Impulsive rats are less maternal.

Early life environment and maternal care can have long-lasting effects on behavior and physiology. Previously, we found that compared to mother-reared (MR) female rats, rats reared without mothers, siblings, and nest, through artificially rearing (AR), show reduced levels of maternal behavior when they grow up. These effects can be reversed if AR pups are provided with extra “licking-like” tactile stimulation during the preweaning period [Gonzalez et al. [2001] Developmental Psychobiology, 38(1), 11–42]. We also found that AR rats are more action impulsive and have reduced attentional capacities in comparison to their MR siblings [Lovic, Fletcher, & Fleming, in preparation; Lovic & Fleming [2004] Behavioural Brain Research 148: 209–219]. However, it is unknown whether increased impulsivity contributes to reduced levels of maternal behaviors. The purpose of this study was to assess the relationship between impulsivity and maternal behavior in AR and MR rats. Female rats were reared with (MR) or without mothers (AR) and half of the AR rats received additional stroking stimulation. As adults, AR and MR rats were mated and maternal behavior towards their own pups was assessed. In addition, rats were assessed on impulsive action (differential reinforcement of low-rate schedule; DRL-20s). Consistent with previous findings, AR rats were both less maternal and more action impulsive than MR rats. Partial correlations revealed that impulsivity was inversely related to pup licking-impulsive rats were less maternal. © 2010 Wiley Periodicals, Inc. Dev Psychobiol 53: 13–22, 2011.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78484/1/20481_ftp.pd

The neural dynamics of individual differences in episodic autobiographical memory

The ability to mentally travel to specific events from one’s past, dubbed episodic autobiographical memory (E-AM), contributes to adaptive functioning. Nonetheless, the mechanisms underlying its typical interindividual variation remain poorly understood. To address this issue, we capitalize on existing evidence that successful performance on E-AM tasks draws on the ability to visualize past episodes and reinstate their unique spatiotemporal context. Hence, here, we test whether features of the brain’s functional architecture relevant to perceptual versus conceptual processes shape individual differences in both self-rated E-AM and laboratory-based episodic memory (EM) for random visual scene sequences (visual EM). We propose that superior subjective E-AM and visual EM are associated with greater similarity in static neural organization patterns, potentially indicating greater efficiency in switching, between rest and mental states relevant to encoding perceptual information. Complementarily, we postulate that impoverished subjective E-AM and visual EM are linked to dynamic brain organization patterns implying a predisposition towards semanticizing novel perceptual information. Analyses were conducted on resting state and task-based fMRI data from 329 participants (160 women) in the Human Connectome Project (HCP) who completed visual and verbal EM assessments, and an independent gender diverse sample (N = 59) who self-rated their E-AM. Interindividual differences in subjective E-AM were linked to the same neural mechanisms underlying visual, but not verbal, EM, in general agreement with the hypothesized static and dynamic brain organization patterns. Our results suggest that higher E-AM entails more efficient processing of temporally extended information sequences, whereas lower E-AM entails more efficient semantic or gist-based processing

Combined Diffusion-Relaxometry MRI to Identify Dysfunction in the Human Placenta

Purpose: A combined diffusion-relaxometry MR acquisition and analysis pipeline for in-vivo human placenta, which allows for exploration of coupling between T2* and apparent diffusion coefficient (ADC) measurements in a sub 10 minute scan time. Methods: We present a novel acquisition combining a diffusion prepared spin-echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in-vivo, including both healthy controls and participants with various pregnancy complications. We estimate the joint T2*-ADC spectra using an inverse Laplace transform. Results: T2*-ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas. Conclusions: Combined T2*-diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T2*-ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs

Evaluation of biogenic amines in the faeces of children with and without autism by LC-MS/MS

Previous researchers have postulated that gastrointestinal bacteria may contribute to the development and maintenance of Autism Spectrum Disorders (ASD). There is evidence based on quantitative evaluation of the gastrointestinal bacterial population in ASD that this is unlikely and an alternate mechanism will be examined where the bacteria may contribute to the development of ASD via their metabolic products and the role of biogenic amines (BAs) will be investigated. In humans, BAs influence a number of physiological processes via their actions as neurotransmitters, local hormones and gastric acid secretion. Various amines have been implicated in several medical conditions such as schizophrenia and colon cancer. To date, the relationship between BAs and autism has not been explored. This study has been designed to identify differences (and/or similarities) in the level of Bas in faecal samples of autistic children (without gastrointestinal dysfunction: n = 14; with gastrointestinal dysfunction; n = 21) and their neurotypical siblings (n = 35) by LC-MS/MS. Regardless of the diagnosis, severity of ASD and gastrointestinal dysfunction there were no significant differences found between the groups. The findings suggest that BAs in the gastrointestinal tract do not play a role in the pathophysiology of gastrointestinal dysfunction associated with ASD

Single cell imaging of nuclear architecture changes

This is the final version. Available from Frontiers Media via the DOI in this record.Data and materials availability: Data obtained in this work are available upon request.The dynamic architecture of chromatin, the macromolecular complex comprised primarily of DNA and histones, is vital for eukaryotic cell growth. Chemical and conformational changes to chromatin are important markers of functional and developmental processes in cells. However, chromatin architecture regulation has not yet been fully elucidated. Therefore, novel approaches to assessing chromatin changes at the single-cell level are required. Here we report the use of FTIR imaging and microfluidic cell-stretcher chips to assess changes to chromatin architecture and its effect on the mechanical properties of the nucleus in immune cells. FTIR imaging enables label-free chemical imaging with subcellular resolution. By optimizing the FTIR methodology and couple it with cell segmentation analysis approach, we have identified key spectral changes corresponding to changes in DNA levels and chromatin conformation at the single cell level. By further manipulating live single cells using pressure-driven microfluidics, we found that chromatin decondensation – either during general transcriptional activation or during specific immune cell maturation – can ultimately lead to nuclear auxeticity which is a new biological phenomenon recently identified. Taken together our findings demonstrate the tight and, potentially bilateral, link between extra-cellular mechanotransduction and intra-cellular nuclear architecture.Engineering and Physical Sciences Research Council (EPSRC)Biotechnology and Biological Sciences Research Council (BBSRC)Academy of Medical SciencesRoyal Societ

P24 Restored Physiological Local Carotid Pulse Wave Velocity After Bariatric Surgery in Obese Subjects

AbstractObesity is a risk factor for cardiovascular events and is associated with increased arterial stiffness [1,2]. However, the effect of drastic changes in Body Mass Index (BMI) on arterial mechanics has not been fully investigated. Our study aimed at evaluating changes in local carotid PWV (cPWV) in obese patients before and 6 months after bariatric surgery. N = 20 obese subjects free of cardiovascular events and diabetes (44 ± 9 years, 5 men, BMI = 48.8 ± 7.5 kg/m2) undergoing bariatric surgery were recruited in the Pisa University Hospital (Italy). Flow and diameter waveforms were acquired by ultrasound scanner (Aloka Alpha10, Hitachi Group, Japan) (1 kHz) at the right common carotid artery at baseline, after a 32.4 ± 7.6 days diet period, and 6.5 ± 2.7 months post-intervention. The lnDU-loop method was used for the estimation of cPWV [3]. Basal cPWV was 6.05 ± 1.21 m/s. The 1-month diet period produced a 2 kg/m2 reduction in BMI, while cPWV decreased by approx. 0.6 m/s. 6–7 months after bariatric surgery, BMI dropped to 35.3 ± 6.5 kg/m2 and cPWV furtherly decreased of approx. 0.9 m/s reaching a mean value of 4.57 ± 1.02 m/s (76% of the basal value) (Figure 1). Bariatric surgery and the consequent intensive weight loss produced a significant decrease of arterial stiffness and restored cPWV to physiological values of age-matched healthy subjects [4]. The fast reversal of increased arterial stiffness suggests a functional mechanism possibly related to a reduced haemodynamic load. Moreover, while having a small effect on the BMI, 1-month diet regulation effectively decreased cPWV by 10%, possibly indicating the short-term positive effects of a healthy lifestyle on haemodynamics

Modeling diffusion of intracellular metabolites in the mouse brain up to very high diffusion-weighting: Diffusion in long fibers (almost) accounts for non-monoexponential attenuation

Purpose: To investigate how intracellular metabolites diffusion measured in vivo up to very high q/b in the mouse brain can be explained in terms of simple geometries. / Methods: 10 mice were scanned using our new STE‐LASER sequence, at 11.7 Tesla (T), up to qmax = 1 μm−1 at diffusion time td = 63.2 ms, corresponding to bmax = 60 ms/µm². We model cell fibers as randomly oriented cylinders, with radius a and intracellular diffusivity urn:x-wiley:07403194:media:mrm26548:mrm26548-math-0004, and fit experimental data as a function of q to estimate urn:x-wiley:07403194:media:mrm26548:mrm26548-math-0005 and a. / Results: Randomly oriented cylinders account well for measured attenuation, giving fiber radii and urn:x-wiley:07403194:media:mrm26548:mrm26548-math-0006 in the expected ranges (0.5–1.5 µm and 0.30–0.45 µm2/ms, respectively). The only exception is N‐acetyl‐aspartate (NAA) (extracted a∼0), which we show to be compatible with a small fraction of the NAA pool being confined in highly restricted compartments (with short T2). / Conclusion: The non‐monoexponential signal attenuation of intracellular metabolites in the mouse brain can be described by diffusion in long and thin cylinders, yielding realistic Dintra and fiber diameters. However, this simple model may require small “corrections” for NAA, in the form of a small fraction of the NAA signal originating from a highly restricted compartment

Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack

The multi-armed bandit formalism has been extensively studied under various attack models, in which an adversary can modify the reward revealed to the player. Previous studies focused on scenarios where the attack value either is bounded at each round or has a vanishing probability of occurrence. These models do not capture powerful adversaries that can catastrophically perturb the revealed reward. This paper investigates the attack model where an adversary attacks with a certain probability at each round, and its attack value can be arbitrary and unbounded if it attacks. Furthermore, the attack value does not necessarily follow a statistical distribution. We propose a novel sample median-based and exploration-aided UCB algorithm (called med-E-UCB) and a median-based $\epsilon$-greedy algorithm (called med-$\epsilon$-greedy). Both of these algorithms are provably robust to the aforementioned attack model. More specifically we show that both algorithms achieve $\mathcal{O}(\log T)$ pseudo-regret (i.e., the optimal regret without attacks). We also provide a high probability guarantee of $\mathcal{O}(\log T)$ regret with respect to random rewards and random occurrence of attacks. These bounds are achieved under arbitrary and unbounded reward perturbation as long as the attack probability does not exceed a certain constant threshold. We provide multiple synthetic simulations of the proposed algorithms to verify these claims and showcase the inability of existing techniques to achieve sublinear regret. We also provide experimental results of the algorithm operating in a cognitive radio setting using multiple software-defined radios.Comment: Published at AAAI'2

Microglial morphometric analysis: so many options, so little consistency

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist’s toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community