Primitive roles for inhibitory interneurons in developing frog spinal cord

Understanding the neuronal networks in the mammal spinal cord is hampered by the diversity of neurons and their connections. The simpler networks in developing lower vertebrates may offer insights into basic organization. To investigate the function of spinal inhibitory interneurons in Xenopus tadpoles, paired whole-cell recordings were used. We show directly that one class of interneuron, with distinctive anatomy, produces glycinergic, negative feedback inhibition that can limit firing in motoneurons and interneurons of the central pattern generator during swimming. These same neurons also produce inhibitory gating of sensory pathways during swimming. This discovery raises the possibility that some classes of interneuron, with distinct functions later in development, may differentiate from an earlier class in which these functions are shared. Preliminary evidence suggests that these inhibitory interneurons express the transcription factor engrailed, supporting a probable homology with interneurons in developing zebrafish that also express engrailed and have very similar anatomy and functions

Morphology and Growth of Methyl Stearate as a Function of Crystallization Environment

In situ studies of methyl stearate growing from supersaturated n-dodecane, kerosene, and toluene solutions reveal strong evidence that solvent choice influences the crystal morphology and crystal growth kinetics. Crystals with similar habit are observed in all solvents, with the exception of lower supersaturations in kerosene, where a less symmetric morphology was observed. BFDH analysis based on the monoclinic C2 crystal structure of methyl stearate yielded the morphological indexation to be (110), (1–10), (−110), and (−1–10) for the dominant observed habit and (110) (1–10) (−1–10) (−240) (−3–10) for the less symmetric habit observed in kerosene solvent. Measurements of the growth rate for the (110) and (1–10) faces are similar for all solutions ranging from 0.02 to 1.13 μm/s, for significantly lower values of supersaturation in the case of toluene. The tendency of the growth rate dependence on σ was consistent with the Burton-Cabrera-Frank (BCF) growth mechanism in n-dodecane, the Birth and Spread (B&S) mechanism in kerosene and diffusion controlled in toluene solvent

A Upf3b-mutant mouse model with behavioral and neurogenesis defects.

Nonsense-mediated RNA decay (NMD) is a highly conserved and selective RNA degradation pathway that acts on RNAs terminating their reading frames in specific contexts. NMD is regulated in a tissue-specific and developmentally controlled manner, raising the possibility that it influences developmental events. Indeed, loss or depletion of NMD factors have been shown to disrupt developmental events in organisms spanning the phylogenetic scale. In humans, mutations in the NMD factor gene, UPF3B, cause intellectual disability (ID) and are strongly associated with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia (SCZ). Here, we report the generation and characterization of mice harboring a null Upf3b allele. These Upf3b-null mice exhibit deficits in fear-conditioned learning, but not spatial learning. Upf3b-null mice also have a profound defect in prepulse inhibition (PPI), a measure of sensorimotor gating commonly deficient in individuals with SCZ and other brain disorders. Consistent with both their PPI and learning defects, cortical pyramidal neurons from Upf3b-null mice display deficient dendritic spine maturation in vivo. In addition, neural stem cells from Upf3b-null mice have impaired ability to undergo differentiation and require prolonged culture to give rise to functional neurons with electrical activity. RNA sequencing (RNAseq) analysis of the frontal cortex identified UPF3B-regulated RNAs, including direct NMD target transcripts encoding proteins with known functions in neural differentiation, maturation and disease. We suggest Upf3b-null mice serve as a novel model system to decipher cellular and molecular defects underlying ID and neurodevelopmental disorders

Inpatient GHB withdrawal management in an inner-city hospital in Sydney, Australia: a retrospective medical record review

Rationale: Regular consumption of gamma-hydroxybutyrate (GHB) may result in a dependence syndrome that can lead to withdrawal symptoms. There are limited data on medications to manage GHB withdrawal. Objectives: To examine characteristics associated with delirium and discharge against medical advice (DAMA), in the context of implementing a GHB withdrawal management protocol at an inner-city hospital in 2020. Methods: We retrospectively reviewed records (01 January 2017–31 March 2021), and included admissions that were ≥ 18 years of age, admitted for GHB withdrawal, and with documented recent GHB use. Admissions were assessed for demographics, medications administered, features of delirium, ICU admission, and DAMA. Exploratory analyses were conducted to examine factors associated (p < 0.2) with features of delirium and DAMA. Results: We identified 135 admissions amongst 91 patients. Medications administered included diazepam (133 admissions, 98.5%), antipsychotics (olanzapine [70 admissions, 51.9%]), baclofen (114 admissions, 84%), and phenobarbital (8 admissions, 5.9%). Features of delirium were diagnosed in 21 (16%) admissions. Delirium was associated with higher daily GHB consumption prior to admission, while duration of GHB use, time from presentation to first dose of diazepam, and concomitant methamphetamine use were inversely associated with delirium. DAMA occurred amongst 41 (30%) admissions, and was associated with a longer time from presentation to first dose of baclofen, while being female and receiving a loading dose of diazepam were inversely associated. Conclusions: This study adds to the literature in support of the safety and feasibility of diazepam and baclofen for the management of GHB withdrawal. Prospective, randomised trials are required

Some remarks on PM2.5

Since 1970, the General Physics Department of «Università degli Studi di Torino» has carried out a project research, on inorganic solid particulate matter. The special issue of Annals of Geophysics, published for Professor Giorgio Fiocco’s 70th birthday, gives us the possibility to make some important remarks on this topic, focusing on PM2.5. This has been possible using all the old and new experimental data of the measures made by the authors of this paper since 1970

Gatekeeping Twitter: Message diffusion in political hashtags

This article explores the structure of gatekeeping in Twitter by means of a statistical analysis of the political hashtags #FreeIran, #FreeVenezuela and #Jan25, each of which reached the top position in Twitter Trending Topics. We performed a statistical correlation analysis on nine variables of the dataset to evaluate if message replication in Twitter political hashtags was correlated with network topology. Our results suggest an alternative scenario to the dominant view regarding gatekeeping in Twitter political hashtags. Instead of depending on hubs that act as gatekeepers, we found that the intense activity of individuals with relatively few connections is capable of generating highly replicated messages that contributed to Trending Topics without relying on the activity of user hubs. The results support the thesis of social consensus through the influence of committed minorities, which states that a prevailing majority opinion in a population can be rapidly reversed by a small fraction of randomly distributed committed agents

Resource use data by patient report or hospital records: Do they agree?

Background: Economic evaluations alongside clinical trials are becoming increasingly common. Cost data are often collected through the use of postal questionnaires; however, the accuracy of this method is uncertain. We compared postal questionnaires with hospital records for collecting data on physiotherapy service use. Methods: As part of a randomised trial of orthopaedic medicine compared with orthopaedic surgery we collected physiotherapy use data on a group of patients from retrospective postal questionnaires and from hospital records. Results: 315 patients were referred for physiotherapy. Hospital data on attendances was available for 30% (n = 96), compared with 48% (n = 150) of patients completing questionnaire data (95% Cl for difference = 10% to 24%); 19% (n = 59) had data available from both sources. The two methods produced an intraclass correlation coefficient of 0.54 (95% Cl 0.31 to 0.70). However, the two methods produced significantly different estimates of resource use with patient self report recalling a mean of 1.3 extra visits (95% Cl 0.4 to 2.2) compared with hospital records. Conclusions: Using questionnaires in this study produced data on a greater number of patients compared with examination of hospital records. However, the two data sources did differ in the quantity of physiotherapy used and this should be taken into account in any analysi

Recognition memory, self-other source memory, and theory-of-mind in children with autism spectrum disorder.

This study investigated semantic and episodic memory in autism spectrum disorder (ASD), using a task which assessed recognition and self-other source memory. Children with ASD showed undiminished recognition memory but significantly diminished source memory, relative to age- and verbal ability-matched comparison children. Both children with and without ASD showed an “enactment effect”, demonstrating significantly better recognition and source memory for self-performed actions than other-person-performed actions. Within the comparison group, theory-of-mind (ToM) task performance was significantly correlated with source memory, specifically for other-person-performed actions (after statistically controlling for verbal ability). Within the ASD group, ToM task performance was not significantly correlated with source memory (after controlling for verbal ability). Possible explanations for these relations between source memory and ToM are considered

Sampling constrained probability distributions using Spherical Augmentation

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.Comment: 41 pages, 13 figure

Efficient real-time monitoring of an emerging influenza epidemic: how feasible?

A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased, and originating from multiple sources. This seriously challenges the capacity for real-time monitoring. Here we assess the feasibility of real-time inference based on such data by constructing an analytic tool combining an age-stratified SEIR transmission model with various observation models describing the data generation mechanisms. As batches of data become available, a sequential Monte Carlo (SMC) algorithm is developed to synthesise multiple imperfect data streams, iterate epidemic inferences and assess model adequacy amidst a rapidly evolving epidemic environment, substantially reducing computation time in comparison to standard MCMC, to ensure timely delivery of real-time epidemic assessments. In application to simulated data designed to mimic the 2009 A/H1N1 epidemic, SMC is shown to have additional benefits in terms of assessing predictive performance and coping with parameter non-identifiability.MRC, NIH