Connectomic Identification and Three-Dimensional Color Tuning of S-OFF Midget Ganglion Cells in the Primate Retina

In the trichromatic primate retina, the “midget” retinal ganglion cell is the classical substrate for red–green color signaling, with a circuitry that enables antagonistic responses between long (L)- and medium (M)-wavelength-sensitive cone inputs. Previous physiological studies showed that some OFF midget ganglion cells may receive sparse input from short (S)-wavelength-sensitive cones, but the effect of S-cone inputs on the chromatic tuning properties of such cells has not been explored. Moreover, anatomical evidence for a synaptic pathway from S cones to OFF midget ganglion cells through OFF midget bipolar cells remains ambiguous. In this study, we address both questions for the macaque monkey retina. First, we used serial block-face electron microscopy to show that every S cone in the parafoveal retina synapses principally with a single OFF midget bipolar cell, which in turn forms a private-line connection with an OFF midget ganglion cell. Second, we used patch electrophysiology to characterize the chromatic tuning of OFF midget ganglion cells in the near peripheral retina that receive combined input from L, M, and S cones. These “S-OFF” midget cells have a characteristic S-cone spatial signature, but demonstrate heterogeneous color properties due to the variable strength of L, M, and S cone input across the receptive field. Together, these findings strongly support the hypothesis that the OFF midget pathway is the major conduit for S-OFF signals in primate retina and redefines the pathway as a chromatically complex substrate that encodes color signals beyond the classically recognized L versus M and S versus L+M cardinal mechanisms

Neural locus of color afterimages.

After fixating on a colored pattern, observers see a similar pattern in complementary colors when the stimulus is removed. Afterimages were important in disproving the theory that visual rays emanate from the eye[1], in demonstrating inter-ocular interactions[2], and in revealing the independence of binocular-vision from eye-movements[3]. Afterimages also prove invaluable in exploring selective attention[4], filling-in[5], and consciousness[6]. Proposed physiological mechanisms for color afterimages range from bleaching of cone photo-pigments[7] to cortical adaptation[4–6, 8, 9], but direct neural measurements have not been reported. We introduce a time-varying method for evoking after-images, which provides precise measurements of adaptation and a direct link between visual percepts and neural responses[10]. We then use in vivo electrophysiological recordings to show that all three classes of primate retinal ganglion cells exhibit subtractive adaptation to prolonged stimuli, with much slower time-constants than those expected of photoreceptors. At the cessation of the stimulus, ganglion cells generate rebound responses that can provide afterimage signals for later neurons. Our results indicate that afterimage signals are generated in the retina, but may be modified like other retinal signals by cortical processes[4–6], so that evidence presented for cortical generation of color afterimages[8, 9] is explainable by spatio-temporal factors that apply to all signals

Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast reconfirmation technology

BACKGROUND Pauses in chest compressions (CCs) have a negative association with survival from cardiac arrest. Electrocardiographic (ECG) rhythm analysis and defibrillator charging are significant contributors to CC pauses. OBJECTIVE Accuracy of the Analysis During Compressions with Fast Reconfirmation (ADC-FR) algorithm, which features automated rhythm analysis and charging during CCs to reduce CC pauses, was retrospectively determined in a large database of ECGs from 2701 patients with out-of-hospital cardiac arrest. METHODS The ADC-FR algorithm generated a total of 7264 advisories, of which 3575 were randomly assigned to a development data set and 3689 to a test data set. With ADC-FR, a high-pass digital filter is used to remove CC artifacts, while the underlying ECG rhythm is automatically interpreted. When CCs are paused at the end of the 2-minute cardiopulmonary resuscitation interval, a 3-second reconfirmation analysis is performed using the artifact-free ECG to confirm the shock/no-shock advisory. The sensitivity and specificity of the ADC-FR algorithm in correctly identifying shockable/nonshockable rhythms during CCs were calculated. RESULTS In both data sets, the accuracy of the ADC-FR algorithm for each ECG rhythm exceeded the recommended performance goals, which apply to a standard artifact-free ECG analysis. Sensitivity and specificity were 97% and 99%, respectively, for the development data set and 95% and 99% for the test data set. CONCLUSION The ADC-FR algorithm is highly accurate in discriminating shockable and nonshockable rhythms and can be used to reduce CC pauses

Anesthetic management of a patient with hypertrophic obstructive cardiomyopathy undergoing modified radical mastectomy.

Hypertrophic obstructive cardiomyopathy (HOCM) is a rare genetic disorder characterized by left ventricular outflow tract (LVOT) obstruction. Clinical presentation ranges from absence of symptoms to sudden death. Our 60 year old Patient scheduled for left modified radical mastectomy had HOCM since seventeen years with severe LVOT obstruction and mitral regurgitation. An implantable cardioverter defibrillator (ICD) and permanent pacemaker (PPM) was inserted 15 months earlier for ventricular tachycardia. Anesthetic management of these Patients presents considerable challenges and requires maintenance of desired hemodynamic parameters and management of specific complications. Factors like tachycardia, hypovolemia, vasodilation and increased cardiac contractility leads to exacerbation of the obstruction. In our Patient there was the additional consideration of the ICD which required to be turned off during surgery with full provision for external defibrillation. We managed to successfully maintain the desired hemodynamics throughout the surgery and the Patient was discharged home on the seventh postoperative day

Joint optimization of Age of Information and Energy Efficiency in IoT Networks

Age of information (AoI) refers to the freshness of data generated by a status-update system. It is a crucial metric in networks such as Internet of things (IoT), specially when the underlying application demands fresh update. In environmental monitoring and smart agriculture, apart from the importance of AoI, energy efficiency (EE) becomes inevitable owing to network longevity. This paper studies an IoT network where the end devices transfer their information to a central gateway residing on a moving platform such as a tractor, which collects information from a large number of sensors in an agri-field. An optimal trajectory of the mobile reader is proposed using a modified nearest neighbor algorithm to gather the information from randomly distributed sensors. A clustering algorithm is also used to cluster the data in such a way that the overall EE of the network is maximized keeping a desired AoI and outage probability

NOMA Enhanced Backscatter Communication for Green IoT Networks

Backscatter communication has recently emerged as a promising technology to enable the passive sensing-based Internet-of-things (IoT) applications. In a backscatter communication network, uplink transmissions of multiple nodes are usually multiplexed in time- or frequency-domain to avoid collisions, yet it is desirable to improve the uplink capacity further. In this paper, we study a wireless-powered backscatter communication system, where the sensors use a hybrid channel access scheme by combining time division multiplexing access (TDMA) with power-domain non-orthogonal multiple access (PD-NOMA) to enhance the system performance in terms of outage probability and throughput. Our analysis shows that the proposed PD-NOMA increases both the spectrum efficiency and the throughput of the system

Retrospective Study of Midazolam Protocol for Prehospital Behavioral Emergencies

Introduction: Agitated patients in the prehospital setting pose challenges for both patient care and emergency medical services (EMS) provider safety. Midazolam is frequently used to control agitation in the emergency department setting; however, limited data exist in the prehospital setting. We describe our experience treating patients with midazolam for behavioral emergencies in a large urban EMS system. We hypothesized that using midazolam for acute agitation leads to improved clinical conditions without causing significant clinical deterioration.Methods: We performed a retrospective review of EMS patient care reports following implementation of a behavioral emergencies protocol in a large urban EMS system from February 2014–June 2016. For acute agitation, paramedics administered midazolam 1 milligram (mg) intravenous (IV), 5 mg intramuscular (IM), or 5 mg intranasal (IN). Results were analyzed using descriptive statistics, Levene’s test for assessing variance among study groups, and t-test to evaluate effectiveness based on route.Results: In total, midazolam was administered 294 times to 257 patients. Median age was 30 (interquartile range 24–42) years, and 66.5% were male. Doses administered were 1 mg (7.1%) and 5 mg (92.9%). Routes were IM (52.0%), IN (40.8%), and IV (7.1%). A second dose was administered to 37 patients. In the majority of administrations, midazolam improved the patient’s condition (73.5%) with infrequent adverse events (3.4%). There was no significant difference between the effectiveness of IM and IN midazolam (71.0% vs 75.4%; p = 0.24).Conclusion: A midazolam protocol for prehospital agitation was associated with reduced agitation and a low rate of adverse events

A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNN-LSTM hybrid model to fully explore the temporal-spatial information in sEMG. Firstly, CNN is utilized to extract deep features from sEMG spectrum, then these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intra-session and inter-session evaluations illustrate that CNN-LSTM significantly outperforms CNN, LSTM and several representative machine learning approaches, particularly when complex wrist movements are activated

A study of frequency and pulses for stepper motor controller system by using programmable logic controller

The stepper motor movement process produced different frequency and pulses. This research explained about the frequency and pulses for the stepper motor movement by using Programmable Logic Controller (PLC) as research method. The study was done to find the suitable frequency and pulses for stepper motor movement by developing a prototype stepper motor controller system. The pulse frequency used did not affected the distance of moving load in the stepper motor operations. The increasing number of pulse frequency only will affect the time taken for the stepper motor to complete its operations. The result showed that number of pulse frequency at high operation was 5000 Hz. Pulse number reacted as a manipulated variable that affected both factor which is time taken of stepper motor operation and the distance of moving load