Explore the vast range of titles available.

The Long COVID/Post Acute Sequelae of COVID-19 (PASC) group includes patients with initial mild-to-moderate symptoms during the acute phase of the illness, in whom recovery is prolonged, or new symptoms are developed over months. Here, we propose a description of the pathophysiology of the Long COVID presentation based on inflammatory cytokine cascades and the p38 MAP kinase signaling pathways that regulate cytokine production. In this model, the SARS-CoV-2 viral infection is hypothesized to trigger a dysregulated peripheral immune system activation with subsequent cytokine release. Chronic low-grade inflammation leads to dysregulated brain microglia with an exaggerated release of central cytokines, producing neuroinflammation. Immunothrombosis linked to chronic inflammation with microclot formation leads to decreased tissue perfusion and ischemia. Intermittent fatigue, Post Exertional Malaise (PEM), CNS symptoms with “brain fog,” arthralgias, paresthesias, dysautonomia, and GI and ophthalmic problems can consequently arise as result of the elevated peripheral and central cytokines. There are abundant similarities between symptoms in Long COVID and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). DNA polymorphisms and viral-induced epigenetic changes to cytokine gene expression may lead to chronic inflammation in Long COVID patients, predisposing some to develop autoimmunity, which may be the gateway to ME/CFS.

Hippocampal neurons encode physical variables 1 – 7 such as space 1 or auditory frequency 6 in cognitive maps 8 . In addition, functional magnetic resonance imaging studies in humans have shown that the hippocampus can also encode more abstract, learned variables 9 – 11 . However, their integration into existing neural representations of physical variables 12 , 13 is unknown. Here, using two-photon calcium imaging, we show that individual neurons in the dorsal hippocampus jointly encode accumulated evidence with spatial position in mice performing a decision-making task in virtual reality 14 – 16 . Nonlinear dimensionality reduction 13 showed that population activity was well-described by approximately four to six latent variables, which suggests that neural activity is constrained to a low-dimensional manifold. Within this low-dimensional space, both physical and abstract variables were jointly mapped in an orderly manner, creating a geometric representation that we show is similar across mice. The existence of conjoined cognitive maps suggests that the hippocampus performs a general computation—the creation of task-specific low-dimensional manifolds that contain a geometric representation of learned knowledge. The hippocampus geometrically represents both physical location and abstract variables on a neural manifold in mice performing a decision-making task in virtual reality.

In vivo two-photon microscopy provides the foundation for an array of powerful techniques for optically measuring and perturbing neural circuits. However, challenging tissue properties and geometry have prevented high-resolution optical access to regions situated within deep fissures. These regions include the medial prefrontal and medial entorhinal cortex (mPFC and MEC), which are of broad scientific and clinical interest. Here, we present a method for in vivo, subcellular resolution optical access to the mPFC and MEC using microprisms inserted into the fissures. We chronically imaged the mPFC and MEC in mice running on a spherical treadmill, using two-photon laser-scanning microscopy and genetically encoded calcium indicators to measure network activity. In the MEC, we imaged grid cells, a widely studied cell type essential to memory and spatial information processing. These cells exhibited spatially modulated activity during navigation in a virtual reality environment. This method should be extendable to other brain regions situated within deep fissures, and opens up these regions for study at cellular resolution in behaving animals using a rapidly expanding palette of optical tools for perturbing and measuring network structure and function. Significance High-resolution optical tools provide unprecedented capabilities for probing brain function in behaving animals, but require the ability to deliver and collect light to and from the brain with high spatial precision. This has proven challenging in regions within deep fissures in the brain, including the medial prefrontal and medial entorhinal cortex (mPFC and MEC), which are of broad scientific and clinical interest. We developed a general method for optical access to brain regions situated within deep fissures, and demonstrate its use in the mPFC and MEC by optically measuring the activity of neurons in behaving mice. In the MEC, we recorded the activity of grid cells—a widely studied group of neurons important for memory and spatial information processing—while mice navigated in a virtual reality environment.

Introduction Chimeric antigen receptor T‐cell (CAR‐T) therapy is highly effective in B‐cell blood cancers, but there is limited data on its safety and efficacy in intra‐cardiac lymphoma, due to the potential risks of cardiotoxicity and pseudo‐progression. Discussion We discuss four high‐risk cases that were managed with a multi‐disciplinary approach, including baseline cardiac risk assessment and surveillance with multimodal cardiac imaging and serum cardiac biomarkers, elective supportive care in the intensive care unit, and early treatment of cytokine release syndrome. Conclusion CAR‐T therapy can be effective and safe in the treatment of B‐cell blood cancers with intra‐cardiac disease.

Severe COVID‐19 disease is a hyperinflammatory, pro‐thrombotic state. We undertook plasma exchange (PEX) to determine its effects on organ function and thrombo‐inflammatory markers. Seven critically ill adults with severe COVID‐19 respiratory failure (PaO2:FiO2 ratio < 200 mm Hg) requiring invasive or noninvasive ventilatory support and elevated thrombo‐inflammatory markers (LDH >800 IU/L and D‐dimer >1000 μg/L (or doubling from baseline) received PEX, daily, for a minimum of 5 days. No other immunomodulatory medications were initiated during this period. Seven patients matched for age and baseline biochemistry were a comparator group. Coagulation screening revealed no evidence of coagulopathy. However, von Willebrand Factor (VWF) activity, antigen and VWF antigen: ADAMTS13 ratio, Factor VIII and D‐dimers were all elevated. Following 5 days of PEX, plasma levels of all the above, and ferritin levels, were significantly reduced (P < .05) while lymphocyte counts normalized (P < .05). The PaO2:FiO2 ratio increased from a median interquartile range (IQR) of 11.6 (10.8‐19.7) kPa to 18.1 (16.0‐25.9) kPa (P < .05). Similar improvements were not observed in controls. Acute kidney injury (AKI) occurred among five patients in the control arm but not in patients receiving PEX. PEX improved oxygenation, decreased the incidence of AKI, normalized lymphocyte counts and reduced circulating thrombo‐inflammatory markers including D‐Dimer and VWF Ag:ADAMTS13 ratio.

Perceptual decisions rely on learned associations between sensory evidence and appropriate actions, involving the filtering and integration of relevant inputs to prepare and execute timely responses 1 , 2 . Despite the distributed nature of task-relevant representations 3 – 10 , it remains unclear how transformations between sensory input, evidence integration, motor planning and execution are orchestrated across brain areas and dimensions of neural activity. Here we addressed this question by recording brain-wide neural activity in mice learning to report changes in ambiguous visual input. After learning, evidence integration emerged across most brain areas in sparse neural populations that drive movement-preparatory activity. Visual responses evolved from transient activations in sensory areas to sustained representations in frontal-motor cortex, thalamus, basal ganglia, midbrain and cerebellum, enabling parallel evidence accumulation. In areas that accumulate evidence, shared population activity patterns encode visual evidence and movement preparation, distinct from movement-execution dynamics. Activity in movement-preparatory subspace is driven by neurons integrating evidence, which collapses at movement onset, allowing the integration process to reset. Across premotor regions, evidence-integration timescales were independent of intrinsic regional dynamics, and thus depended on task experience. In summary, learning aligns evidence accumulation to action preparation in activity dynamics across dozens of brain regions. This leads to highly distributed and parallelized sensorimotor transformations during decision-making. Our work unifies concepts from decision-making and motor control fields into a brain-wide framework for understanding how sensory evidence controls actions. Brain-wide recordings in mice show that learning leads to sensory evidence integration in many brain areas simultaneously, allowing sensory input to drive global movement preparatory dynamics, which collapse upon movement onset.

In summer, 2011, we investigated suspected glyphosate-resistant (GR) kochia in three chem-fallow fields (designated F1, F2, F3, each farmed by a different grower) in southern Alberta. This study characterizes glyphosate resistance in those populations, based on data from dose–response experiments. In a greenhouse experiment, the three populations exhibited a resistance factor ranging from 4 to 6 based on shoot biomass response (GR50 ratios), or 5 to 7 based on survival response (LD50 ratios). Similar results were found in a field dose–response experiment at Lethbridge, AB, in spring 2012 using the F2 kochia population. In fall 2011, we surveyed 46 fields within a 20-km radius of the three chem-fallow fields for GR kochia. In the greenhouse, populations were screened with glyphosate at 900 g ae ha−1. Seven populations were confirmed as GR, the farthest site located about 13 km from the three originally confirmed populations. An additional GR population more than 100 km away was later confirmed. Populations were screened for acetolactate synthase (ALS)–inhibitor (thifensulfuron ∶ tribenuron) and dicamba resistance in the greenhouse, with molecular characterization of ALS-inhibitor resistance in the F1, F2, and F3 populations. All GR populations were resistant to the ALS-inhibiting herbicide, but susceptible to dicamba. ALS-inhibitor resistance in kochia was conferred by Pro197, Asp376, or Trp574 amino acid substitutions. Based upon a simple empirical model with a parameter for selection pressure, calculated from weed relative abundance and glyphosate efficacy, and a parameter for seedbank longevity, kochia, wild oat, and green foxtail were the top three weeds, respectively, predicted at risk of selection for glyphosate resistance in the semiarid Grassland region of the Canadian prairies; wild oat, green foxtail, and cleavers species were predicted at greatest risk in the subhumid Parkland region. This study confirms the first occurrence of a GR weed in western Canada. Future research on GR kochia will include monitoring, biology and ecology, fitness, mechanism of resistance, and best management practices. Nomenclature: Dicamba; glyphosate; thifensulfuron; tribenuron; cleavers: false cleavers, Galium spurium L. or catchweed bedstraw, Galium aparine L.; green foxtail, Setaria viridis (L.) Beauv.; kochia, Kochia scoparia (L.) Schrad. KCHSC, synonym: Bassia scoparia (L.) A.J. Scott.; wild oat, Avena fatua L.

Progress in systems neuroscience requires effective tools and techniques for probing neural circuits, and for analyzing the resulting data in ways that drive theoretical insight. This thesis consists of three parts, aimed broadly toward furthering the measurement and analysis of neural circuits. In the first part, we present methods for two-photon imaging of brain regions situated in deep fissures, enabling the use of cellular resolution optical tools for probing areas such as the medial prefrontal cortex (mPFC) and medial entorhinal cortex (MEC). We demonstrate recordings of population activity in the mPFC and grid cells in the MEC in behaving mice. In the second part, we present an optical approach for measuring dopaminergic input to the mPFC with high spatiotemporal resolution, which has not been feasible using traditional methods. We demonstrate recordings of mPFC dopamine signals in behaving mice, and present preliminary evidence for fine-scale heterogeneity across individual dopaminergic axons. In the third part, we present a new unsupervised learning algorithm for inferring underlying, nonlinear structure in neuronal population activity. We use this algorithm to characterize the geometric properties of hippocampal activity and their relationship to behavior. And, we propose a conceptual model explaining how neural coding and trial-to-trial variability both arise from movement along a low dimensional, nonlinear activity manifold, driven by internal cognitive processes.

Hippocampal neurons fire selectively in local behavioral contexts such as the position in an environment or phase of a task, and are thought to form a cognitive map of task-relevant variables. However, their activity varies over repeated behavioral conditions, such as different runs through the same position or repeated trials. Although widely observed across the brain, such variability is not well understood, and could reflect noise or structure, such as the encoding of additional cognitive information. Here, we introduce a conceptual model to explain variability in terms of underlying, population-level structure in single-trial neural activity. To test this model, we developed a novel unsupervised learning algorithm incorporating temporal dynamics, in order to characterize population activity as a trajectory on a nonlinear manifold--a space of possible network states. The manifold's structure captures correlations between neurons and temporal relationships between states, constraints arising from underlying network architecture and inputs. Using measurements of activity over time but no information about exogenous behavioral variables, we recovered hippocampal activity manifolds during spatial and non-spatial cognitive tasks in rats. Manifolds were low dimensional and smoothly encoded task-related variables, but contained an extra dimension reflecting information beyond the measured behavioral variables. Consistent with our model, neurons fired as a function of overall network state, and fluctuations in their activity across trials corresponded to variation in the underlying trajectory on the manifold. In particular, the extra dimension allowed the system to take different trajectories despite repeated behavioral conditions. Furthermore, the trajectory could temporarily decouple from current behavioral conditions and traverse neighboring manifold points corresponding to past, future, or nearby behavioral states. Our results suggest that trial-to-trial variability in the hippocampus is structured, and may reflect the operation of internal cognitive processes. The manifold structure of population activity is well-suited for organizing information to support memory, planning, and reinforcement learning. In general, our approach could find broader use in probing the organization and computational role of circuit dynamics in other brain regions.

Self-interacting dark matter (SIDM) offers the potential to mitigate some of the discrepancies between simulated cold dark matter (CDM) and observed galactic properties. We introduce a physically motivated SIDM model to understand the effects of self interactions on the properties of Milky Way and dwarf galaxy sized haloes. This model consists of dark matter with a nearly degenerate excited state, which allows for both elastic and inelastic scattering. In particular, the model includes a significant probability for particles to up-scatter from the ground state to the excited state. We simulate a suite of zoom-in Milky Way-sized N-body haloes with six models with different scattering cross sections to study the effects of up-scattering in SIDM models. We find that the up-scattering reaction greatly increases the central densities of the main halo through the loss of kinetic energy. However, the physical model still results in significant coring due to the presence of elastic scattering and down-scattering. These effects are not as apparent in the subhalo population compared to the main halo, but the number of subhaloes is reduced compared to CDM.