TO STUDY THE SERUM LIPID PROFILE IN ISCHEMIC AND HEMORRHAGIC STROKE AMONG THE PATIENTS IN TERTIARY HEALTH CENTRE

Objectives: To assess, correlate and compare the levels of various parameters of lipid profile in patients with ischemic and hemorrhagic stroke. Methods: The study was conducted as an observational study at the Department of Medicine, People’s Hospital on 100 patients presenting with focal neurological deficit, altered sensorium, or with CT/MRI findings suggestive of stroke during the study duration of 18 months. Based upon the type of stroke, patients were categorized into two groups, i.e. ischemic stroke and hemorrhagic stroke. NCEP-ATP III guidelines were used for estimation of dyslipidemia and association of dyslipidemia was observed with type of stroke. Results: Of 100 cases, ischemic stroke was documented in 74% cases, whereas 26% of patients presented with hemorrhagic stroke. Two groups were comparable in terms of baseline characteristics (p>0.05). Mean total cholesterol levels and total cholesterol and total cholesterol to HDL ratio was significantly higher in ischemic stroke as compared to hemorrhagic stroke (p<0.05). However, mean serum HDL level was significantly lower in patients with ischemic stroke as compared to hemorrhagic stroke (p<0.05). Total cholesterol, and total cholesterol: HDL ratio showed statistically significantly negative correlation with type of stroke and positive correlation was noted between HDL and hemorrhagic stroke. Conclusion: The prevalence of stroke is rising rapidly and the age of presentation of stroke is reducing. Ischemic stroke is the most common type of stroke whereas hemorrhagic stroke is less commonly observed in less than one-third of patients. Dyslipidemia is a significant risk factor for ischemic stroke. Raised Total cholesterol, and total cholesterol: HDL levels and lower HDL levels are independent predictors of ischemic stroke

Saccade Velocity Driven Oscillatory Network Model of Grid Cells

Grid cells and place cells are believed to be cellular substrates for the spatial navigation functions of hippocampus as experimental animals physically navigated in 2D and 3D spaces. However, a recent saccade study on head fixated monkey has also reported grid-like representations on saccadic trajectory while the animal scanned the images on a computer screen. We present two computational models that explain the formation of grid patterns on saccadic trajectory formed on the novel Images. The first model named Saccade Velocity Driven Oscillatory Network -Direct PCA (SVDON—DPCA) explains how grid patterns can be generated on saccadic space using Principal Component Analysis (PCA) like learning rule. The model adopts a hierarchical architecture. We extend this to a network model viz. Saccade Velocity Driven Oscillatory Network—Network PCA (SVDON-NPCA) where the direct PCA stage is replaced by a neural network that can implement PCA using a neurally plausible algorithm. This gives the leverage to study the formation of grid cells at a network level. Saccade trajectory for both models is generated based on an attention model which attends to the salient location by computing the saliency maps of the images. Both models capture the spatial characteristics of grid cells such as grid scale variation on the dorso-ventral axis of Medial Entorhinal cortex. Adding one more layer of LAHN over the SVDON-NPCA model predicts the Place cells in saccadic space, which are yet to be discovered experimentally. To the best of our knowledge, this is the first attempt to model grid cells and place cells from saccade trajectory

Searching for Dark Matter with a Superconducting Qubit

Detection mechanisms for low mass bosonic dark matter candidates, such the axion or hidden photon, leverage potential interactions with electromagnetic fields, whereby the dark matter (of unknown mass) on rare occasion converts into a single photon. Current dark matter searches operating at microwave frequencies use a resonant cavity to coherently accumulate the field sourced by the dark matter and a near standard quantum limited (SQL) linear amplifier to read out the cavity signal. To further increase sensitivity to the dark matter signal, sub-SQL detection techniques are required. Here we report the development of a novel microwave photon counting technique and a new exclusion limit on hidden photon dark matter. We operate a superconducting qubit to make repeated quantum non-demolition measurements of cavity photons and apply a hidden Markov model analysis to reduce the noise to 15.7 dB below the quantum limit, with overall detector performance limited by a residual background of real photons. With the present device, we perform a hidden photon search and constrain the kinetic mixing angle to $\epsilon \leq 1.68 \times 10^{-15}$ in a band around 6.011 GHz (24.86 $\mu$eV) with an integration time of 8.33 s. This demonstrated noise reduction technique enables future dark matter searches to be sped up by a factor of 1300. By coupling a qubit to an arbitrary quantum sensor, more general sub-SQL metrology is possible with the techniques presented in this work.Comment: 15 pages, 14 figures, 2 table. Dark matter exclusion analysis modified to include experimental systematics. Discussion of background calibration and detector compatibility with tunable cavity added to conclusion. Future optimizations and integration into axion search sections moved to Supplemental Material. References update