Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

COVID-19 and possible links with Parkinson\u27s disease and parkinsonism: from bench to bedside

This Viewpoint discusses insights from basic science and clinical perspectives on coronavirus disease 2019 (COVID-19)/severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection in the brain, with a particular focus on Parkinson\u27s disease. Major points include that neuropathology studies have not answered the central issue of whether the virus enters central nervous system neurons, astrocytes or microglia, and the brain vascular cell types that express virus have not yet been identified. Currently, there is no clear evidence for human neuronal or astrocyte expression of angiotensin-converting enzyme 2 (ACE2), the major receptor for viral entry, but ACE2 expression may be activated by inflammation, and a comparison of healthy and infected brains is important. In contrast to the 1918 influenza pandemic and avian flu, reports of encephalopathy in COVID-19 have been slow to emerge, and there are so far no documented reports of parkinsonism apart from a single case report. We recommend consensus guidelines for the clinical treatment of Parkinson\u27s patients with COVID-19. While a role for the virus in causing or exacerbating Parkinson\u27s disease appears unlikely at this time, aggravation of specific motor and non-motor symptoms has been reported, and it will be important to monitor subjects after recovery, particularly for those with persisting hyposmia

Methamphetamine Inhibits the Glucose Uptake by Human Neurons and Astrocytes: Stabilization by Acetyl-L-Carnitine

Methamphetamine (METH), an addictive psycho-stimulant drug exerts euphoric effects on users and abusers. It is also known to cause cognitive impairment and neurotoxicity. Here, we hypothesized that METH exposure impairs the glucose uptake and metabolism in human neurons and astrocytes. Deprivation of glucose is expected to cause neurotoxicity and neuronal degeneration due to depletion of energy. We found that METH exposure inhibited the glucose uptake by neurons and astrocytes, in which neurons were more sensitive to METH than astrocytes in primary culture. Adaptability of these cells to fatty acid oxidation as an alternative source of energy during glucose limitation appeared to regulate this differential sensitivity. Decrease in neuronal glucose uptake by METH was associated with reduction of glucose transporter protein-3 (GLUT3). Surprisingly, METH exposure showed biphasic effects on astrocytic glucose uptake, in which 20 µM increased the uptake while 200 µM inhibited glucose uptake. Dual effects of METH on glucose uptake were paralleled to changes in the expression of astrocytic glucose transporter protein-1 (GLUT1). The adaptive nature of astrocyte to mitochondrial β-oxidation of fatty acid appeared to contribute the survival of astrocytes during METH-induced glucose deprivation. This differential adaptive nature of neurons and astrocytes also governed the differential sensitivity to the toxicity of METH in these brain cells. The effect of acetyl-L-carnitine for enhanced production of ATP from fatty oxidation in glucose-free culture condition validated the adaptive nature of neurons and astrocytes. These findings suggest that deprivation of glucose-derived energy may contribute to neurotoxicity of METH abusers

Dopamine and Glutamate in Antipsychotic-Responsive Compared With Antipsychotic-Nonresponsive Psychosis: A Multicenter Positron Emission Tomography and Magnetic Resonance Spectroscopy Study (STRATA)

The variability in the response to antipsychotic medication in schizophrenia may reflect between-patient differences in neurobiology. Recent cross-sectional neuroimaging studies suggest that a poorer therapeutic response is associated with relatively normal striatal dopamine synthesis capacity but elevated anterior cingulate cortex (ACC) glutamate levels. We sought to test whether these measures can differentiate patients with psychosis who are antipsychotic responsive from those who are antipsychotic nonresponsive in a multicenter cross-sectional study. 1H-magnetic resonance spectroscopy (1H-MRS) was used to measure glutamate levels (Glucorr) in the ACC and in the right striatum in 92 patients across 4 sites (48 responders [R] and 44 nonresponders [NR]). In 54 patients at 2 sites (25 R and 29 NR), we additionally acquired 3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine (18F-DOPA) positron emission tomography (PET) to index striatal dopamine function (Kicer, min−1). The mean ACC Glucorr was higher in the NR than the R group after adjustment for age and sex (F1,80 = 4.27; P = .04). This was associated with an area under the curve for the group discrimination of 0.59. There were no group differences in striatal dopamine function or striatal Glucorr. The results provide partial further support for a role of ACC glutamate, but not striatal dopamine synthesis, in determining the nature of the response to antipsychotic medication. The low discriminative accuracy might be improved in groups with greater clinical separation or increased in future studies that focus on the antipsychotic response at an earlier stage of the disorder and integrate other candidate predictive biomarkers. Greater harmonization of multicenter PET and 1H-MRS may also improve sensitivity

Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment.

Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different self-regulation mechanisms prefer different feedback timings, and that these factors can be effectively explored and optimized via simulation prior to deployment in the MRI scanner

Automatic learning of neurofeedback signals.

<p><b>(A)</b> Reinforcement learning is used to shape neural activity in the absence of delay. <b>(B)</b> A simple two-voxel neural model is used to demonstrate how our model of automatic learning can learn a pattern of neural activity associated with a stimulus. <b>(C)</b> Without delays, our model is able to learn the desired pattern of neural activity. The top row shows example data from one trial, while the filled plots on the bottom row show the mean and 50% confidence interval (CI) for each signal at each time point, averaged over 1000 simulated trials. <b>(D)</b> If significant physiological delays exist, then an internal model must exist to hold in memory underlying neural activity before it can be reinforced by the delayed feedback signal. <b>(E)</b> If no internal model exists, learning of neural activity filtered by the HRF does not occur.</p

Cognitive learning curves.

<p>Learning curves were constructed by combining all target searches over all participants for each physiological filter. Plots in green show the mean classifier output (+/-s.d.) over the course of each target search. Because a new target was selected as soon as the current target was reached, all time points after success were simulated using randomly generated volumes at the target +/-5°. For means (+/-s.e.) of time to target for continuous (circles) and intermittent (diamonds) feedback, stars indicate significant differences at p<0.05 (*) and p<0.001 (***). In the sample strategy plots, the grating orientation for all HRF target searches from one participant (continuous: participant #3; intermittent: participant #9) are plotted relative to the target, with black circles indicating the time at which the target was reached.</p