Anomalous morphology in left hemisphere motor and premotor cortex of children who stutter

Stuttering is a neurodevelopmental disorder that affects the smooth flow of speech production. Stuttering onset occurs during a dynamic period of development when children first start learning to formulate sentences. Although most children grow out of stuttering naturally, ∼1% of all children develop persistent stuttering that can lead to significant psychosocial consequences throughout one’s life. To date, few studies have examined neural bases of stuttering in children who stutter, and even fewer have examined the basis for natural recovery versus persistence of stuttering. Here we report the first study to conduct surface-based analysis of the brain morphometric measures in children who stutter. We used FreeSurfer to extract cortical size and shape measures from structural MRI scans collected from the initial year of a longitudinal study involving 70 children (36 stuttering, 34 controls) in the 3–10-year range. The stuttering group was further divided into two groups: persistent and recovered, based on their later longitudinal visits that allowed determination of their eventual clinical outcome. A region of interest analysis that focused on the left hemisphere speech network and a whole-brain exploratory analysis were conducted to examine group differences and group × age interaction effects. We found that the persistent group could be differentiated from the control and recovered groups by reduced cortical thickness in left motor and lateral premotor cortical regions. The recovered group showed an age-related decrease in local gyrification in the left medial premotor cortex (supplementary motor area and and pre-supplementary motor area). These results provide strong evidence of a primary deficit in the left hemisphere speech network, specifically involving lateral premotor cortex and primary motor cortex, in persistent developmental stuttering. Results further point to a possible compensatory mechanism involving left medial premotor cortex in those who recover from childhood stuttering.This study was supported by Award Numbers R01DC011277 (SC) and R01DC007683 (FG) from the National Institute on Deafness and other Communication Disorders (NIDCD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDCD or the National Institutes of Health. (R01DC011277 - National Institute on Deafness and other Communication Disorders (NIDCD); R01DC007683 - National Institute on Deafness and other Communication Disorders (NIDCD))Accepted manuscrip

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of \u3e0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes

The Spitzer Survey of Stellar Structure in Galaxies (S^4G)

The Spitzer Survey of Stellar Structure in Galaxies S^4G is an Exploration Science Legacy Program approved for the Spitzer post-cryogenic mission. It is a volume-, magnitude-, and size-limited (d < 40 Mpc, |b| > 30 degrees, m_(Bcorr) < 15.5, D25>1') survey of 2,331 galaxies using IRAC at 3.6 and 4.5 microns. Each galaxy is observed for 240 s and mapped to > 1.5 x D25. The final mosaicked images have a typical 1 sigma rms noise level of 0.0072 and 0.0093 MJy / sr at 3.6 and 4.5 microns, respectively. Our azimuthally-averaged surface brightness profile typically traces isophotes at mu_3.6 (AB) (1 sigma) ~ 27 mag arcsec^-2, equivalent to a stellar mass surface density of ~ 1 Msun pc^-2. S^4G thus provides an unprecedented data set for the study of the distribution of mass and stellar structures in the local Universe. This paper introduces the survey, the data analysis pipeline and measurements for a first set of galaxies, observed in both the cryogenic and warm mission phase of Spitzer. For every galaxy we tabulate the galaxy diameter, position angle, axial ratio, inclination at mu_3.6 (AB) = 25.5 and 26.5 mag arcsec^-2 (equivalent to ~ mu_B (AB) =27.2 and 28.2 mag arcsec^-2, respectively). These measurements will form the initial S^4G catalog of galaxy properties. We also measure the total magnitude and the azimuthally-averaged radial profiles of ellipticity, position angle, surface brightness and color. Finally, we deconstruct each galaxy using GALFIT into its main constituent stellar components: the bulge/spheroid, disk, bar, and nuclear point source, where necessary. Together these data products will provide a comprehensive and definitive catalog of stellar structures, mass and properties of galaxies in the nearby Universe.Comment: Accepted for Publication in PASP, 14 pages, 13 figure

AR2, a novel automatic muscle artifact reduction software method for ictal EEG interpretation: Validation and comparison of performance with commercially available software.

Objective: To develop a novel software method (AR2) for reducing muscle contamination of ictal scalp electroencephalogram (EEG), and validate this method on the basis of its performance in comparison to a commercially available software method (AR1) to accurately depict seizure-onset location. Methods: A blinded investigation used 23 EEG recordings of seizures from 8 patients. Each recording was uninterpretable with digital filtering because of muscle artifact and processed using AR1 and AR2 and reviewed by 26 EEG specialists. EEG readers assessed seizure-onset time, lateralization, and region, and specified confidence for each determination. The two methods were validated on the basis of the number of readers able to render assignments, confidence, the intra-class correlation (ICC), and agreement with other clinical findings. Results: Among the 23 seizures, two-thirds of the readers were able to delineate seizure-onset time in 10 of 23 using AR1, and 15 of 23 using AR2 (

Detection of intrinsic source structure at ~3 Schwarzschild radii with Millimeter-VLBI observations of SAGITTARIUS A*

We report results from very long baseline interferometric (VLBI) observations of the supermassive black hole in the Galactic center, Sgr A*, at 1.3 mm (230 GHz). The observations were performed in 2013 March using six VLBI stations in Hawaii, California, Arizona, and Chile. Compared to earlier observations, the addition of the APEX telescope in Chile almost doubles the longest baseline length in the array, provides additional {\it uv} coverage in the N-S direction, and leads to a spatial resolution of $\sim$30 $\mu$as ($\sim$3 Schwarzschild radii) for Sgr A*. The source is detected even at the longest baselines with visibility amplitudes of $\sim$4-13% of the total flux density. We argue that such flux densities cannot result from interstellar refractive scattering alone, but indicate the presence of compact intrinsic source structure on scales of $\sim$3 Schwarzschild radii. The measured nonzero closure phases rule out point-symmetric emission. We discuss our results in the context of simple geometric models that capture the basic characteristics and brightness distributions of disk- and jet-dominated models and show that both can reproduce the observed data. Common to these models are the brightness asymmetry, the orientation, and characteristic sizes, which are comparable to the expected size of the black hole shadow. Future 1.3 mm VLBI observations with an expanded array and better sensitivity will allow a more detailed imaging of the horizon-scale structure and bear the potential for a deep insight into the physical processes at the black hole boundary.Comment: 11 pages, 5 figures, accepted to Ap

Discovery and Use of a Natural Mutation that Results in Severe Combined Immuno Deficiency in Pigs

Piglets from the low residual feed intake (RFI) line at ISU were found to be affected with a lethal autosomal recessive mutation that causes Severe Combined Immunodeficiency (SCID). Bone marrow allotransplantation rescued the immune deficiency in four of nine attempted transfers; the other five exhibited signs of severe graft versus host disease and were euthanized. A genome wide association study identified a 5.6 Mb region that contained the causative mutation. Affected haplotypes were traced back to the founders of the RFI population, who were sourced from the purebred Yorkshire population. The SCID pigs will be useful as a biomedical model, as pigs are anatomically and genetically more similar to humans than SCID mice, which are now widely used. Development of a genetic test for the causative mutation will be valuable to the swine industry, allowing breeders to identify carriers

Longitudinal biomarkers in amyotrophic lateral sclerosis

OBJECTIVE: To investigate neurodegenerative and inflammatory biomarkers in people with amyotrophic lateral sclerosis (PALS), evaluate their predictive value for ALS progression rates, and assess their utility as pharmacodynamic biomarkers for monitoring treatment effects. METHODS: De-identified, longitudinal plasma, and cerebrospinal fluid (CSF) samples from PALS (n = 108; 85 with samples from \u3e /=2 visits) and controls without neurological disease (n = 41) were obtained from the Northeast ALS Consortium (NEALS) Biofluid Repository. Seventeen of 108 PALS had familial ALS, of whom 10 had C9orf72 mutations. Additional healthy control CSF samples (n = 35) were obtained from multiple sources. We stratified PALS into fast- and slow-progression subgroups using the ALS Functional Rating Scale-Revised change rate. We compared cytokines/chemokines and neurofilament (NF) levels between PALS and controls, among progression subgroups, and in those with C9orf72 mutations. RESULTS: We found significant elevations of cytokines, including MCP-1, IL-18, and neurofilaments (NFs), indicators of neurodegeneration, in PALS versus controls. Among PALS, these cytokines and NFs were significantly higher in fast-progression and C9orf72 mutation subgroups versus slow progressors. Analyte levels were generally stable over time, a key feature for monitoring treatment effects. We demonstrated that CSF/plasma neurofilament light chain (NFL) levels may predict disease progression, and stratification by NFL levels can enrich for more homogeneous patient groups. INTERPRETATION: Longitudinal stability of cytokines and NFs in PALS support their use for monitoring responses to immunomodulatory and neuroprotective treatments. NFs also have prognostic value for fast-progression patients and may be used to select similar patient subsets in clinical trials