Abstract-This letter proposes two new variable step-size algorithms for normalized least mean square and affine projection. The proposed schemes lead to faster convergence rate and lower misadjustment error.Index Terms-Adaptive filters, affine projection algorithm, normalized least mean square (NLMS), variable step-size.

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SummaryObjectives-Therapeutic hypothermia (TH) after cardiac arrest (CA) improves outcomes in a fraction of patients. To enhance the administration of TH, we studied brain electrophysiological monitoring in determining the benefit of early initiation of TH compared to conventional administration in a rat model. Methods-Using an asphyxial CA model, we compared the benefit of immediate hypothermia (IH, T=33°C, immediately post-resuscitation, maintained 6 hours) to conventional hypothermia (CH, T=33°C, starting 1 hour post-resuscitation, maintained 12 hours) via surface cooling. We tracked quantitative EEG using relative entropy (qEEG) with outcome verification by serial Neurological Deficit Score (NDS) and quantitative brain histopathological damage scoring (HDS). Thirty-two rats were divided into 4 groups based on CH/IH and 7/9-minute duration of asphyxial CA. Four sham rats were included for evaluation of the effect of hypothermia on qEEG.Results-The 72-hour NDS of the IH group was significantly better than the CH group for both 7-minute (74/63; Median, IH/CH, p<0.001) and 9-minute (54/47, p=0.022) groups. qEEG showed greater recovery with IH (p<0.001) and significantly less neuronal cortical injury by HDS (IH: 18.9 ±2.5% versus CH: 33.2±4.4%, p=0.006). The 1-hour post-resuscitation qEEG correlated well with 72-hour NDS (p<0.05) and 72-hour behavioral subgroup of NDS (p<0.01). No differences in qEEG were noted in the sham group. *Corresponding author: Xiaofeng JIA MD, PhD, CRB II Building 3M-South, 1550 Orleans Street, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Telephone number: +1-410-502-2820, Fax: +1-410-502-7869, E-mail address: xjia1@jhmi.edu. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Portions of this work were previously presented in 4 th Annual Meeting of the Neurocritical Care Society in Baltimore, MD (November 2006) and at the 36 th Annual Meeting of the Society for Neuroscience at Atlanta, Georgia (October 2006) where it was selected for the lay language summary press book. Conflict of interest statementThere are no conflicts of interest in this study. Conclusions-Immediate but shorter hypothermia compared to CH leads to better functional outcome in rats after 7-and 9-minute CA. The beneficial effect of IH was readily detected by neuroelectrophysiological monitoring and histological changes supported the value of this observation. NIH Public Access

We test the hypothesis that quantitative electroencephalogram (qEEG) can be used to objectively assess functional electrophysiological recovery of brain after hypothermia in an asphyxial cardiac arrest rodent model. Twenty-eight rats were randomly subjected to 7-min (n = 14) and 9-min (n = 14) asphyxia times. One half of each group (n = 7) was randomly subjected to hypothermia (T = 33 degrees C for 12 h) and the other half (n = 7) to normothermia (T = 37 degrees C). Continuous physiologic monitoring of blood pressure, EEG, and core body temperature monitoring and intermittent arterial blood gas (ABG) analysis was undertaken. Neurological recovery after resuscitation was monitored using serial Neurological Deficit Score (NDS) calculation and qEEG analysis. Information Quantity (IQ), a previously validated measure of relative EEG entropy, was employed to monitor electrical recovery. The experiment demonstrated greater recovery of IQ in rats treated with hypothermia compared to normothermic controls in both injury groups (P < 0.05). The 72-h NDS of the hypothermia group was also significantly improved compared to the normothermia group (P < 0.05). IQ values measured at 4 h had a strong correlation with the primary neurological outcome measure, 72-h NDS score (Pearson correlation 0.746, 2-tailed significance <0.001). IQ is sensitive to the acceleration of neurological recovery as measured NDS after asphyxial cardiac arrest known to occur with induced hypothermia. These results demonstrate the potential utility of qEEG-IQ to track the response to neuroprotective hypothermia during the early phase of recovery from cardiac arrest.

While previous efforts in Brain-Machine Interfaces (BMI) have looked at decoding movement intent or hand and arm trajectory, current neural control strategies have not focused on the decoding of dexterous actions such as finger movements. The present work demonstrates the asynchronous deciphering of the neural coding associated with the movement of individual and combined fingers. Single-unit activities were recorded sequentially from a population of neurons in the M1 hand area of trained rhesus monkeys during flexion and extension movements of each finger and the wrist. Non-linear filters were used to decode both movement intent and movement type from randomly selected neuronal ensembles. Average asynchronous decoding accuracies as high as 99.8% ± 0.1%, 96.2% ± 1.8%, and 90.5% ± 2.1%, were achieved for individuated finger and wrist movements with three monkeys. Average decoding accuracy was still 92.5% ± 1.1% when combined movements of two fingers were included. These results demonstrate that it is possible to asynchronously decode dexterous finger movements from a neuronal ensemble with high accuracy. This is an important step towards the development of a BMI for direct neural control of a state-of-the-art, multi-fingered hand prosthesis.

In this paper, we provide a quantitative electroencephalogram (EEG) analysis to study the effect of hypothermia on the neurological recovery of brain after cardiac arrest. We hypothesize that the brain injury results in a reduction in information of the brain rhythm. To measure the information content of the EEG a new measure called information quantity (IQ), which is the Shannon entropy of decorrelated EEG signals, is developed. For decorrelating EEG signals, we use the discrete wavelet transform (DWT) which is known to have good decorrelating properties and to show a good match to the standard clinical bands in EEG. In measuring the amount of information, IQ shows better tracking capability for dynamic amplitude change and frequency component change than conventional entropy-based measures. Experiments are carried out in rodents (n = 30) to monitor the neurological recovery after cardiac arrest. In addition, EEG signal recovery under normothermic (37 °C) and hypothermic (33 °C) resuscitation following 5, 7, and 9 min of cardiac arrest is recorded and analyzed. Experimental results show that the IQ is greater for hypothermic than normothermic rats, with an IQ difference of more than 0.20 (0.20 ± 0.11 is 95% condidence interval). The results quantitatively support the hypothesis that hypothermia accelerates the electrical recovery from brain injury after cardiac arrest.

A quantum-dot transistor based on silicon self-assembled quantum dots has been fabricated. The device shows staircases and oscillations in the drain current at room temperature. These data are interpreted as due to single electron tunneling through the dots located in the shortest current path between the source and the drain electrodes. The dot size calculated from the data is ∼7 nm, which is consistent with the size of the self-assembled dots incorporated in the transistor.

The authors modified the charge decay model of silicon-oxide-nitride-oxide-silicon-type memory at the temperatures above 150°C. The modified model includes the effect of the internal electric field induced by the charges trapped in silicon nitride layer. The authors extracted the trap density distributions in energy level of the Si-rich silicon nitride using the model and compared them with those of stoichiometric silicon nitride. It has been revealed that the Si-rich silicon nitride has larger trap density in shallow energy level than the stoichiometric silicon nitride and this relation is reversed as the energy level goes deeper.