D4M 3.0: Extended Database and Language Capabilities

The D4M tool was developed to address many of today's data needs. This tool is used by hundreds of researchers to perform complex analytics on unstructured data. Over the past few years, the D4M toolbox has evolved to support connectivity with a variety of new database engines, including SciDB. D4M-Graphulo provides the ability to do graph analytics in the Apache Accumulo database. Finally, an implementation using the Julia programming language is also now available. In this article, we describe some of our latest additions to the D4M toolbox and our upcoming D4M 3.0 release. We show through benchmarking and scaling results that we can achieve fast SciDB ingest using the D4M-SciDB connector, that using Graphulo can enable graph algorithms on scales that can be memory limited, and that the Julia implementation of D4M achieves comparable performance or exceeds that of the existing MATLAB(R) implementation.Comment: IEEE HPEC 201

TEMPLATES: A Robust Outlier Rejection Method for JWST/NIRSpec Integral Field Spectroscopy

We describe a custom outlier rejection algorithm for JWST/NIRSpec integral field spectroscopy. This method uses a layered sigma clipping approach that adapts clipping thresholds based upon the spatial profile of the science target. We find that this algorithm produces a robust outlier rejection while simultaneously preserving the signal of the science target. Originally developed as a response to unsatisfactory initial performance of the jwst pipeline outlier detection step, this method works either as a standalone solution, or as a supplement to the current pipeline software. Comparing leftover (i.e., not flagged) artifacts with the current pipeline's outlier detection step, we find that our method results in one fifth as many residual artifacts as the jwst pipeline. However, we find a combination of both methods removes nearly all artifacts -- an approach that takes advantage of both our algorithm's robust outlier rejection and the pipeline's use of individual dithers. This combined approach is what the TEMPLATES Early Release Science team has converged upon for our NIRSpec observations. Finally, we publicly release the code and Jupyter notebooks for the custom outlier rejection algorithm.Comment: 10 pages, including 5 figures. Submitted to the Publications of the Astronomical Society of the Pacific (PASP). Code associated with paper released at https://github.com/aibhleog/baryon-swee

Optimising experimental design for MEG resting state functional connectivity measurement

The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of co-registration method and data recording duration. We show that the use of a foam head-cast, which is known to improve co-registration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies

A multi-layer network approach to MEG connectivity analysis

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia

DACC Resting State Functional Connectivity as a Predictor of Pain Symptoms Following Motor Vehicle Crash: A Preliminary Investigation

There is significant heterogeneity in pain outcomes following motor vehicle crashes (MVCs), such that a sizeable portion of individuals develop symptoms of chronic pain months after injury while others recover. Despite variable outcomes, the pathogenesis of chronic pain is currently unclear. Previous neuroimaging work implicates the dorsal anterior cingulate cortex (dACC) in adaptive control of pain, while prior resting state functional magnetic resonance imaging studies find increased functional connectivity (FC) between the dACC and regions involved in pain processing in those with chronic pain. Hyper-connectivity of the dACC to regions that mediate pain response may therefore relate to pain severity. The present study completed rsfMRI scans on N=22 survivors of MVCs collected within two weeks of the incident to test whole-brain dACC-FC as a predictor of pain severity six months later. At two weeks, pain symptoms were predicted by positive connectivity between the dACC and the premotor cortex. Controlling for pain symptoms at two weeks, pain symptoms at six months were predicted by negative connectivity between the dACC and the precuneus. Previous research implicates the precuneus in the individual subjective awareness of pain. Given a relatively small sample size, approximately half of which did not experience chronic pain at six months, findings warrant replication. Nevertheless, this study provides preliminary evidence of enhanced dACC connectivity with motor regions and decreased connectivity with pain processing regions as immediate and prospective predictors of pain following MVC. Perspective: This article presents evidence of distinct neural vulnerabilities that predict chronic pain in motor vehicle crash survivors based on whole-brain connectivity with the dorsal anterior cingulate cortex

Physical activity and mental health in women with polycystic ovary syndrome.

Physical activity is prescribed as a component of primary management for Polycystic Ovary Syndrome (PCOS). This study investigates the association between physical activity and mental health as well as the exercise barriers, motivators and support providers for younger women with and without PCOS to assist in physical activity uptake and prescription for these women

Novel gene function revealed by mouse mutagenesis screens for models of age-related disease

Determining the genetic bases of age-related disease remains a major challenge requiring a spectrum of approaches from human and clinical genetics to the utilization of model organism studies. Here we report a large-scale genetic screen in mice employing a phenotype-driven discovery platform to identify mutations resulting in age-related disease, both late-onset and progressive. We have utilized N-ethyl-N-nitrosourea mutagenesis to generate pedigrees of mutagenized mice that were subject to recurrent screens for mutant phenotypes as the mice aged. In total, we identify 105 distinct mutant lines from 157 pedigrees analysed, out of which 27 are late-onset phenotypes across a range of physiological systems. Using whole-genome sequencing we uncover the underlying genes for 44 of these mutant phenotypes, including 12 late-onset phenotypes. These genes reveal a number of novel pathways involved with age-related disease. We illustrate our findings by the recovery and characterization of a novel mouse model of age-related hearing loss

Pain neuroimaging in humans: a primer for beginners and non-imagers

The field of human pain neuroimaging has exploded in the last two decades. During this time, the broader neuroimaging community has continued to investigate and refine methods. Another key to progress is exchange with clinicians and pain scientists working with other model systems and approaches. These collaborative efforts require that non-imagers be able to evaluate and assess the evidence provided in these papers. Likewise, new trainees must design rigorous and reliable pain imaging experiments. Here, we provide a guideline for designing, reading, evaluating, analyzing, and reporting results of a pain neuroimaging experiment, with a focus on functional and structural MRI. We focus in particular on considerations that are unique to neuroimaging studies of pain in humans, including study design and analysis, inferences that can be drawn from these studies, and the strengths and limitations of the approach. This article provides an overview of the concepts and considerations of structural and functional MRI neuroimaging studies. The primer is written for those who are not familiar with brain imaging. We review key concepts related to recruitment and study sample, experimental design, data analysis and data interpretation. [Abstract copyright: Copyright © 2018. Published by Elsevier Inc.

Hypoxia-inducible factor-1α expression in the gastric carcinogenesis sequence and its prognostic role in gastric and gastro-oesophageal adenocarcinomas

Hypoxia-inducible factor-1 (HIF-1)α expression was studied in the gastric carcinogenesis sequence and as a prognostic factor in surgically resected gastric and gastro-oesophageal junction tumours. Protein expression was examined using immunohistochemistry on formalin-fixed biopsies of normal mucosa (n=20), Helicobacter pylori associated gastritis (n=24), intestinal metaplasia (n=24), dysplasia (n=12) and intestinal (n=19) and diffuse (n=21) adenocarcinoma. The relationship between HIF-1α expression and prognosis was assessed in resection specimens from 177 patients with gastric and gastro-oesophageal junction adenocarcinoma. Hypoxia-inducible factor-1α expression was not observed in normal gastric mucosa but increased in density (P<0.01) and intensity (P<0.01) with progression from H. pylori-associated gastritis, intestinal metaplasia, dysplasia to adenocarcinoma. The pattern of staining in the resection specimens was focally positive in 49 (28%) and at the invasive tumour edge in 41 (23%). Invasive edge expression was associated with lymph node metastases (P=0.034), advanced TNM stage (P=0.001) and was an adverse prognostic factor for cancer-specific survival (P=0.019). In univariate analysis and in comparison with tumours not expressing HIF-1α, invasive edge staining was associated with a hazard ratio of 1.6 (95% CI 1.0−2.5) and focally positive staining a hazard ratio of 0.7 (95% CI 0.5−1.2). Hypoxia-inducible factor-1α lost prognostic significance in multivariate analysis. The results suggest HIF-1α is involved in gastric carcinogenesis and disease progression, but is only a weak prognostic factor for survival