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Wed 17 Aug 14:30: naRNA is a canonical neutrophil extracellular trap (NET) component and novel inflammation-amplifying composite DAMP Prof Alex Weber, University of Tübingen, Germany. Hosted by Prof Nick Gay

http://talks.cam.ac.uk/show/rss/29729 - Wed, 10/08/2022 - 08:04
naRNA is a canonical neutrophil extracellular trap (NET) component and novel inflammation-amplifying composite DAMP

Neutrophil extracellular traps (NETs) have emerged as a key feature of cellular innate immunity mediated by polymorphonuclear neutrophils (PMNs), the primary leukocyte population in humans. Forming web-like structures composed of DNA , histones, and antimicrobial proteins, NETs trap and kill microbial invaders and thus enhance host defense. However, they have also been linked to inflammatory states, e.g. in atherosclerosis or psoriasis. Whilst DNA has been in focus as a primary structural component of NETs, we here characterize naRNA (NET-associated RNA ), as a new canonical, abundant, and largely unexplored NET component. naRNA decorated all types of NETs in complex with the antimicrobial peptide LL37 . In fact, naRNA was preassociated with LL37 intracellularly as a ‘composite’ danger-associated molecular pattern (DAMP) prior to neutrophil activation. Externalized, naRNA propagated NET formation in naïve PMN , dependent on TLR8 in humans and Tlr13 in mice, in vitro and in vivo. naRNA-TLR8/Tlr13 signaling contributed significantly to the highly sensitive pro-inflammatory response of both tissue cells, like keratinocytes, and other immune cell types, such as macrophages. Those responses could be blocked by inhibition and genetic ablation of RNA receptors or RNase treatment. Importantly, in vivo naRNA strongly drove skin inflammation whereas genetic ablation of RNA sensing drastically ameliorated skin inflammation in the imiquimod psoriasis model. Our data highlight naRNA as a novel composite DAMP signaling and amplifying neutrophil activation. Moreover, naRNA emerges as the likely driver of inflammation in conditions previously linked to NETs and extracellular RNA , suggesting blockade of TLRmediated RNA sensing as potential new intervention target.

Prof Alex Weber, University of Tübingen, Germany. Hosted by Prof Nick Gay

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Wed 17 Aug 14:30: naRNA is a canonical neutrophil extracellular trap (NET) component and novel inflammation-amplifying composite DAMP

http://talks.cam.ac.uk/show/rss/29729 - Tue, 09/08/2022 - 15:40
naRNA is a canonical neutrophil extracellular trap (NET) component and novel inflammation-amplifying composite DAMP

Neutrophil extracellular traps (NETs) have emerged as a key feature of cellular innate immunity mediated by polymorphonuclear neutrophils (PMNs), the primary leukocyte population in humans. Forming web-like structures composed of DNA , histones, and antimicrobial proteins, NETs trap and kill microbial invaders and thus enhance host defense. However, they have also been linked to inflammatory states, e.g. in atherosclerosis or psoriasis. Whilst DNA has been in focus as a primary structural component of NETs, we here characterize naRNA (NET-associated RNA ), as a new canonical, abundant, and largely unexplored NET component. naRNA decorated all types of NETs in complex with the antimicrobial peptide LL37 . In fact, naRNA was preassociated with LL37 intracellularly as a ‘composite’ danger-associated molecular pattern (DAMP) prior to neutrophil activation. Externalized, naRNA propagated NET formation in naïve PMN , dependent on TLR8 in humans and Tlr13 in mice, in vitro and in vivo. naRNA-TLR8/Tlr13 signaling contributed significantly to the highly sensitive pro-inflammatory response of both tissue cells, like keratinocytes, and other immune cell types, such as macrophages.Those responses could be blocked by inhibition and genetic ablation of RNA receptors or RNase treatment. Importantly, in vivo naRNA strongly drove skin inflammation whereas genetic ablation of RNA sensing drastically ameliorated skin inflammation in the imiquimod psoriasis model. Our data highlight naRNA as a novel composite DAMP signaling and amplifying neutrophil activation. Moreover, naRNA emerges as the likely driver of inflammation in conditions previously linked to NETs and extracellular RNA , suggesting blockade of TLRmediated RNA sensing as potential new intervention target.

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Thu 09 Mar 16:00: TBC This is a hybrid talk. You can attend in person or via zoom. See abstract for details

http://talks.cam.ac.uk/show/rss/29729 - Mon, 08/08/2022 - 15:34
TBC

This talk will be broadcasted via Zoom. Please use this link to gain access.

This is a hybrid talk. You can attend in person or via zoom. See abstract for details

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Wed 30 Nov 16:00: Defining genetic and environmental determinants of malaria transmission This is a hybrid talk. You can attend in person or via zoom. See abstract for details

http://talks.cam.ac.uk/show/rss/29729 - Mon, 08/08/2022 - 15:31
Defining genetic and environmental determinants of malaria transmission

This talk will be broadcasted via Zoom. Please use this link to gain access.

This is a hybrid talk. You can attend in person or via zoom. See abstract for details

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Wed 31 Aug 16:30: Statistics Clinic Summer 2022 III The clinic takes place in MR5 at the Centre for Mathematical Sciences.

http://talks.cam.ac.uk/show/rss/49845 - Mon, 01/08/2022 - 12:12
Statistics Clinic Summer 2022 III

If you would like to participate, please fill in the following form. The deadline for signing up for a session is 12pm on Monday the 29th of August. Subject to availability of members of the Statistics Clinic team, we will confirm your in-person or remote appointment.

This event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

The clinic takes place in MR5 at the Centre for Mathematical Sciences.

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Mon 22 Aug 15:30: BSU Seminar: "Genome-wide genetic models for association, heritability analyses and prediction" This will be a free hybrid seminar. To register to attend virtually, please click here: https://us02web.zoom.us/meeting/register...

http://talks.cam.ac.uk/show/rss/49845 - Wed, 27/07/2022 - 09:50
BSU Seminar: "Genome-wide genetic models for association, heritability analyses and prediction"

Although simultaneous analysis of genome-wide SNPs has been popular for over a decade, the problems posed by more SNPs than study participants (more parameters than data points), and correlations among the SNPs, have not been adequately overcome so that almost all published genome-wide analyses are suboptimal. While there has been much attention paid to the shape of prior distributions for SNP effect sizes, we argue that this attention is misplaced. We focus on what we call the “heritability model”: a low-dimensional model for the expected heritability at each SNP , which is key to both individual-data and summary-statistic analyses. The 1-df uniform heritability model has been implicitly adopted in a wide range of analyses. Replacing it with better heritability models, using predictors based on allele frequency, linkage disequilibrium and functional annotations, leads to substantial improvements in estimates of heritability and selection parameters over traits, and over genome regions, as well as improvements in gene-based association testing and prediction. Key collaborators Doug Speed, Aarhus, Denmark and Melbourne PhD student Anubhav Kaphle.

This will be a free hybrid seminar. To register to attend virtually, please click here: https://us02web.zoom.us/meeting/register/tZcpcuipqTMuGtP_pIJrVQDLQGbP3ndqAh-N

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Sun 20 Nov 17:00: A Distance Function based Cascaded Neural Network for accurate Polyps Segmentation and Classification

http://talks.cam.ac.uk/show/rss/49845 - Wed, 20/07/2022 - 08:20
A Distance Function based Cascaded Neural Network for accurate Polyps Segmentation and Classification

In clinical practice, it is often difficult to locate and measure the size of polyps accurately for the follow-up surgical operation decision. In this paper, based on the position constraint between the primary organ and polyps boundary, we propose a U-Net based cascaded neural network for the joint segmentation of the organ of interest and polyps. The constraint on their position relation is further imposed by adding a narrow-band distance function and complimentary dice function to the loss function. Through a series of comparisons and ablation study, the proposed method with the cascaded network architecture and the additional loss functions was validated on an in-house dataset for gallbladder polyps segmentation and classification. It has been demonstrated that the proposed method achieved a significant improvement over conventional U-Net, U-Net++ etc.. Eventually, the pathological type classification based on the segmented polys shows 30% higher accuracy compared to those conventional ResNet based result

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Tue 26 Jul 14:00: Analysis by synthesis for interpretable image collection analysis

http://talks.cam.ac.uk/show/rss/49845 - Tue, 19/07/2022 - 15:30
Analysis by synthesis for interpretable image collection analysis

Abstract

I will present our recent work on analyzing the content of image collections by learning a simple prototype-based model of images. I will start by introducing the idea and framework of Deep Transformation Invariant image analysis in the case of image clustering [1], where that a simple modification of the standard K-means algorithm can lead to state of the art image clustering, while computing distances in pixel space and being easy to interpret. I will then show how the idea can be extended to perform object recovery [3], decomposing every image in a collection into layers derived from a small set of image prototypes. This can be applied to real world data, such as collection of Instagram images, and provide models and segmentation of repeated objects. Finally, I will explain how a similar idea can be used to perform single view reconstruction from a categorical image collection without any supervision.

[1] Deep Transformation-Invariant Clustering, T. Monnier, T. Groueix, M. Aubry, NeurIPS 2020, link

[2] Unsupervised Layered Image Decomposition into Object Prototypes, T. Monnier, E. Vincent, J. Ponce, M. Aubry, ICCV 2021 , link

[3] Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency, T. Monnier, M. Fisher, A. Efros, M. Aubry, ECCV 2022 , link

Bio

Mathieu Aubry is a tenured researcher in the Imagine team of Ecole des Ponts ParisTech. His work is mainly focussed on Computer Vision and Deep Learning, and their intersection with Computer Graphics, Machine Learning, and Digital Humanities. His PhD on 3D shapes representations obtained in 2015 at ENS was co-advised by Josef Sivic (INRIA) and Daniel Cremers (TUM). In 2015, he spent a year working as a postdoc with Alexei Efros in UC Berkeley.

Location

This seminar will be run as a hybrid event. Attendees can join in person by coming to the MIL Meeting Room in Baker Building (CB2 1PZ). You can also join via Zoom, using the provided link.

Google Calendar for Future Seminars: https://calendar.google.com/calendar/u/0?cid=c2pjcHN0YXM2N3QyMWU3c2FqNjBqNWNiYXNAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ

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Mon 22 Aug 15:30: BSU Seminar: "Genome-wide genetic models for association, heritability analyses and prediction" This will be a free hybrid seminar. To register to attend virtually, please click here: https://us02web.zoom.us/meeting...

http://talks.cam.ac.uk/show/rss/49845 - Tue, 19/07/2022 - 13:51
BSU Seminar: "Genome-wide genetic models for association, heritability analyses and prediction"

Although simultaneous analysis of genome-wide SNPs has been popular for over a decade, the problems posed by more SNPs than study participants (more parameters than data points), and correlations among the SNPs, have not been adequately overcome so that almost all published genome-wide analyses are suboptimal. While there has been much attention paid to the shape of prior distributions for SNP effect sizes, we argue that this attention is misplaced. We focus on what we call the “heritability model”: a low-dimensional model for the expected heritability at each SNP , which is key to both individual-data and summary-statistic analyses. The 1-df uniform heritability model has been implicitly adopted in a wide range of analyses. Replacing it with better heritability models, using predictors based on allele frequency, linkage disequilibrium and functional annotations, leads to substantial improvements in estimates of heritability and selection parameters over traits, and over genome regions, as well as improvements in gene-based association testing and prediction. Key collaborators Doug Speed, Aarhus, Denmark and Melbourne PhD student Anubhav Kaphle.

This will be a free hybrid seminar. To register to attend virtually, please click here: https://us02web.zoom.us/meeting/register/tZAsdeyopj0uGdYCZRIOFnl1wH5y0cU3amO-

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Fri 28 Oct 17:00: Approximate Equivariance SO(3) Needlet Convolution

http://talks.cam.ac.uk/show/rss/49845 - Mon, 18/07/2022 - 18:12
Approximate Equivariance SO(3) Needlet Convolution

This work focuses on the development of a rotation-invariant needlet convolution for rotation group SO(3) to distill multiscale information of spherical signals. The spherical needlet transform is generalized from $\sS^2$ onto the SO(3) group, which decomposes a spherical signal to approximate and detailed spectral coefficients by a set of tight framelet operators. The spherical signal during the decomposition and reconstruction achieves rotation invariance. Based on needlet transforms, we form a Needlet approximate Equivariance Spherical CNN (NES) with multiple SO(3) needlet convolutional layers. The network establishes a powerful tool to extract geometric-invariant features of spherical signals. The model allows sufficient network scalability with multi-resolution representation. A robust signal embedding is learned with wavelet shrinkage activation function, which filters out redundant high-pass representation while maintaining approximate rotation invariance. The NES achieves state-of-the-art performance for quantum chemistry regression and Cosmic Microwave Background (CMB) delensing reconstruction, which shows great potential for solving scientific challenges with high-resolution and multi-scale spherical signal representation.

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Tue 27 Sep 13:00: ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition

http://talks.cam.ac.uk/show/rss/49845 - Mon, 18/07/2022 - 15:26
ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition

Neural message passing is a basic feature extraction unit for graph-structured data that takes account of the impact of neighboring node features in network propagation from one layer to the next. We model such process by an interacting particle system with attractive and repulsive forces and the Allen-Cahn force arising in the modeling of phase transition. The system is a reaction-diffusion process which can separate particles to different clusters. This induces an Allen-Cahn message passing (ACMP) for graph neural networks where the numerical iteration for the solution constitutes the message passing propagation. The mechanism behind ACMP is phase transition of particles which enables the formation of multi- clusters and thus GNNs prediction for node classification. ACMP can propel the network depth to hundreds of layers with theoretically proven strictly positive lower bound of the Dirichlet energy. It thus provides a deep model of GNNs which circumvents the common GNN problem of oversmoothing. Experiments for various real node classification datasets, with possible high homophily difficulty, show the GNNs with ACMP can achieve state of the art performance with no decay of Dirichlet energy.

Joint work with Yuelin Wang (SJTU), Kai Yi (UNSW), Xinliang Liu (KAUST) and Shi Jin (SJTU).

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Wed 13 Jul 16:30: Statistics Clinic Summer 2022 I The clinic takes place in MR2 at the Centre for Mathematical Sciences.

http://talks.cam.ac.uk/show/rss/49845 - Wed, 13/07/2022 - 16:16
Statistics Clinic Summer 2022 I

If you would like to participate, please fill in the following form. The deadline for signing up for a session is 12pm on Monday the 11th of July. Subject to availability of members of the Statistics Clinic team, we will confirm your in-person or remote appointment.

This event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

The clinic takes place in MR2 at the Centre for Mathematical Sciences.

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Thu 14 Jul 14:00: Image Quality Metrics at the Time of Deep Learning The talk can also be attended via zoom. Join Zoom Meeting https://cl-cam-ac-uk.zoom.us/j/92211133320?pwd=dHJqR3lnYzNCN0ZxYnIzRUNZUnRMdz09 ID: 92211133320 passcode: 054090

http://talks.cam.ac.uk/show/rss/49845 - Wed, 13/07/2022 - 16:10
Image Quality Metrics at the Time of Deep Learning

In this talk, we present the current status of image quality metrics in this deep learning age. From this, we will analyze a possible solution to speed-up classic perceptual image quality metrics, and how to move into the no-reference domain. We will show applications why no reference metrics may be useful for image quality assessment.

The talk can also be attended via zoom. Join Zoom Meeting https://cl-cam-ac-uk.zoom.us/j/92211133320?pwd=dHJqR3lnYzNCN0ZxYnIzRUNZUnRMdz09 ID: 92211133320 passcode: 054090

  • Speaker: Francesco Banterle, Visual Computing Lab, ISTI-CNR, Italy
  • Thursday 14 July 2022, 14:00-15:00
  • Venue: SS03.
  • Series: Rainbow Group Seminars; organiser: am2806.

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Wed 13 Jul 16:30: Statistics Clinic Summer 2022 I The clinic takes place in MR11 at the Centre for Mathematical Sciences.

http://talks.cam.ac.uk/show/rss/49845 - Wed, 13/07/2022 - 11:06
Statistics Clinic Summer 2022 I

If you would like to participate, please fill in the following form. The deadline for signing up for a session is 12pm on Monday the 11th of July. Subject to availability of members of the Statistics Clinic team, we will confirm your in-person or remote appointment.

This event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

The clinic takes place in MR11 at the Centre for Mathematical Sciences.

Add to your calendar or Include in your list