples. Over this reduced set, it is estimated which classes are the closest ones to the input sample. These classes are referred to as promising classes. Eventually, classification is performed using the original training set using the nearest neighbor rule but restricted to the promising classes. Our experiments with several datasets and significance tests show that a similar classification accuracy can be obtained compared to using the original training set, with a significantly higher efficiency.This work has been supported by the Vicerrectorado de Investigación, Desarrollo e Innovación de la Universidad de Alicante through the FPU programme (UAFPU2014–5883), the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012–0939) and the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R, supported by UE FEDER funds)

Replication Timing: A Fingerprint for Cell Identity and Pluripotency

Many types of epigenetic profiling have been used to classify stem cells, stages of cellular differentiation, and cancer subtypes. Existing methods focus on local chromatin features such as DNA methylation and histone modifications that require extensive analysis for genome-wide coverage. Replication timing has emerged as a highly stable cell type-specific epigenetic feature that is regulated at the megabase-level and is easily and comprehensively analyzed genome-wide. Here, we describe a cell classification method using 67 individual replication profiles from 34 mouse and human cell lines and stem cell-derived tissues, including new data for mesendoderm, definitive endoderm, mesoderm and smooth muscle. Using a Monte-Carlo approach for selecting features of replication profiles conserved in each cell type, we identify “replication timing fingerprints” unique to each cell type and apply a k nearest neighbor approach to predict known and unknown cell types. Our method correctly classifies 67/67 independent replication-timing profiles, including those derived from closely related intermediate stages. We also apply this method to derive fingerprints for pluripotency in human and mouse cells. Interestingly, the mouse pluripotency fingerprint overlaps almost completely with previously identified genomic segments that switch from early to late replication as pluripotency is lost. Thereafter, replication timing and transcription within these regions become difficult to reprogram back to pluripotency, suggesting these regions highlight an epigenetic barrier to reprogramming. In addition, the major histone cluster Hist1 consistently becomes later replicating in committed cell types, and several histone H1 genes in this cluster are downregulated during differentiation, suggesting a possible instrument for the chromatin compaction observed during differentiation. Finally, we demonstrate that unknown samples can be classified independently using site-specific PCR against fingerprint regions. In sum, replication fingerprints provide a comprehensive means for cell characterization and are a promising tool for identifying regions with cell type-specific organization