Sequence learning in Associative Neuronal-Astrocytic Network

The neuronal paradigm of studying the brain has left us with limitations in both our understanding of how neurons process information to achieve biological intelligence and how such knowledge may be translated into artificial intelligence and its most brain-derived branch, neuromorphic computing. Overturning our fundamental assumptions of how the brain works, the recent exploration of astrocytes is revealing that these long-neglected brain cells dynamically regulate learning by interacting with neuronal activity at the synaptic level. Following recent experimental evidence, we designed an associative, Hopfield-type, neuronal-astrocytic network and analyzed the dynamics of the interaction between neurons and astrocytes. We show that astrocytes were sufficient to trigger transitions between learned memories in the neuronal component of the network. Further, we mathematically derived the timing of the transitions that was governed by the dynamics of the calcium-dependent slow-currents in the astrocytic processes. Overall, we provide a brain-morphic mechanism for sequence learning that is inspired by, and aligns with, recent experimental findings. To evaluate our model, we emulated astrocytic atrophy and showed that memory recall becomes significantly impaired after a critical point of affected astrocytes was reached. This brain-inspired and brain-validated approach supports our ongoing efforts to incorporate non-neuronal computing elements in neuromorphic information processing.Comment: 8 pages, 5 figure

Excess α-synuclein compromises phagocytosis in iPSC-derived macrophages

To examine the pathogenic role of α-synuclein (αS) in Parkinson's Disease, we have generated induced Pluripotent Stem Cell lines from early onset Parkinson's Disease patients with SNCA A53T and SNCA Triplication mutations, and in this study have differentiated them to PSC-macrophages (pMac), which recapitulate many features of their brain-resident cousins, microglia. We show that SNCA Triplication pMac, but not A53T pMac, have significantly increased intracellular αS versus controls and release significantly more αS to the medium. SNCA Triplication pMac, but not A53T pMac, show significantly reduced phagocytosis capability and this can be phenocopied by adding monomeric αS to the cell culture medium of control pMac. Fibrillar αS is taken up by pMac by actin-rearrangement-dependent pathways, and monomeric αS by actin-independent pathways. Finally, pMac degrade αS and this can be arrested by blocking lysosomal and proteasomal pathways. Together, these results show that macrophages are capable of clearing αS, but that high levels of exogenous or endogenous αS compromise this ability, likely a vicious cycle scenario faced by microglia in Parkinson's disease

The reference frame for encoding and retention of motion depends on stimulus set size

YesThe goal of this study was to investigate the reference frames used in perceptual encoding and storage of visual motion information. In our experiments, observers viewed multiple moving objects and reported the direction of motion of a randomly selected item. Using a vector-decomposition technique, we computed performance during smooth pursuit with respect to a spatiotopic (nonretinotopic) and to a retinotopic component and compared them with performance during fixation, which served as the baseline. For the stimulus encoding stage, which precedes memory, we found that the reference frame depends on the stimulus set size. For a single moving target, the spatiotopic reference frame had the most significant contribution with some additional contribution from the retinotopic reference frame. When the number of items increased (Set Sizes 3 to 7), the spatiotopic reference frame was able to account for the performance. Finally, when the number of items became larger than 7, the distinction between reference frames vanished. We interpret this finding as a switch to a more abstract nonmetric encoding of motion direction. We found that the retinotopic reference frame was not used in memory. Taken together with other studies, our results suggest that, whereas a retinotopic reference frame may be employed for controlling eye movements, perception and memory use primarily nonretinotopic reference frames. Furthermore, the use of nonretinotopic reference frames appears to be capacity limited. In the case of complex stimuli, the visual system may use perceptual grouping in order to simplify the complexity of stimuli or resort to a nonmetric abstract coding of motion information

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Improved Outcome Prediction Using CT Angiography in Addition to Standard Ischemic Stroke Assessment: Results from the STOPStroke Study

Purpose: To improve ischemic stroke outcome prediction using imaging information from a prospective cohort who received admission CT angiography (CTA). Methods: In a prospectively designed study, 649 stroke patients diagnosed with acute ischemic stroke had admission NIH stroke scale scores, noncontrast CT (NCCT), CTA, and 6-month outcome assessed using the modified Rankin scale (mRS) scores. Poor outcome was defined as mRS.2. Strokes were classified as ‘‘major’ ’ by the (1) Alberta Stroke Program Early CT Score (ASPECTS+) if NCCT ASPECTS was#7; (2) Boston Acute Stroke Imaging Scale (BASIS+) if they were ASPECTS+ or CTA showed occlusion of the distal internal carotid, proximal middle cerebral, or basilar arteries; and (3) NIHSS for scores.10. Results: Of 649 patients, 253 (39.0%) had poor outcomes. NIHSS, BASIS, and age, but not ASPECTS, were independent predictors of outcome. BASIS and NIHSS had similar sensitivities, both superior to ASPECTS (p,0.0001). Combining NIHSS with BASIS was highly predictive: 77.6 % (114/147) classified as NIHSS.10/BASIS+ had poor outcomes, versus 21.5 % (77/358) with NIHSS#10/BASIS2 (p,0.0001), regardless of treatment. The odds ratios for poor outcome is 12.6 (95 % CI: 7.9 to 20.0

Divorce, divorce rates, and professional care seeking for mental health problems in Europe: a cross-sectional population-based study

Background: Little is known about differences in professional care seeking based on marital status. The few existing studies show more professional care seeking among the divorced or separated compared to the married or cohabiting. The aim of this study is to determine whether, in a sample of the European general population, the divorced or separated seek more professional mental health care than the married or cohabiting, regardless of self-reported mental health problems. Furthermore, we examine whether two country-level features-the supply of mental health professionals and the country-level divorce rates-contribute to marital status differences in professional care-seeking behavior. Methods: We use data from the Eurobarometer 248 on mental well-being that was collected via telephone interviews. The unweighted sample includes 27,146 respondents (11,728 men and 15,418 women). Poisson hierarchical regression models were estimated to examine whether the divorced or separated have higher professional health care use for emotional or psychological problems, after controlling for mental and somatic health, sociodemographic characteristics, support from family and friends, and degree of urbanization. We also considered country-level divorce rates and indicators of the supply of mental health professionals, and applied design and population weights. Results: We find that professional care seeking is strongly need based. Moreover, the divorced or separated consult health professionals for mental health problems more often than people who are married or who cohabit do. In addition, we find that the gap between the divorced or separated and the married or cohabiting is highest in countries with low divorce rates. Conclusions: The higher rates of professional care seeking for mental health problems among the divorced or separated only partially correlates with their more severe mental health problems. In countries where marital dissolution is more common, the marital status gap in professional care seeking is narrower, partially because professional care seeking is more common among the married or cohabiting

Activation of the p53 pathway by the MDM2 inhibitor nutlin-3a overcomes BCL2 overexpression in a preclinical model of diffuse large B-cell lymphoma associated with t(14;18)(q32;q21)

p53 is frequently wild type (wt) in diffuse large B-cell lymphoma (DLBCL) associated with t(14;18)(q32;q21) that overexpresses BCL2. Nutlin-3a is a small molecule that activates the p53 pathway by disrupting p53–MDM2 interaction. We show that nutlin-3a activates p53 in DLBCL cells associated with t(14;18)(q32;q21), BCL2 overexpression and wt p53, resulting in cell cycle arrest and apoptosis. Nutlin-3a treatment had similar effects on DLBCL cells of activated B-cell phenotype with wt p53. Cell cycle arrest was associated with upregulation of p21. Nutlin-3a-induced apoptosis was accompanied by BAX and PUMA upregulation, BCL-XL downregulation, serine-70 dephosphorylation of BCL2, direct binding of BCL2 by p53, caspase-9 upregulation and caspase-3 cleavage. Cell death was reduced when p53-dependent transactivation activity was inhibited by pifithrin-α (PFT-α), or PFT-μ inhibited direct p53 targeting of mitochondria. Nutlin-3a sensitized activation of the intrinsic apoptotic pathway by BCL2 inhibitors in t(14;18)-positive DLBCL cells with wt p53, and enhanced doxorubicin cytotoxicity against t(14;18)-positive DLBCL cells with wt or mutant p53, the latter in part via p73 upregulation. Nutlin-3a treatment in a xenograft animal lymphoma model inhibited growth of t(14;18)-positive DLBCL tumors, associated with increased apoptosis and decreased proliferation. These data suggest that disruption of the p53–MDM2 interaction by nutlin-3a offers a novel therapeutic approach for DLBCL associated with t(14;18)(q32;q21)

Neural networks for modeling gene-gene interactions in association studies

<p>Abstract</p> <p>Background</p> <p>Our aim is to investigate the ability of neural networks to model different two-locus disease models. We conduct a simulation study to compare neural networks with two standard methods, namely logistic regression models and multifactor dimensionality reduction. One hundred data sets are generated for each of six two-locus disease models, which are considered in a low and in a high risk scenario. Two models represent independence, one is a multiplicative model, and three models are epistatic. For each data set, six neural networks (with up to five hidden neurons) and five logistic regression models (the null model, three main effect models, and the full model) with two different codings for the genotype information are fitted. Additionally, the multifactor dimensionality reduction approach is applied.</p> <p>Results</p> <p>The results show that neural networks are more successful in modeling the structure of the underlying disease model than logistic regression models in most of the investigated situations. In our simulation study, neither logistic regression nor multifactor dimensionality reduction are able to correctly identify biological interaction.</p> <p>Conclusions</p> <p>Neural networks are a promising tool to handle complex data situations. However, further research is necessary concerning the interpretation of their parameters.</p

Metabolic regulation by p53

We are increasingly aware that cellular metabolism plays a vital role in diseases such as cancer, and that p53 is an important regulator of metabolic pathways. By transcriptional activation and other means, p53 is able to contribute to the regulation of glycolysis, oxidative phosphorylation, glutaminolysis, insulin sensitivity, nucleotide biosynthesis, mitochondrial integrity, fatty acid oxidation, antioxidant response, autophagy and mTOR signalling. The ability to positively and negatively regulate many of these pathways, combined with feedback signalling from these pathways to p53, demonstrates the reciprocal and flexible nature of the regulation, facilitating a diverse range of responses to metabolic stress. Intriguingly, metabolic stress triggers primarily an adaptive (rather than pro-apoptotic) p53 response, and p53 is emerging as an important regulator of metabolic homeostasis. A better understanding of how p53 coordinates metabolic adaptation will facilitate the identification of novel therapeutic targets and will also illuminate the wider role of p53 in human biology

Epigenetic Regulation of HIV-1 Latency by Cytosine Methylation

Human immunodeficiency virus type 1 (HIV-1) persists in a latent state within resting CD4+ T cells of infected persons treated with highly active antiretroviral therapy (HAART). This reservoir must be eliminated for the clearance of infection. Using a cDNA library screen, we have identified methyl-CpG binding domain protein 2 (MBD2) as a regulator of HIV-1 latency. Two CpG islands flank the HIV-1 transcription start site and are methylated in latently infected Jurkat cells and primary CD4+ T cells. MBD2 and histone deacetylase 2 (HDAC2) are found at one of these CpG islands during latency. Inhibition of cytosine methylation with 5-aza-2'deoxycytidine (aza-CdR) abrogates recruitment of MBD2 and HDAC2. Furthermore, aza-CdR potently synergizes with the NF-kappaB activators prostratin or TNF-alpha to reactivate latent HIV-1. These observations confirm that cytosine methylation and MBD2 are epigenetic regulators of HIV-1 latency. Clearance of HIV-1 from infected persons may be enhanced by inclusion of DNA methylation inhibitors, such as aza-CdR, and NF-kappaB activators into current antiviral therapies