Loss of intranetwork and internetwork resting state functional connections with Alzheimer\u27s disease progression

Alzheimer\u27s disease (AD) is the most common cause of dementia. Much is known concerning AD pathophysiology but our understanding of the disease at the systems level remains incomplete. Previous AD research has used resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) to assess the integrity of functional networks within the brain. Most studies have focused on the default-mode network (DMN), a primary locus of AD pathology. However, other brain regions are inevitably affected with disease progression. We studied rs-fcMRI in five functionally defined brain networks within a large cohort of human participants of either gender (n = 510) that ranged in AD severity from unaffected [clinical dementia rating (CDR) 0] to very mild (CDR 0.5) to mild (CDR 1). We observed loss of correlations within not only the DMN but other networks at CDR 0.5. Within the salience network (SAL), increases were seen between CDR 0 and CDR 0.5. However, at CDR 1, all networks, including SAL, exhibited reduced correlations. Specific networks were preferentially affected at certain CDR stages. In addition, cross-network relations were consistently lost with increasing AD severity. Our results demonstrate that AD is associated with widespread loss of both intranetwork and internetwork correlations. These results provide insight into AD pathophysiology and reinforce an integrative view of the brain\u27s functional organization

Cortex-wide, cellular-resolution two-photon microscopy

Functional imaging of the mouse brain in its extreme complexity involves substantial trade-offs. An optical intrinsic spectroscopy system can image the entire cortex but at the expense of spatial and temporal resolution [1]. A two-photon microscope (TPM) can image single neurons with high temporal resolution, but the field of view (FOV) is generally restricted. Advanced techniques like random-access scanning allow for imaging single neurons that are millimeters apart but only by ignoring the neurons and tissue in between [2]. By carefully considering the properties of the optical components as well as the imaging requirements, we present a TPM capable of imaging nearly the entire mouse cortex with 15 Hz frame rates and single neuron resolution. Please click Additional Files below to see the full abstract

Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization

Highlights •We use the well characterized matrix regularization technique described by Ledoit and Wolf to calculate high dimensional partial correlations in fMRI data. •Using this approach we demonstrate that partial correlations reveal RSN structure suggesting that RSNs are defined by widely and uniquely shared variance. •Partial correlation functional connectivity is sensitive to changes in brain state indicating that they contain functional information. Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit–Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity

Dynamic Bradley–Terry modelling of sports tournaments

Summary.  In the course of national sports tournaments, usually lasting several months, it is expected that the abilities of teams taking part in the tournament will change over time. A dynamic extension of the Bradley–Terry model for paired comparison data is introduced to model the outcomes of sporting contests, allowing for time varying abilities. It is assumed that teams’ home and away abilities depend on past results through exponentially weighted moving average processes. The model proposed is applied to sports data with and without tied contests, namely the 2009–2010 regular season of the National Basketball Association tournament and the 2008–2009 Italian Serie A football season

Adjusting Laser Injections for Fully Controlled Faults

Hardware characterizations of integrated circuits have been evolving rapidly with the advent of more precise, sophisticated and cost-efficient tools. In this paper we describe how the fine tuning of a laser source has been used to characterize, set and reset the state of registers in a 90 nm chip. By adjusting the incident laser beam’s location, it is possible to choose to switch any register value from ‘ 0 ’ to ‘ 1 ’ or vice-versa by targeting the PMOS side or the NMOS side. Plus, we show how to clear a register by selecting a laser beam’s power. With the help of imaging techniques, we are able to explain the underlying phenomenon and provide a direct link between the laser mapping and the physical gate structure. Thus, we correlate the localization of laser fault injections with implementations of the PMOS and NMOS areas in the silicon substrate. This illustrates to what extent laser beams can be used to monitor the bits stored within registers, with adverse consequences in terms of security evaluation of integrated circuits

Ignition of solid propellants by forced convection

Experimental data are reported for the ignition of single grains of solid propellant in a stream of gas at high temperature. The investigation encompassed gas temperatures from 578° to 1,070°K., gas velocities corresponding to free-stream Reynolds numbers from 156 to 624, a complete range of oxygen-nitrogen mixtures, and a few oxygen-carbon dioxide mixtures. Pyrocellulose and double-base propellants were tested. The grains were approximately 1/8 in. in diameter and extended through the gas stream, so that ignition was forced to take place on the cylindrical surface rather than on the end of the grain. The exposure before ignition was measured for a large number of grains. The data can be represented by an equation that is consistent with the known effect of flow rate on convective heat transfer and the known effect of temperature on chemical reaction rates, an indication that both processes are important in ignition.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/37288/1/690020427_ftp.pd

Dynamics of chromosome organization in a minimal bacterial cell

Computational models of cells cannot be considered complete unless they include the most fundamental process of life, the replication and inheritance of genetic material. By creating a computational framework to model systems of replicating bacterial chromosomes as polymers at 10 bp resolution with Brownian dynamics, we investigate changes in chromosome organization during replication and extend the applicability of an existing whole-cell model (WCM) for a genetically minimal bacterium, JCVI-syn3A, to the entire cell-cycle. To achieve cell-scale chromosome structures that are realistic, we model the chromosome as a self-avoiding homopolymer with bending and torsional stiffnesses that capture the essential mechanical properties of dsDNA in Syn3A. In addition, the conformations of the circular DNA must avoid overlapping with ribosomes identitied in cryo-electron tomograms. While Syn3A lacks the complex regulatory systems known to orchestrate chromosome segregation in other bacteria, its minimized genome retains essential loop-extruding structural maintenance of chromosomes (SMC) protein complexes (SMC-scpAB) and topoisomerases. Through implementing the effects of these proteins in our simulations of replicating chromosomes, we find that they alone are sufficient for simultaneous chromosome segregation across all generations within nested theta structures. This supports previous studies suggesting loop-extrusion serves as a near-universal mechanism for chromosome organization within bacterial and eukaryotic cells. Furthermore, we analyze ribosome diffusion under the influence of the chromosome and calculate in silico chromosome contact maps that capture inter-daughter interactions. Finally, we present a methodology to map the polymer model of the chromosome to a Martini coarse-grained representation to prepare molecular dynamics models of entire Syn3A cells, which serves as an ultimate means of validation for cell states predicted by the WCM. </p

Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. These histopathological patterns are then used to represent the interaction between complex tissues and predict clinical outcomes directly. We furthermore show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information. Histomorphological clusters obtained by our method are evaluated by training survival models. The experimental results demonstrate statistically significant patient stratification, and our approach outperformed the state-of-the-art deep clustering methods