Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

On robust estimation of the location parameter

https://hal-pasteur.archives-ouvertes.fr/pasteur-01799353/document

TrackMate: An open and extensible platform for single-particle tracking.

Thank you for reading principles of optics electromagnetic theory of propagation interference and diffraction of light. As you may know, people have search hundreds times for their favorite novels like this principles of optics electromagnetic theory of propagation interference and diffraction of light, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their computer.

Principles Of Optics Electromagnetic Theory Of Propagation Interference And Diffraction Of Light

Myocardial tagging using magnetic resonance imaging (MRI) is a well-known noninvasive method for studying regional heart dynamics. While it offers great potential for quantitative analysis of a variety of kinematic and kinetic parameters, its clinical use has so far been limited, mainly due to mediocre performance of existing tag tracking algorithms under poor imaging conditions. In this paper we propose a new approach to tracking of MRI tag intersections. It is based on a Bayesian estimation framework, implemented by means of particle filtering, and combines information about heart dynamics, the imaging process, and tag appearance. Since at any time point it optimally incorporates all available information, it can be expected to be more robust and accurate. This is demonstrated by results of preliminary experiments on image sequences from (small) animal imaging studies.

/pdf/particle-filtering-methods-for-motion-analysis-in-tagged-mri-1pyi6tl5xi.pdf

Particle filtering methods for motion analysis in tagged MRI

Stalled fork protection pathway mediated by BRCA1/2 proteins is critical for replication fork stability that has implications in tumorigenesis. However, it is unclear if additional mechanisms are required to maintain replication fork stability. We describe a novel mechanism by which the chromatin remodeler SMARCAD1 stabilizes active replication forks that is essential for resistance towards replication poisons. We find that loss of SMARCAD1 results in toxic enrichment of 53BP1 at replication forks which mediates untimely dissociation of PCNA via the PCNA-unloader, ATAD5. Faster dissociation of PCNA causes frequent fork stalling, inefficient fork restart and accumulation of single-stranded DNA resulting in genome instability. Although, loss of 53BP1 in SMARCAD1 mutants restore PCNA levels, fork restart efficiency, genome stability and tolerance to replication poisons; this requires BRCA1 mediated fork protection. Interestingly, fork protection challenged BRCA1-deficient naive- or PARPi-resistant tumors require SMARCAD1 mediated active fork stabilization to maintain unperturbed fork progression and cellular proliferation.

/pdf/smarcad1-mediated-active-replication-fork-stability-1isngegmf1.pdf

SMARCAD1 Mediated Active Replication Fork Stability Maintains Genome Integrity

A crucial aspect of automated particle tracking in time-lapse fluorescence microscopy images is the linking or association of detected objects between frames. Recent evaluation studies have shown that the best results are achieved by making use of accurate motion models of the underlying particle dynamics. However, existing approaches often employ rather simple motion models which may be inappropriate for a given application, and even if complex models are used they all require careful user-parameter tuning. To alleviate these problems we propose a novel method based on autoencoding and score matching which can learn the dynamics from the data. Results on both synthetic and real data show the method performs comparable to state-of-the-art linking methods.

Facilitating Data Association In Particle Tracking Using Autoencoding And Score Matching

The study of neuronal cell morphology and function in relation to neurological disease processes is of high importance for developing suitable drugs and therapies. To accelerate discovery, biological experiments for this purpose are increasingly scaled up using high-content screening, resulting in vast amounts of image data. For the analysis of these data fully automatic methods are needed. The first step in this process is the detection of neuron regions in the high-content images. In this paper we investigate the potential of two machine-learning based detection approaches based on different feature sets and classifiers and we compare their performance to an alternative method based on hysteresis thresholding. The experimental results indicate that with the right feature set and training procedure, machine-learning based methods may yield superior detection performance.

Automatic detection of neurons in high-content microscope images using machine learning approaches

Quantitative analysis of neuronal cell morphology from microscopic image data requires accurate reconstruction of the axonal and dendritic trees. The most critical points to be detected in this process are the bifurcations. Here we present a new method for fully automatic detection of bifurcations in microscopic images. The proposed method models the essential characteristics of bifurcations and employs fuzzy rule based reasoning to decide whether the extracted image features indicate the presence of a bifurcation. Algorithm tests on synthetic image data show high noise immunity and experiments with real fluorescence microscopy data exhibit average recall and precision of 90.4% and 90.5% respectively.

/pdf/fuzzy-logic-based-detection-of-neuron-bifurcations-in-27ondf45yy.pdf

Ihor Smal

Papers

Particle filtering methods for motion analysis in tagged MRI

SMARCAD1 Mediated Active Replication Fork Stability Maintains Genome Integrity

Facilitating Data Association In Particle Tracking Using Autoencoding And Score Matching

Automatic detection of neurons in high-content microscope images using machine learning approaches

Fuzzy logic based detection of neuron bifurcations in microscopy images