Higher Frequency Network Activity Flow Predicts Lower Frequency Node Activity in Intrinsic Low-Frequency BOLD Fluctuations

The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based timefrequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow- 5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz) and slow-2 (0.198–0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow- 2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not ‘entirely’ at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural connectivity

Oscillatory Motor Network Activity During Rest and Movement: An fNIRS Study

Coherent network oscillations (\u3c0.1 Hz) linking distributed brain regions are commonly observed in the brain during both rest and task conditions. What oscillatory network exists and how network oscillations change in connectivity strength, frequency and direction when going from rest to explicit task are topics of recent inquiry. Here, we study network oscillations within the sensorimotor regions of able-bodied individuals using hemodynamic activity as measured by functional near-infrared spectroscopy (fNIRS). Using spectral interdependency methods, we examined how the supplementary motor area (SMA), the left premotor cortex (LPMC) and the left primary motor cortex (LM1) are bound as a network during extended resting state (RS) and between-tasks resting state (btRS), and how the activity of the network changes as participants execute left, right, and bilateral hand (LH, RH, and BH) finger movements. We found: (i) power, coherence and Granger causality (GC) spectra had significant peaks within the frequency band (0.01–0.04 Hz) during RS whereas the peaks shifted to a bit higher frequency range (0.04–0.08 Hz) during btRS and finger movement tasks, (ii) there was significant bidirectional connectivity between all the nodes during RS and unidirectional connectivity from the LM1 to SMA and LM1 to LPMC during btRS, and (iii) the connections from SMA to LM1 and from LPMC to LM1 were significantly modulated in LH, RH, and BH finger movements relative to btRS. The unidirectional connectivity from SMA to LM1 just before the actual task changed to the bidirectional connectivity during LH and BH finger movement. The uni-directionality could be associated with movement suppression and the bi-directionality with preparation, sensorimotor update and controlled execution. These results underscore that fNIRS is an effective tool for monitoring spectral signatures of brain activity, which may serve as an important precursor before monitoring the recovery progress following brain injury

A comparative analysis of cloud based services platform

Data processing at high speeds and storage solutions on a massive scale are two things that are becoming increasingly important as a result of the proliferation of new start-up companies and the vast volume of data that users consume daily. The cloud platforms offer a solution to these issues in the shape of virtualization, which entails the creation of several distinct virtual machines within a single actual system. Because of this, the efficiency of the processor increases, and it now has far less downtime. The four major cloud platforms are discussed, and each has its own set of benefits, which helps make them successful in their unique conducts. Although Amazon EC2 is one of the most used and earliest that offers assistance for the utmost numeral of already configured system software termed as operating systems, which falls short in terms of both its scope and the support that it makes available. In a similar vein, the Google Cloud Platform can support the greatest number of databases and has an excellent repository of in-built libraries. The Microsoft Azure has the greatest number of users out of four, however, it is badly lacking in the database supports it offers. IBM Watson offers user friendly services but has scalability issues. As a result, a conclusion has been reached, that the selecting a particular cloud platform, there is extreme dependence on the needs of the users. These requirements may vary from client to client which are the end-users of the applications. Before deciding on a particular platform, it is best to conduct a thorough analysis of the needs.</p

Design and implementation of energy reshaping based fuzzy logic control for optimal power extraction of PMSG wind energy converter

Given the greater penetration of wind power, the impact of wind generators on grid electricity reliability imposes additional requirements. One of the most common technologies in wind power generating schemes is the permanent magnet synchronous generator (PMSG) converter. However, the controller calculation is difficult due to the nonlinear dynamical and time-varying characteristics of this type of conversion system. This study develops a unique intelligent controller approach based on the passivity notion that tracks velocity and maintains it functioning at the optimum torque. To address the robustness issues encountered by traditional generator-side converter (MSC) strategies such as proportional-integral (PI), this suggested scheme integrates a passivity-based procedure with a fuzzy logic control (FLC) methodology for a PMSG-based wind power converter. The suggested controller is distinguished by the fact that the nonlinear features are compensated in a damped manner rather than canceled. To achieve the required dynamic, the fuzzy controller is used, which ensures quick convergence and global stability of the closed loop system. The development of the maximum power collected, the lowered fixed gains, and the real-time application of the control method are the primary contributions and novelties. The primary objectives of this project are to manage DC voltage and attain adequate reactive power levels in order to provide dependable and efficient electricity to the grid. The proposed scheme is being used to regulate the MSC, while the grid-side employs a traditional proportional-integral method. The efficiency of the suggested technique is investigated numerically using MATLAB/Simulink software. Furthermore, the processor-in-the-loop (PIL) tests are carried out to demonstrate that the suggested regulator is practically implementable

Functional Organization and Restoration of the Brain Motor-Execution Network After Stroke and Rehabilitation

Multiple cortical areas of the human brain motor system interact coherently in the low frequency range (\u3c0.1 Hz), even in the absence of explicit tasks. Following stroke, cortical interactions are functionally disturbed. How these interactions are affected and how the functional organization is regained from rehabilitative treatments as people begin to recover motor behaviors has not been systematically studied. We recorded the intrinsic functional magnetic resonance imaging (fMRI) signals from 30 participants: 17 young healthy controls and 13 aged stroke survivors. Stroke participants underwent mental practice (MP) or both mental practice and physical therapy (MP+PT) within 14–51 days following stroke. We investigated the network activity of five core areas in the motor-execution network, consisting of the left primary motor area (LM1), the right primary motor area (RM1), the left pre-motor cortex (LPMC), the right pre-motor cortex (RPMC) and the supplementary motor area (SMA). We discovered that (i) the network activity dominated in the frequency range 0.06–0.08 Hz for all the regions, and for both able-bodied and stroke participants (ii) the causal information flow between the regions: LM1 and SMA, RPMC and SMA, RPMC and LM1, SMA and RM1, SMA and LPMC, was reduced significantly for stroke survivors (iii) the flow did not increase significantly after MP alone and (iv) the flow among the regions during MP+PT increased significantly. We also found that sensation and motor scores were significantly higher and correlated with directed functional connectivity measures when the stroke-survivors underwent MP+PT but not MP alone. The findings provide evidence that a combination of mental practice and physical therapy can be an effective means of treatment for stroke survivors to recover or regain the strength of motor behaviors, and that the spectra of causal information flow can be used as a reliable biomarker for evaluating rehabilitation in stroke survivors

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to &lt;90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], &gt;300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of &lt;15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P&lt;0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P&lt;0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Trading Behavior and the Unbiasedness of the Market Reaction to Dividend Announcements.

This article examines the price formation process during dividend announcement day using daily closing prices and transactions data. The authors find that the unconditional positive excess returns, first documented by A. Kalay and U. Loewenstein (1985), are higher for small-firm and low-priced stocks. Price volatility and trading volume also increase during this period. Examination of trade prices relative to the bid-ask spread and volume of trades at bid and asked prices shows that the excess returns cannot be attributed to measurement errors or to spillover effects of tax-related ex-day trading. Rather, the price behavior is related to the absorption of dividend information. Copyright 1995 by American Finance Association.

The relationship between ownership, financing decisions and firm performance:a signaling model

We develop a signaling model to show how adverse selection and moral hazard interact to determine a firm's ownership structure and financing and investment decisions endogenously. Testable implications are derived regarding the relationship between insider ownership, performance measures such as Tobin's Q ratio, and elements of financial structure such as the debt-equity ratio