An Analysis of Errors in Graph-Based Keypoint Matching and Proposed Solutions

International audienceAn error occurs in graph-based keypoint matching when key-points in two different images are matched by an algorithm but do not correspond to the same physical point. Most previous methods acquire keypoints in a black-box manner, and focus on developing better algorithms to match the provided points. However to study the complete performance of a matching system one has to study errors through the whole matching pipeline, from keypoint detection, candidate selection to graph optimisation. We show that in the full pipeline there are six different types of errors that cause mismatches. We then present a matching framework designed to reduce these errors. We achieve this by adapting keypoint detectors to better suit the needs of graph-based matching, and achieve better graph constraints by exploiting more information from their keypoints. Our framework is applicable in general images and can handle clutter and motion discontinuities. We also propose a method to identify many mismatches a posteriori based on Left-Right Consistency inspired by stereo matching due to the asymmetric way we detect keypoints and define the graph

Analysis and Computational Dissection of Molecular Signature Multiplicity

Molecular signatures are computational or mathematical models created to diagnose disease and other phenotypes and to predict clinical outcomes and response to treatment. It is widely recognized that molecular signatures constitute one of the most important translational and basic science developments enabled by recent high-throughput molecular assays. A perplexing phenomenon that characterizes high-throughput data analysis is the ubiquitous multiplicity of molecular signatures. Multiplicity is a special form of data analysis instability in which different analysis methods used on the same data, or different samples from the same population lead to different but apparently maximally predictive signatures. This phenomenon has far-reaching implications for biological discovery and development of next generation patient diagnostics and personalized treatments. Currently the causes and interpretation of signature multiplicity are unknown, and several, often contradictory, conjectures have been made to explain it. We present a formal characterization of signature multiplicity and a new efficient algorithm that offers theoretical guarantees for extracting the set of maximally predictive and non-redundant signatures independent of distribution. The new algorithm identifies exactly the set of optimal signatures in controlled experiments and yields signatures with significantly better predictivity and reproducibility than previous algorithms in human microarray gene expression datasets. Our results shed light on the causes of signature multiplicity, provide computational tools for studying it empirically and introduce a framework for in silico bioequivalence of this important new class of diagnostic and personalized medicine modalities

A Proposed Taxonomy of Anaerobic Fungi (Class Neocallimastigomycetes) Suitable for Large-Scale Sequence-Based Community Structure Analysis

Anaerobic fungi are key players in the breakdown of fibrous plant material in the rumen, but not much is known about the composition and stability of fungal communities in ruminants. We analyzed anaerobic fungi in 53 rumen samples from farmed sheep (4 different flocks), cattle, and deer feeding on a variety of diets. Denaturing gradient gel electrophoresis fingerprinting of the internal transcribed spacer 1 (ITS1) region of the rrn operon revealed a high diversity of anaerobic fungal phylotypes across all samples. Clone libraries of the ITS1 region were constructed from DNA from 11 rumen samples that had distinctly different fungal communities. A total of 417 new sequences were generated to expand the number and diversity of ITS1 sequences available. Major phylogenetic groups of anaerobic fungi in New Zealand ruminants belonged to the genera Piromyces, Neocallimastix, Caecomyces and Orpinomyces. In addition, sequences forming four novel clades were obtained, which may represent so far undetected genera or species of anaerobic fungi. We propose a revised phylogeny and pragmatic taxonomy for anaerobic fungi, which was tested and proved suitable for analysis of datasets stemming from high-throughput next-generation sequencing methods. Comparing our revised taxonomy to the taxonomic assignment of sequences deposited in the GenBank database, we believe that >29% of ITS1 sequences derived from anaerobic fungal isolates or clones are misnamed at the genus level

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Role of the receptor Mas in macrophage-mediated inflammation in vivo

Recently, an alternative renin-angiotensin system pathway has been described, which involves binding of angiotensin-(1-7) to its receptor Mas. The Mas axis may counterbalance angiotensin-II-mediated proinflammatory effects, likely by affecting macrophage function. Here we investigate the role of Mas in murine models of autoimmune neuroinflammation and atherosclerosis, which both involve macrophage-driven pathomechanisms. Mas signaling affected macrophage polarization, migration, and macrophage-mediated T-cell activation. Mas deficiency exacerbated the course of experimental autoimmune encephalomyelitis and increased macrophage infiltration as well as proinflammatory gene expression in the spleen and spinal cord. Furthermore, Mas deficiency promoted atherosclerosis by affecting macrophage infiltration and migration and led to increased oxidative stress as well as impaired endothelial function in ApoE-deficient mice. In summary, we identified the Mas axis as an important factor in macrophage function during inflammation of the central nervous and vascular system in vivo. Modulating the Mas axis may constitute an interesting therapeutic target in multiple sclerosis and/or atherosclerosis

Dietary fatty acids directly impact central nervous system autoimmunity via the small intestine

Growing empirical evidence suggests that nutrition and bacterial metabolites might impact the systemic immune response in the context of disease and autoimmunity. We report that long-chain fatty acids (LCFAs) enhanced differentiation and proliferation of T helper 1 (Th1) and/or Th17 cells and impaired their intestinal sequestration via p38-MAPK pathway. Alternatively, dietary short-chain FAs (SCFAs) expanded gut T regulatory (Treg) cells by suppression of the JNK1 and p38 pathway. We used experimental autoimmune encephalomyelitis (EAE) as a model of T cell-mediated autoimmunity to show that LCFAs consistently decreased SCFAs in the gut and exacerbated disease by expanding pathogenic Th1 and/or Th17 cell populations in the small intestine. Treatment with SCFAs ameliorated EAE and reduced axonal damage via long-lasting imprinting on lamina-propria-derived Treg cells. These data demonstrate a direct dietary impact on intestinal-specific, and subsequently central nervous system-specific, Th cell responses in autoimmunity, and thus might have therapeutic implications for autoimmune diseases such as multiple sclerosis