The Distribution of Toxoplasma gondii Cysts in the Brain of a Mouse with Latent Toxoplasmosis: Implications for the Behavioral Manipulation Hypothesis

reportedly manipulates rodent behavior to enhance the likelihood of transmission to its definitive cat host. The proximate mechanisms underlying this adaptive manipulation remain largely unclear, though a growing body of evidence suggests that the parasite-entrained dysregulation of dopamine metabolism plays a central role. Paradoxically, the distribution of the parasite in the brain has received only scant attention. at six months of age and examined 18 weeks later. The cysts were distributed throughout the brain and selective tropism of the parasite toward a particular functional system was not observed. Importantly, the cysts were not preferentially associated with the dopaminergic system and absent from the hypothalamic defensive system. The striking interindividual differences in the total parasite load and cyst distribution indicate a probabilistic nature of brain infestation. Still, some brain regions were consistently more infected than others. These included the olfactory bulb, the entorhinal, somatosensory, motor and orbital, frontal association and visual cortices, and, importantly, the hippocampus and the amygdala. By contrast, a consistently low incidence of tissue cysts was recorded in the cerebellum, the pontine nuclei, the caudate putamen and virtually all compact masses of myelinated axons. Numerous perivascular and leptomeningeal infiltrations of inflammatory cells were observed, but they were not associated with intracellular cysts. distribution stems from uneven brain colonization during acute infection and explains numerous behavioral abnormalities observed in the chronically infected rodents. Thus, the parasite can effectively change behavioral phenotype of infected hosts despite the absence of well targeted tropism

Coulomb dissociation of N 20,21

Neutron-rich light nuclei and their reactions play an important role in the creation of chemical elements. Here, data from a Coulomb dissociation experiment on N20,21 are reported. Relativistic N20,21 ions impinged on a lead target and the Coulomb dissociation cross section was determined in a kinematically complete experiment. Using the detailed balance theorem, the N19(n,γ)N20 and N20(n,γ)N21 excitation functions and thermonuclear reaction rates have been determined. The N19(n,γ)N20 rate is up to a factor of 5 higher at

Clinical and demographic details.

<p>Clinical and demographic details.</p

Comparison between model and empirical performance in grip force tracking.

<p>Comparison between model and empirical performance in grip force tracking.</p

Separate PCA of force tracking traces for control subjects and patients.

<p><b>A</b>. Average force trace for each control subject (left) and each patient (medicated and non-medicated patients pooled, right). Note higher variations of baseline force in patients. <b>B–D</b>. PC loading as a function of time for PC1, PC2 and PC3, respectively. <b>B</b>. PC1 loading as a function of time for controls (left) and patients (right). Strong resemblance to force trace present in controls, less so in patients. <b>C</b>. PC2 loading as a function of time for controls (left) and patients (right). Resemblance to the inverse force profile in both groups. <b>D</b>. PC3 loading as a function of time for controls (left) and patients (right). Strongest loading during force transitions (ramp and release) for both groups.</p

Model data: functional consequences of gain changes.

<p><b>A</b>. Single trial runs with identical seed for a simulated average control subject (left) and an average schizophrenia patient (right) at the low force level. <b>C–F</b>. Performance measures as a function of gains. Twenty runs with pseudo-randomized initial seeds were computed for each condition. Performance measures (mean ± SD) were calculated similar to the empirical data. Black: low force condition (F_L), gray: high force condition (F_H). <b>C, E</b>. Influence of SDN-gain on relative error (C) and on release duration (E). Increasing SDN-gains provides higher relative error (and higher CV, not shown), but has no effect on release duration. In C, stippled vertical lines indicate the average SDN_gain for controls (0.016) and patients (0.028). <b>D, F</b>. Impact of inhibition-gain on relative error (D), and on release duration (F). Increasing inhibitory gain has little effect on relative error (and CV, not shown), but decreases the release duration. In F, stippled vertical lines indicate the average I_Gain for controls (0.2) and patients (0.12). Note that, for a given gain, error and CV are always higher for the low force compared to the high force condition (c.f. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111853#pone-0111853-g001" target="_blank">Fig. 1C, D</a>). <b>B</b>. Relation between I_Gain and SDN_Gain after fitting the gains to each subject's performance. There is a significant negative correlation (regression line stippled), across the whole population [controls, medicated patients, and non-medicated patients (NMP)], with patients tending to have lower I_Gains and higher SDN_Gains. Note: this resembles the correlation found empirically between mean error and release duration (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111853#s3" target="_blank">Results</a>).</p

Block-scheme of the computational model of sensorimotor integration for grip force tracking.

<p>Three input signals are integrated to form a motor command: (i) visual information on the ramp-hold-and-release target force trajectory for two different force levels (F_L, F_H for low and high force, respectively), (ii) inhibition modulated as a function of target force (stronger modulation for high force F_H, weaker modulation for low force F_L condition), and (iii) tactile/proprioceptive feedback (a function of force). Each input signal has a gain (gray triangle): the sum of these gains needs to be  = 1. Grip force (the model output) is regulated by a negative feedback-controller as a function of the error between the motor command and the actual grip force. Signal-dependent noise (SDN), with an adjustable gain (black-and-white triangle) is added to the grip force. The goal is to simulate empirically observed behavioral differences between patients and control subjects. Main assumption: a change in gains is sufficient to explain the behavioral difference.</p

PCA of force tracking traces across subjects.

<p><b>A</b>. Average force trace over all conditions and subjects (N = 38). <b>B–D</b>. PC loading as a function of time for PC1, PC2 and PC3, respectively. <b>B</b>. Loading profile similar to force profile for PC1, positive and increasing scores during ramp, more stable and strongest positive scores during hold. <b>C</b>. Inverse loading profile compared to force for PC2. <b>D</b>. Strongest loading during force transitions (ramp and release) for PC3. <b>E</b>. Average factor score (±SD) for PC1, PC2 and PC3 for control subjects vs medicated patients, and non-medicated patients (NMP). Significant difference between controls and both groups of patients only found for PC2 (more negative scores for controls: asterisk). <b>F</b>. Positive correlation between PC2 factor score and release duration for control subjects and patients. Correlation remained significant with exclusion of outlier subject (p = 0.003). No correlation was found between PC2 factor score and relative error or CV (p>0.5). <b>G</b>. Positive rank correlation between PC2 factor scores and PANSS scores in patients.</p

Standards for the care of people with cystic fibrosis (CF); Planning for a longer life

This is the final of four papers updating standards for the care of people with CF. That this paper "Planning a longer life" was considered necessary, highlights how much CF care has progressed over the past decade. Several factors underpin this progress, notably increased numbers of people with CF with access to CFTR modulator therapy. As the landscape for CF changes, so do the hopes and aspirations of people with CF and their families. This paper reflects the need to consider people with CF not as a "problem" to be solved, but as a success, a potential and a voice to be heard. People with CF and the wider CF community have driven this approach, reflecting many of the topics in this paper. This exercise involved wide stakeholder engagement. People with CF are keen to contribute to research priorities and be involved in all stages of research. People with CF want healthcare professionals to respect them as individuals and consider the impact of our actions on the world around us. Navigating life presents challenges to all, but for people with CF these challenges are heightened and complex. In this paper we highlight the concerns and life moments that impact people with CF, and events that the CF team should aim to support, including the challenges around having a family. People with CF and their care teams must embrace the updated standards outlined in these four papers to enjoy the full potential for a healthier life.</p