Anti-manic effect of deep brain stimulation of the ventral tegmental area in an animal model of mania induced by methamphetamine

BACKGROUND

Treatment of refractory bipolar disorder (BD) is extremely challenging. Deep brain stimulation (DBS) holds promise as an effective treatment intervention. However, we still understand very little about the mechanisms of DBS and its application on BD.

AIM

The present study aimed to investigate the behavioural and neurochemical effects of ventral tegmental area (VTA) DBS in an animal model of mania induced by methamphetamine (m-amph).

METHODS

Wistar rats were given 14 days of m-amph injections, and on the last day, animals were submitted to 20 min of VTA DBS in two different patterns: intermittent low-frequency stimulation (LFS) or continuous high-frequency stimulation (HFS). Immediately after DBS, manic-like behaviour and nucleus accumbens (NAc) phasic dopamine (DA) release were evaluated in different groups of animals through open-field tests and fast-scan cyclic voltammetry. Levels of NAc dopaminergic markers were evaluated by immunohistochemistry.

RESULTS

M-amph induced hyperlocomotion in the animals and both DBS parameters reversed this alteration. M-amph increased DA reuptake time post-sham compared to baseline levels, and both LFS and HFS were able to block this alteration. LFS was also able to reduce phasic DA release when compared to baseline. LFS was able to increase dopamine transporter (DAT) expression in the NAc.

CONCLUSION

These results demonstrate that both VTA LFS and HFS DBS exert anti-manic effects and modulation of DA dynamics in the NAc. More specifically the increase in DA reuptake driven by increased DAT expression may serve as a potential mechanism by which VTA DBS exerts its anti-manic effects.

A self-stiffening compliant intracortical microprobe

Utilising a flexible intracortical microprobe to record/stimulate neurons minimises the incompatibility between the implanted microprobe and the brain, reducing tissue damage due to the brain micromotion. Applying bio-dissolvable coating materials temporarily makes a flexible microprobe stiff to tolerate the penetration force during insertion. However, the inability to adjust the dissolving time after the microprobe contact with the cerebrospinal fluid may lead to inaccuracy in the microprobe positioning. Furthermore, since the dissolving process is irreversible, any subsequent positioning error cannot be corrected by re-stiffening the microprobe. The purpose of this study is to propose an intracortical microprobe that incorporates two compressible structures to make the microprobe both adaptive to the brain during operation and stiff during insertion. Applying a compressive force by an inserter compresses the two compressible structures completely, resulting in increasing the equivalent elastic modulus. Thus, instant switching between stiff and soft modes can be accomplished as many times as necessary to ensure high-accuracy positioning while causing minimal tissue damage. The equivalent elastic modulus of the microprobe during operation is ≈ 23 kPa, which is ≈ 42% less than the existing counterpart, resulting in ≈ 46% less maximum strain generated on the surrounding tissue under brain longitudinal motion. The self-stiffening microprobe and surrounding neural tissue are simulated during insertion and operation to confirm the efficiency of the design. Two-photon polymerisation technology is utilised to 3D print the proposed microprobe, which is experimentally validated and inserted into a lamb's brain without buckling.

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study

BACKGROUND

This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants.

OBJECTIVE

The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL.

METHODS

A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement.

RESULTS

Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%).

CONCLUSIONS

Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

SmartFSCV: An Artificial Intelligence Enabled Miniaturised FSCV Device Targeting Serotonin

Goal: Dynamically monitoring serotonin in real-time within target brain regions would significantly improve the diagnostic and therapeutic approaches to a variety of neurological and psychiatric disorders. Current systems for measuring serotonin lack immediacy and portability and are bulky and expensive. Methods: We present a new miniaturised device, named SmartFSCV, designed to monitor dynamic changes of serotonin using fast-scan cyclic voltammetry (FSCV). This device outputs a precision voltage potential between -3 to +3 V, and measures current between -1.5 to +1.5 μA with nano-ampere accuracy. The device can output modifiable arbitrary waveforms for various measurements and uses an N-shaped waveform at a scan-rate of 1000 V/s for sensing serotonin. Results: Four experiments were conducted to validate SmartFSCV: static bench test, dynamic serotonin test and two artificial intelligence (AI) algorithm tests. Conclusions: These tests confirmed the ability of SmartFSCV to accurately sense and make informed decisions about the presence of serotonin using AI.

ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation

Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabular-type data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future.

Dual core photonic crystal fiber based plasmonic refractive index sensor with ultra-wide detection range

In this study, an ultra-wide range plasmonic refractive index sensor based on dual core photonic crystal fiber is suggested and analyzed numerically. The proposed design achieves fabrication feasibility by employing external sensing mechanism in which silver is deposited onto the flat outer surface of the fiber as plasmonic material. A thin layer of titanium oxide (TiO2) is considered on top of the silver layer for preventing its oxidation problem. The sensor attains identification of a vast array of analytes consisting a wide range of refractive indices of 1.10 - 1.45. It achieves a maximum spectral sensitivity of 24300 nm/RIU along with its corresponding resolution of 4.12 × 10-6 RIU. The maximum figure of merit of the sensor is 120 RIU-1. The sensor also supports amplitude interrogation approach and exhibits a maximum amplitude sensitivity of 172 RIU-1. The impact of the design parameters such as radius of air holes, polishing distance, thickness of silver and titanium oxide layers are investigated thoroughly. An ultra-wide detection range with high sensitivity, fabrication feasibility, and easy application make the sensor a potential candidate for detection of a wide array of bio-originated materials, chemicals, and other analytes.

Oxycodone-induced dopaminergic and respiratory effects are modulated by deep brain stimulation

Introduction: Opioids are the leading cause of overdose death in the United States, accounting for almost 70,000 deaths in 2020. Deep brain stimulation (DBS) is a promising new treatment for substance use disorders. Here, we hypothesized that VTA DBS would modulate both the dopaminergic and respiratory effect of oxycodone. Methods: Multiple-cyclic square wave voltammetry (M-CSWV) was used to investigate how deep brain stimulation (130 Hz, 0.2 ms, and 0.2 mA) of the rodent ventral segmental area (VTA), which contains abundant dopaminergic neurons, modulates the acute effects of oxycodone administration (2.5 mg/kg, i.v.) on nucleus accumbens core (NAcc) tonic extracellular dopamine levels and respiratory rate in urethane-anesthetized rats (1.5 g/kg, i.p.). Results: I.V. administration of oxycodone resulted in an increase in NAcc tonic dopamine levels (296.9 ± 37.0 nM) compared to baseline (150.7 ± 15.5 nM) and saline administration (152.0 ± 16.1 nM) (296.9 ± 37.0 vs. 150.7 ± 15.5 vs. 152.0 ± 16.1, respectively, p = 0.022, n = 5). This robust oxycodone-induced increase in NAcc dopamine concentration was associated with a sharp reduction in respiratory rate (111.7 ± 2.6 min-1 vs. 67.9 ± 8.3 min-1; pre- vs. post-oxycodone; p < 0.001). Continuous DBS targeted at the VTA (n = 5) reduced baseline dopamine levels, attenuated the oxycodone-induced increase in dopamine levels to (+39.0% vs. +95%), and respiratory depression (121.5 ± 6.7 min-1 vs. 105.2 ± 4.1 min-1; pre- vs. post-oxycodone; p = 0.072). Discussion: Here we demonstrated VTA DBS alleviates oxycodone-induced increases in NAcc dopamine levels and reverses respiratory suppression. These results support the possibility of using neuromodulation technology for treatment of drug addiction.

Comparative Study and Multi-Parameter Analysis to Optimize Device Structure of Triboelectric Nanogenerators

Triboelectric nanogenerator is becoming one of the most efficient energy harvesting device among all mechanical energy harvesters. This device consists of dielectric friction layers and metal electrode which generates electrical charges using electrostatic induction effect. There are several factors influencing the performance of this generator which needs to be evaluated prior to experiment. The absence of a universal technique for TENG simulation makes the device design and optimization hard before practical fabrication, which also lengthens the exploration and advancement cycle and hinders the arrival of practical applications. In order to deepen the understanding the core physic behind the working process of this device, this work will provide comparative analysis on different modes of TENG. Systematic investigation on different material combination, effect of material thickness, impact of surface patterning is evaluated to shortlist the best material combination. COMSOL Multiphysics simulating environment is used to design, model and analyze factor affecting the overall output performance of TENG. The stationary study in this simulator is performed using 2D geometry structure with higher mesh density. During this study short circuit and open circuit condition were applied to observe the behavior of charge and electric potential produced. This observation is analyzed by plotting charge transfer/electric potential against various displacement distances of dielectric friction layers. Overall, this study provides an excellent understanding and multi-parameter analysis on basic theoretical and simulation modeling of TENG device.

Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review

BACKGROUND AND OBJECTIVE

Early identification of patients at risk of deterioration can prevent life-threatening adverse events and shorten length of stay. Although there are numerous models applied to predict patient clinical deterioration, most are based on vital signs and have methodological shortcomings that are not able to provide accurate estimates of deterioration risk. The aim of this systematic review is to examine the effectiveness, challenges, and limitations of using machine learning (ML) techniques to predict patient clinical deterioration in hospital settings.

METHODS

A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines using EMBASE, MEDLINE Complete, CINAHL Complete, and IEEExplore databases. Citation searching was carried out for studies that met inclusion criteria. Two reviewers used the inclusion/exclusion criteria to independently screen studies and extract data. To address any discrepancies in the screening process, the two reviewers discussed their findings and a third reviewer was consulted as needed to reach a consensus. Studies focusing on use of ML in predicting patient clinical deterioration that were published from inception to July 2022 were included.

RESULTS

A total of 29 primary studies that evaluated ML models to predict patient clinical deterioration were identified. After reviewing these studies, we found that 15 types of ML techniques have been employed to predict patient clinical deterioration. While six studies used a single technique exclusively, several others utilised a combination of classical techniques, unsupervised and supervised learning, as well as other novel techniques. Depending on which ML model was applied and the type of input features, ML models predicted outcomes with an area under the curve from 0.55 to 0.99.

CONCLUSIONS

Numerous ML methods have been employed to automate the identification of patient deterioration. Despite these advancements, there is still a need for further investigation to examine the application and effectiveness of these methods in real-world situations.

A convolutional attention mapping deep neural network for classification and localization of cardiomegaly on chest X-rays

Building a reliable and precise model for disease classification and identifying abnormal sites can provide physicians assistance in their decision-making process. Deep learning based image analysis is a promising technique for enriching the decision making process, and accordingly strengthening patient care. This work presents a convolutional attention mapping deep learning model, Cardio-XAttentionNet, to classify and localize cardiomegaly effectively. We revisit the global average pooling (GAP) system and add a weighting term to develop a light and effective Attention Mapping Mechanism (AMM). The model enables the classification of cardiomegaly from chest X-rays through image-level classification and pixel-level localization only from image-level labels. We leverage some of the advanced ConvNet architectures as a backbone-model of the proposed attention mapping network to build Cardio-XAttentionNet. The proposed model is trained on ChestX-Ray14, which is a publicly accessible chest X-ray dataset. The best single model achieves an overall precision, recall, F-1 measure and area under curve (AUC) scores of 0.87, 0.85, 0.86 and 0.89, respectively, for the classification of the cardiomegaly. The results also demonstrate that the Cardio-XAttentionNet model well captures the cardiomegaly class information at image-level as well as localization at pixel-level on chest x-rays. A comparative analysis between the proposed AMM and existing GAP based models shows that the proposed model achieves a state-of-the-art performance on this dataset for cardiomegaly detection using a single model.

A self-healing magneto-responsive nanocellulose ferrogel and flexible soft strain sensor

Crosslinks are the building blocks of hydrogels and play an important role in their overall properties. They may either be reversible and dynamic allowing for autonomous self-healing properties, or permanent and static resulting in robustness and mechanical strength. Hence, a combination of crosslinks is often required to engineer the 3D network of hydrogels with both autonomous self-healing and required robustness for strain sensing application; however, this complicates the fabrication of such hydrogels. The facile, yet versatile, approach used in this study is to forgo the use of extra crosslinks and instead rely solely on the properties of magnetic nanocellulose to fabricate a tough, stretchy, yet magneto-responsive, ionic conductive ferrogel for strain sensing. The ferrogel also gives stimuli-free and autonomous self-healing capacity, as well as the ability to monitor real-time strain under external magnetic actuation. The ferrogel also functions as a touch-screen pen. Based on our findings, this study has the potential to advance the rational design of multifunctional hydrogels, with applications in soft and flexible strain sensors, health monitoring and soft robotics.

A self-healing nanocomposite double network bacterial nanocellulose/gelatin hydrogel for three dimensional printing

Extrusion-based three-dimensional (3D) printing of gelatin is important for additive manufactured tissue engineering scaffolds, but gelatin's thermal instability has remained an ongoing challenge. The gelatin tends to suddenly collapse at mild temperatures, which is a significant limitation for using it at physiological temperature of 37 °C. Hence, fabrication of a thermo-processable gelatin hydrogel adapted for extrusion-based additive manufacturing is still a challenge. To achieve this, a self-healing nanocomposite double-network (ncDN) gelatin hydrogel was fabricated with high thermo-processability, shear-thinning, mechanical strength, self-healing, self-recovery, and biocompatibility. To do this, amino group-rich gelatin was first created by combining gelatin with carboxyl methyl chitosan. Afterwards, a self-healing ncDN gelatin hydrogel was formed via an in-situ formation of imine bonds between the blend of gelatin/carboxyl methyl chitosan (Gel/CMCh) and dialdehyde-functionalized bacterial nanocellulose (dBNC). dBNC plays as nanofiber cross-linkers capable of simultaneously crosslinking and reinforcing the double networks of Gel/CMCh through formation of dynamic 3D imine bonds. Based on our findings, our self-healing ncDA gelatin hydrogel displayed great potential as a promising ink for additive manufactured tissue engineering scaffolds.

Attention-assisted hybrid 1D CNN-BiLSTM model for predicting electric field induced by transcranial magnetic stimulation coil

Deep learning-based models such as deep neural network (DNN) and convolutional neural network (CNN) have recently been established as state-of-the-art for enumerating electric fields from transcranial magnetic stimulation coil. One of the main challenges related to this electric field enumeration is the prediction time and accuracy. Despite the low computational cost, the performance of the existing prediction models for electric field enumeration is quite inefficient. This study proposes a 1D CNN-based bi-directional long short-term memory (BiLSTM) model with an attention mechanism to predict electric field induced by a transcranial magnetic stimulation coil. The model employs three consecutive 1D CNN layers followed by the BiLSTM layer for extracting deep features. After that, the weights of the deep features are redistributed and integrated by the attention mechanism and a fully connected layer is utilized for the prediction. For the prediction purpose, six input features including coil turns of single wing, coil thickness, coil diameter, distance between two wings, distance between head and coil position, and angle between two wings of coil are mapped with the output of the electric field. The performance evaluation is conducted based on four verification metrics (e.g. R2, MSE, MAE, and RMSE) between the simulated data and predicted data. The results indicate that the proposed model outperforms existing DNN and CNN models in predicting the induced electrical field with R2 = 0.9992, MSE = 0.0005, MAE = 0.0188, and RMSE = 0.0228 in the testing stage.

Development of an integrated microperfusion-EEG electrode for unbiased multimodal sampling of brain interstitial fluid and concurrent neural activity

Objective. To modify off-the-shelf components to build a device for collecting electroencephalography (EEG) from macroelectrodes surrounded by large fluid access ports sampled by an integrated microperfusion system in order to establish a method for sampling brain interstitial fluid (ISF) at the site of stimulation or seizure activity with no bias for molecular size.Approach. Twenty-four 560µm diameter holes were ablated through the sheath surrounding one platinum-iridium macroelectrode of a standard Spencer depth electrode using a femtosecond UV laser. A syringe pump was converted to push-pull configuration and connected to the fluidics catheter of a commercially available microdialysis system. The fluidics were inserted into the lumen of the modified Spencer electrode with the microdialysis membrane removed, converting the system to open flow microperfusion. Electrical performance and analyte recovery were measured and parameters were systematically altered to improve performance. An optimized device was tested in the pig brain and unbiased quantitative mass spectrometry was used to characterize the perfusate collected from the peri-electrode brain in response to stimulation.Main results. Optimized parameters resulted in >70% recovery of 70 kDa dextran from a tissue analog. The optimized device was implanted in the cortex of a pig and perfusate was collected during four 60 min epochs. Following a baseline epoch, the macroelectrode surrounded by microperfusion ports was stimulated at 2 Hz (0.7 mA, 200µs pulse width). Following a post-stimulation epoch, the cortex near the electrode was stimulated with benzylpenicillin to induce epileptiform activity. Proteomic analysis of the perfusates revealed a unique inflammatory signature induced by electrical stimulation. This signature was not detected in bulk tissue ISF.Significance. A modified dual-sensing electrode that permits coincident detection of EEG and ISF at the site of epileptiform neural activity may reveal novel pathogenic mechanisms and therapeutic targets that are otherwise undetectable at the bulk tissue level.

High frequency deep brain stimulation can mitigate the acute effects of cocaine administration on tonic dopamine levels in the rat nucleus accumbens

Cocaine's addictive properties stem from its capacity to increase tonic extracellular dopamine levels in the nucleus accumbens (NAc). The ventral tegmental area (VTA) is a principal source of NAc dopamine. To investigate how high frequency stimulation (HFS) of the rodent VTA or nucleus accumbens core (NAcc) modulates the acute effects of cocaine administration on NAcc tonic dopamine levels multiple-cyclic square wave voltammetry (M-CSWV) was used. VTA HFS alone decreased NAcc tonic dopamine levels by 42%. NAcc HFS alone resulted in an initial decrease in tonic dopamine levels followed by a return to baseline. VTA or NAcc HFS following cocaine administration prevented the cocaine-induced increase in NAcc tonic dopamine. The present results suggest a possible underlying mechanism of NAc deep brain stimulation (DBS) in the treatment of substance use disorders (SUDs) and the possibility of treating SUD by abolishing dopamine release elicited by cocaine and other drugs of abuse by DBS in VTA, although further studies with chronic addiction models are required to confirm that. Furthermore, we demonstrated the use of M-CSWV can reliably measure tonic dopamine levels in vivo with both drug administration and DBS with minimal artifacts.

Starch-g-Acrylic Acid/Magnetic Nanochitin Self-Healing Ferrogels as Flexible Soft Strain Sensors

Mechanically robust ferrogels with high self-healing ability might change the design of soft materials used in strain sensing. Herein, a robust, stretchable, magneto-responsive, notch insensitive, ionic conductive nanochitin ferrogel was fabricated with both autonomous self-healing and needed resilience for strain sensing application without the need for additional irreversible static chemical crosslinks. For this purpose, ferric (III) chloride hexahydrate and ferrous (II) chloride as the iron source were initially co-precipitated to create magnetic nanochitin and the co-precipitation was confirmed by FTIR and microscopic images. After that, the ferrogels were fabricated by graft copolymerisation of acrylic acid-g-starch with a monomer/starch weight ratio of 1.5. Ammonium persulfate and magnetic nanochitin were employed as the initiator and crosslinking/nano-reinforcing agents, respectively. The ensuing magnetic nanochitin ferrogel provided not only the ability to measure strain in real-time under external magnetic actuation but also the ability to heal itself without any external stimulus. The ferrogel may also be used as a stylus for a touch-screen device. Based on our findings, our research has promising implications for the rational design of multifunctional hydrogels, which might be used in applications such as flexible and soft strain sensors, health monitoring, and soft robotics.

AI-Based Soft Module for Safe Human-Robot Interaction towards 4D Printing

Soft robotic modules have potential use for therapeutic and educational purposes. To do so, they need to be safe, soft, smart, and customizable to serve individuals' different preferences and personalities. A safe modular robotic product made of soft materials, particularly silicon, programmed by artificial intelligence algorithms and developed via additive manufacturing would be promising. This study focuses on the safe tactile interaction between humans and robots by means of soft material characteristics for translating physical communication to auditory. The embedded vibratory sensors used to stimulate touch senses transmitted through soft materials are presented. The soft module was developed and verified successfully to react to three different patterns of human-robot contact, particularly users' touches, and then communicate the type of contact with sound. The study develops and verifies a model that can classify different tactile gestures via machine learning algorithms for safe human-robot physical interaction. The system accurately recognizes the gestures and shapes of three-dimensional (3D) printed soft modules. The gestures used for the experiment are the three most common, including slapping, squeezing, and tickling. The model builds on the concept of how safe human-robot physical interactions could help with cognitive and behavioral communication. In this context, the ability to measure, classify, and reflect the behavior of soft materials in robotic modules represents a prerequisite for endowing robotic materials in additive manufacturing for safe interaction with humans.

Deep Brain Stimulation for Addictive Disorders-Where Are We Now?

In the face of a global epidemic of drug addiction, neglecting to develop new effective therapies will perpetuate the staggering human and economic costs of substance use. This review aims to summarize and evaluate the preclinical and clinical studies of deep brain stimulation (DBS) as a novel therapy for refractory addiction, in hopes to engage and inform future research in this promising novel treatment avenue. An electronic database search (MEDLINE, EMBASE, Cochrane library) was performed using keywords and predefined inclusion criteria between 1974 and 6/18/2021 (registered on Open Science Registry). Selected articles were reviewed in full text and key details were summarized and analyzed to understand DBS' therapeutic potential and possible mechanisms of action. The search yielded 25 animal and 22 human studies. Animal studies showed that DBS of targets such as nucleus accumbens (NAc), insula, and subthalamic nucleus reduces drug use and seeking. All human studies were case series/reports (level 4/5 evidence), mostly targeting the NAc with generally positive outcomes. From the limited evidence in the literature, DBS, particularly of the NAc, appears to be a reasonable last resort option for refractory addictive disorders. We propose that future research in objective electrophysiological (e.g., local field potentials) and neurochemical (e.g., extracellular dopamine levels) biomarkers would assist monitoring the progress of treatment and developing a closed-loop DBS system. Preclinical literature also highlighted the prefrontal cortex as a promising DBS target, which should be explored in human research.

Resource-constrained FPGA implementation of YOLOv2

Progress is being made to deploy convolutional neural networks (CNNs) into the Internet of Things (IoT) edge devices for handling image analysis tasks locally. These tasks require low-latency and low-power computation on low-resource IoT edge devices. However, CNN-based algorithms, e.g. YOLOv2, typically contain millions of parameters. With the increase in the CNN’s depth, filters are increased by a power of two. A large number of filters and operations could lead to frequent off-chip memory access that affects the operation speed and power consumption of the device. Therefore, it is a challenge to map a deep CNN into a low-resource edge IoT platform. To address this challenge, we present a resource-constrained Field-Programmable Gate Array implementation of YOLOv2 with optimized data transfer and computing efficiency. Firstly, a scalable cross-layer dataflow strategy is proposed which allows on-chip data transfer between different types of layers, and offers flexible off-chip data transfer when the intermediate results are unaffordable on-chip. Next, a filter-level data-reuse dataflow strategy together with a filter-level parallel multiply-accumulate operation computing processing elements array is developed. Finally, multi-level sliding buffers are developed to optimize the convolutional computing loop and reuse the input feature maps and weights. Experiment results show that our implementation has achieved 4.8 W of low-power consumption for executing YOLOv2, an 8-bit deep CNN containing 50.6 MB weights, using low-resource of 8.3 Mbits on-chip memory. The throughput and power efficiency are 100.33 GOP/s and 20.90 GOP/s/W, respectively.

Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks

Artificial neural networks (ANNs) have experienced a rapid advancement for their success in various application domains, including autonomous driving and drone vision. Researchers have been improving the performance efficiency and computational requirement of ANNs inspired by the mechanisms of the biological brain. Spiking neural networks (SNNs) provide a power-efficient and brain-inspired computing paradigm for machine learning applications. However, evaluating large-scale SNNs on classical von Neumann architectures (central processing units/graphics processing units) demands a high amount of power and time. Therefore, hardware designers have developed neuromorphic platforms to execute SNNs in and approach that combines fast processing and low power consumption. Recently, field-programmable gate arrays (FPGAs) have been considered promising candidates for implementing neuromorphic solutions due to their varied advantages, such as higher flexibility, shorter design, and excellent stability. This review aims to describe recent advances in SNNs and the neuromorphic hardware platforms (digital, analog, hybrid, and FPGA based) suitable for their implementation. We present that biological background of SNN learning, such as neuron models and information encoding techniques, followed by a categorization of SNN training. In addition, we describe state-of-the-art SNN simulators. Furthermore, we review and present FPGA-based hardware implementation of SNNs. Finally, we discuss some future directions for research in this field.

Cocaine increases stimulation-evoked serotonin efflux in the nucleus accumbens

Although dopamine is the most implicated neurotransmitter in the mediation of the pathophysiology of addiction, animal studies show serotonin also plays a vital role. Cocaine is one of the most common illicit drugs globally, but the role of serotonin in its mechanism of action is insufficiently characterized. Consequently, we investigated the acute effects of the psychomotor stimulant cocaine on electrical stimulation-evoked serotonin (phasic) release in the nucleus accumbens core (NAcc) of urethane-anesthetized (1.5 g/kg ip) male Sprague-Dawley rats using N-shaped fast-scan cyclic voltammetry (N-FSCV). A single carbon fiber microelectrode was first implanted in the NAcc. Stimulation was applied to the medial forebrain bundle using 60 Hz, 2 ms, 0.2 mA, 2-s biphasic pulses before and after cocaine (2 mg/kg iv) was administered. Stimulation-evoked serotonin release significantly increased 5 min after cocaine injection compared with baseline (153 ± 21 nM vs. 257 ± 12 nM; P = 0.0042; n = 5) but was unaffected by saline injection (1 mL/kg iv; n = 5). N-FSCV's selective measurement of serotonin release in vivo was confirmed pharmacologically via administration of the selective serotonin reuptake inhibitor escitalopram (10 mg/kg ip) that effectively increased the signal in a separate group of rats (n = 5). Selectivity to serotonin was further confirmed in vitro in which dopamine was minimally detected by N-FSCV with a serotonin to dopamine response ratio of 1:0.04 (200 nM of serotonin:1 µM dopamine ratio; P = 0.0048; n = 5 electrodes). This study demonstrates a noteworthy influence of cocaine on serotonin dynamics, and confirms that N-FSCV can effectively and selectively measure phasic serotonin release in the NAcc.NEW & NOTEWORTHY Serotonin plays a vital role in drug addiction. Here, using N-shaped fast-scan cyclic voltammetry, we demonstrated the effect of cocaine on the phasic release of serotonin at the nucleus accumbens core. To the best of our knowledge, this has not previously been elucidated. Our results not only reinforce the role of serotonin in the mechanism of action of cocaine but also help to fill a gap in our knowledge and provide a baseline for future studies in cocaine addiction.

Automatic Target Detection from Satellite Imagery Using Machine Learning

Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 × 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

Biomarkers for Deep Brain Stimulation in Animal Models of Depression

OBJECTIVES

Despite recent advances in depression treatment, many patients still do not respond to serial conventional therapies and are considered "treatment resistant." Deep brain stimulation (DBS) has therapeutic potential in this context. This comprehensive review of recent studies of DBS for depression in animal models identifies potential biomarkers for improving therapeutic efficacy and predictability of conventional DBS to aid future development of closed-loop control of DBS systems.

MATERIALS AND METHODS

A systematic search was performed in Pubmed, EMBASE, and Cochrane Review using relevant keywords. Overall, 56 animal studies satisfied the inclusion criteria.

RESULTS

Outcomes were divided into biochemical/physiological, electrophysiological, and behavioral categories. Promising biomarkers include biochemical assays (in particular, microdialysis and electrochemical measurements), which provide real-time results in awake animals. Electrophysiological tests, showing changes at both the target site and downstream structures, also revealed characteristic changes at several anatomic targets (such as the medial prefrontal cortex and locus coeruleus). However, the substantial range of models and DBS targets limits the ability to draw generalizable conclusions in animal behavioral models.

CONCLUSIONS

Overall, DBS is a promising therapeutic modality for treatment-resistant depression. Different outcomes have been used to assess its efficacy in animal studies. From the review, electrophysiological and biochemical markers appear to offer the greatest potential as biomarkers for depression. However, to develop closed-loop DBS for depression, additional preclinical and clinical studies with a focus on identifying reliable, safe, and effective biomarkers are warranted.

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

Electrophysiology-based Closed Loop Optogenetic Brain Stimulation Devices: Recent Developments and Future Prospects

Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.

Neural tissue-microelectrode interaction: Brain micromotion, electrical impedance, and flexible microelectrode insertion

Insertion of a microelectrode into the brain to record/stimulate neurons damages neural tissue and blood vessels and initiates the brain's wound healing response. Due to the large difference between the stiffness of neural tissue and microelectrode, brain micromotion also leads to neural tissue damage and associated local immune response. Over time, following implantation, the brain's response to the tissue damage can result in microelectrode failure. Reducing the microelectrode's cross-sectional dimensions to single-digit microns or using soft materials with elastic modulus close to that of the neural tissue are effective methods to alleviate the neural tissue damage and enhance microelectrode longevity. However, the increase in electrical impedance of the microelectrode caused by reducing the microelectrode contact site's dimensions can decrease the signal-to-noise ratio. Most importantly, the reduced dimensions also lead to a reduction in the critical buckling force, which increases the microelectrode's propensity to buckling during insertion. After discussing brain micromotion, the main source of neural tissue damage, surface modification of the microelectrode contact site is reviewed as a key method for addressing the increase in electrical impedance issue. The review then focuses on recent approaches to aiding insertion of flexible microelectrodes into the brain, including bending stiffness modification, effective length reduction, and application of a magnetic field to pull the electrode. An understanding of the advantages and drawbacks of the developed strategies offers a guide for dealing with the buckling phenomenon during implantation.

Quad-Band Rectenna for Ambient Radio Frequency (RF) Energy Harvesting

RF power is broadly available in both urban and semi-urban areas and thus exhibits as a promising candidate for ambient energy scavenging sources. In this research, a high-efficiency quad-band rectenna is designed for ambient RF wireless energy scavenging over the frequency range from 0.8 to 2.5 GHz. Firstly, the detailed characteristics (i.e., available frequency bands and associated power density levels) of the ambient RF power are studied and analyzed. The data (i.e., RF survey results) are then applied to aid the design of a new quad-band RF harvester. A newly designed impedance matching network (IMN) with an additional L-network in a third-branch of dual-port rectifier circuit is familiarized to increase the performance and RF-to-DC conversion efficiency of the harvester with comparatively very low input RF power density levels. A dual-polarized multi-frequency bow-tie antenna is designed, which has a wide bandwidth (BW) and is miniature in size. The dual cross planer structure internal triangular shape and co-axial feeding are used to decrease the size and enhance the antenna performance. Consequently, the suggested RF harvester is designed to cover all available frequency bands, including part of most mobile phone and wireless local area network (WLAN) bands in Malaysia, while the optimum resistance value for maximum dc rectification efficiency (up to 48%) is from 1 to 10 kΩ. The measurement result in the ambient environment (i.e., both indoor and outdoor) depicts that the new harvester is able to harvest dc voltage of 124.3 and 191.0 mV, respectively, which can be used for low power sensors and wireless applications.

Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation

The smart grid (SG) is a contemporary electrical network that enhances the network's performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.

Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Defective PV panels reduce the efficiency of the whole PV string, causing loss of investment by decreasing its efficiency and lifetime. In this study, firstly, an isolated convolution neural model (ICNM) was prepared from scratch to classify the infrared images of PV panels based on their health, i.e., healthy, hotspot, and faulty. The ICNM occupies the least memory, and it also has the simplest architecture, lowest execution time, and an accuracy of 96% compared to transfer learned pre-trained ShuffleNet, GoogleNet, and SqueezeNet models. Afterward, ICNM, based on its advantages, is reused through transfer learning to classify the defects of PV panels into five classes, i.e., bird drop, single, patchwork, horizontally aligned string, and block with 97.62% testing accuracy. This proposed approach can identify and classify the PV panels based on their health and defects faster with high accuracy and occupies the least amount of the system's memory, resulting in savings in the PV investment.

Mood Regulatory Actions of Active and Sham Nucleus Accumbens Deep Brain Stimulation in Antidepressant Resistant Rats

The antidepressant actions of deep brain stimulation (DBS) are associated with progressive neuroadaptations within the mood network, modulated in part, by neurotrophic mechanisms. We investigated the antidepressant-like effects of chronic nucleus accumbens (NAc) DBS and its association with change in glycogen synthase kinase 3 (GSK3) and mammalian target of rapamycin (mTOR) expression in the infralimbic cortex (IL), and the dorsal (dHIP) and ventral (vHIP) subregions of the hippocampus of antidepressant resistant rats. Antidepressant resistance was induced via daily injection of adrenocorticotropic hormone (ACTH; 100 μg/day; 15 days) and confirmed by non-response to tricyclic antidepressant treatment (imipramine, 10 mg/kg). Portable microdevices provided continuous bilateral NAc DBS (130 Hz, 200 μA, 90 μs) for 7 days. A control sham electrode group was included, together with ACTH- and saline-treated control groups. Home cage monitoring, open field, sucrose preference, and, forced swim behavioral tests were performed. Post-mortem levels of GSK3 and mTOR, total and phosphorylated, were determined with Western blot. As previously reported, ACTH treatment blocked the immobility-reducing effects of imipramine in the forced swim test. In contrast, treatment with either active DBS or sham electrode placement in the NAc significantly reduced forced swim immobility time in ACTH-treated animals. This was associated with increased homecage activity in the DBS and sham groups relative to ACTH and saline groups, however, no differences in locomotor activity were observed in the open field test, nor were any group differences seen for sucrose consumption across groups. The antidepressant-like actions of NAc DBS and sham electrode placements were associated with an increase in levels of IL and vHIP phospho-GSK3β and phospho-mTOR, however, no differences in these protein levels were observed in the dHIP region. These data suggest that early response to electrode placement in the NAc, irrespective of whether active DBS or sham, has antidepressant-like effects in the ACTH-model of antidepressant resistance associated with distal upregulation of phospho-GSK3β and phospho-mTOR in the IL and vHIP regions of the mood network.

Cocaine-Induced Changes in Tonic Dopamine Concentrations Measured Using Multiple-Cyclic Square Wave Voltammetry in vivo

For over 40 years, in vivo microdialysis techniques have been at the forefront in measuring the effects of illicit substances on brain tonic extracellular levels of dopamine that underlie many aspects of drug addiction. However, the size of microdialysis probes and sampling rate may limit this technique’s ability to provide an accurate assessment of drug effects in microneural environments. A novel electrochemical method known as multiple-cyclic square wave voltammetry (M-CSWV), was recently developed to measure second-to-second changes in tonic dopamine levels at microelectrodes, providing spatiotemporal resolution superior to microdialysis. Here, we utilized M-CSWV and fast-scan cyclic voltammetry (FSCV) to measure changes in tonic or phasic dopamine release in the nucleus accumbens core (NAcc) after acute cocaine administration. Carbon-fiber microelectrodes (CFM) and stimulating electrodes were implanted into the NAcc and medial forebrain bundle (MFB) of urethane anesthetized (1.5 g/kg i.p.) Sprague-Dawley rats, respectively. Using FSCV, depths of each electrode were optimized by determining maximal MFB electrical stimulation-evoked phasic dopamine release. Changes in phasic responses were measured after a single dose of intravenous saline or cocaine hydrochloride (3 mg/kg; n = 4). In a separate group, changes in tonic dopamine levels were measured using M-CSWV after intravenous saline and after cocaine hydrochloride (3 mg/kg; n = 5). Both the phasic and tonic dopamine responses in the NAcc were augmented by the injection of cocaine compared to saline control. The phasic and tonic levels changed by approximately x2.4 and x1.9, respectively. These increases were largely consistent with previous studies using FSCV and microdialysis. However, the minimal disruption/disturbance of neuronal tissue by the CFM may explain why the baseline tonic dopamine values (134 ± 32 nM) measured by M-CSWV were found to be 10-fold higher when compared to conventional microdialysis. In this study, we demonstrated phasic dopamine dynamics in the NAcc with acute cocaine administration. M-CSWV was able to record rapid changes in tonic levels of dopamine, which cannot be achieved with other current voltammetric techniques. Taken together, M-CSWV has the potential to provide an unprecedented level of physiologic insight into dopamine signaling, both in vitro and in vivo, which will significantly enhance our understanding of neurochemical mechanisms underlying psychiatric conditions.

Dynamic nanocellulose hydrogels: Recent advancements and future outlook

Nanocellulose is of great interest in material science nowadays mainly because of its hydrophilic, renewable, biodegradable, and biocompatible nature, as well as its excellent mechanical strength and tailorable surface ready for modification. Currently, nanocellulose is attracting attention to overcome the current challenges of dynamic hydrogels: robustness, autonomous self-healing, and self-recovery (SELF) properties simultaneously occurring in one system. In this regard, this review aims to explore current advances in design and fabrication of dynamic nanocellulose hydrogels and elucidate how incorporating nanocellulose with dynamic motifs simultaneously improves both SELF and robustness of hydrogels. Finally, current challenges and prospects of dynamic nanocellulose hydrogels are discussed.

DeepNavNet: Automated Landmark Localization for Neuronavigation

Functional neurosurgery requires neuroimaging technologies that enable precise navigation to targeted structures. Insufficient image resolution of deep brain structures necessitates alignment to a brain atlas to indirectly locate targets within preoperative magnetic resonance imaging (MRI) scans. Indirect targeting through atlas-image registration is innately imprecise, increases preoperative planning time, and requires manual identification of anterior and posterior commissure (AC and PC) reference landmarks which is subject to human error. As such, we created a deep learning-based pipeline that consistently and automatically locates, with submillimeter accuracy, the AC and PC anatomical landmarks within MRI volumes without the need for an atlas. Our novel deep learning pipeline (DeepNavNet) regresses from MRI scans to heatmap volumes centered on AC and PC anatomical landmarks to extract their three-dimensional coordinates with submillimeter accuracy. We collated and manually labeled the location of AC and PC points in 1128 publicly available MRI volumes used for training, validation, and inference experiments. Instantiations of our DeepNavNet architecture, as well as a baseline model for reference, were evaluated based on the average 3D localization errors for the AC and PC points across 311 MRI volumes. Our DeepNavNet model significantly outperformed a baseline and achieved a mean 3D localization error of 0.79 ± 0.33 mm and 0.78 ± 0.33 mm between the ground truth and the detected AC and PC points, respectively. In conclusion, the DeepNavNet model pipeline provides submillimeter accuracy for localizing AC and PC anatomical landmarks in MRI volumes, enabling improved surgical efficiency and accuracy.

An FSCV Deep Neural Network: Development, Pruning, and Acceleration on an FPGA

Fast-scan cyclic voltammetry (FSCV) is an electrochemical technique for measuring rapid changes in the extracellular concentration of neurotransmitters within the brain. Due to its fast scan rate and large output-data size, the current analysis of the FSCV data is often conducted on a computer external to the FSCV device. Moreover, the analysis is semi-automated and requires a good understanding of the characteristics of the underlying chemistry to interpret, making it unsuitable for real-time implementation on low-resource FSCV devices. This paper presents a hardware-software co-design approach for the analysis of FSCV data. Firstly, a deep neural network (DNN) is developed to predict the concentration of a dopamine solution and identify the data recording electrode. Secondly, the DNN is pruned to decrease its computation complexity, and a custom overlay is developed to implement the pruned DNN on a low-resource FPGA-based platform. The pruned DNN attains a recognition accuracy of 97.2% with a compression ratio of 3.18. When the DNN overlay is implemented on a PYNQ-Z2 platform, it achieves the execution time of 13 ms and power consumption of 1.479 W on the entire PYNQ-Z2 board. This study demonstrates the possibility of operating the DNN for FSCV data analysis on portable FPGA-based platforms.

Wearable Peripheral Electrical Stimulation Devices for the Reduction of Essential Tremor: A Review

Essential tremor is the most common pathological tremor, with a prevalence of 6.3% in people over 65 years of age. This disorder interferes with a patient's ability to carry out activities of daily living independently, and treatment with medical and surgical interventions is often insufficient or contraindicated. Mechanical orthoses have not been widely adopted by patients due to discomfort and lack of discretion. Over the past 30 years, peripheral electrical stimulation has been investigated as a possible treatment for patients who have not found other treatment options to be satisfactory, with wearable devices revolutionizing this emerging approach in recent years. In this paper, an overview of essential tremor and its current medical and surgical treatment options are presented. Following this, tremor detection, measurement and characterization methods are explored with a focus on the measurement options that can be incorporated into wearable devices. Then, novel interventions for essential tremor are described, with a detailed review of open and closed-loop peripheral electrical stimulation methods. Finally, discussion of the need for wearable closed-loop peripheral electrical stimulation devices for essential tremor, approaches in their implementation, and gaps in the literature for further research are presented.

Design and Numerical Analysis of a Graphene-Coated SPR Biosensor for Rapid Detection of the Novel Coronavirus

In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.

Resource-constrained FPGA/DNN co-design

Deep neural networks (DNNs) have demonstrated super performance in most learning tasks. However, a DNN typically contains a large number of parameters and operations, requiring a high-end processing platform for high-speed execution. To address this challenge, hardware-and-software co-design strategies, which involve joint DNN optimization and hardware implementation, can be applied. These strategies reduce the parameters and operations of the DNN, and fit it into a low-resource processing platform. In this paper, a DNN model is used for the analysis of the data captured using an electrochemical method to determine the concentration of a neurotransmitter and the recoding electrode. Next, a DNN miniaturization algorithm is introduced, involving combined pruning and compression, to reduce the DNN resource utilization. Here, the DNN is transformed to have sparse parameters by pruning a percentage of its weights. The Lempel-Ziv-Welch algorithm is then applied to compress the sparse DNN. Next, a DNN overlay is developed, combining the decompression of the DNN parameters and DNN inference, to allow the execution of the DNN on a FPGA on the PYNQ-Z2 board. This approach helps avoid the need for inclusion of a complex quantization algorithm. It compresses the DNN by a factor of 6.18, leading to about 50% reduction in the resource utilization on the FPGA.

Comparative accuracy testing of non-contact infrared thermometers and temporal artery thermometers in an adult hospital setting

Background

NCIT are non-invasive devices for fever screening in children. However, evidence of their accuracy for fever screening in adults is lacking. This study aimed to compare the accuracy of non-contact infrared thermometers (NCIT) with temporal artery thermometers (TAT) in an adult hospital.

Methods

A prospective observational study was conducted on a convenience sample of non-infectious inpatients in 2 Australian hospitals. NCIT and TAT devices were used to collect body temperature recordings. Participant characteristics included age, gender, skin color, highest temperature, and antipyretic medications recorded in last 24-hour.

Results

In 265 patients, a mean difference of ± 0.26°C was recorded between the NCIT (36.64°C) and the reference TAT (36.90°C) temperature devices. Bland-Altman analysis showed that NCIT and TAT temperatures were closely aligned at temperatures <37.5°C, but not at temperatures >37.5°C. NCIT had low sensitivity (16.13%) at temperatures ≥37.5°C. An AUROC score of 0.67 (SD 0.05) demonstrated poor accuracy of the NCIT device at temperatures ≥37.5°C.

Conclusion

This is the first study to compare accuracy of NCIT thermometers to TAT in adult patients. Although mass fever screening is currently underway using NCIT, these results indicate that the NCIT may not be the most accurate device for fever mass screening during a pandemic.

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

Multi-disease Deep Brain Stimulation

Current closed-loop deep brain stimulation (DBS) devices can generally tackle one disorder. This paper presents the design and evaluation of a multi-disease closed-loop DBS device that can sense multiple brain biomarkers, detect a disorder, and adaptively deliver electrical stimulation pulses based on the disease state. The device consists of: (i) a neural sensor, (ii) a controller involving a feature extractor, a disease classifier, and a control strategy, and (iii) neural stimulator. The neural sensor records and processes local field potentials and spikes from within the brain using two low-frequency and high-frequency channels. The feature extractor digitally processes the output of the neural sensor, and extracts five potential biomarkers: alpha, beta, slow gamma, high-frequency oscillations, and spikes. The disease classifier identifies the type of the neurological disorder through an analysis of the biomarkers' amplitude features. The control strategy considers the disease state and supplies the stimulation settings to the neural stimulator. Both the disease classifier and control strategy are based on fuzzy algorithms. The neural stimulator generates electrical stimulation pulses according to the control commands, and delivers them to the target area of the brain. The device can generate current stimulation pulses with specific amplitude, frequency, and duration. The fabricated device has the maximum radius of 15 mm. Its total weight including the circuit board, battery and battery holder is 5.1 g. The performance of the integrated device has been evaluated through six bench and in-vitro experiments. The experimental results are presented, analyzed, and discussed. Six bench and in-vitro experiments were conducted using sinusoidal, normal pre-recorded, and diseased neural signals representing normal, epilepsy, depression and PD conditions. The results obtained through these tests indicate the successful neural sensing, classification, control, and neural stimulating performance.

Rational Design of Mussel-Inspired Hydrogels with Dynamic Catecholato-Metal Coordination Bonds

Nature has often been the main source of inspiration for designing smart functional materials. As an example, mussels can attach to almost any wet surfaces, for example, wood, rocks, metal, etc., due to the presence of catechols containing amino acid 3,4-dihydroxyphenyl-l-alanine (DOPA). Fabrication of mussel-inspired hydrogels using dynamic catecholato-metal coordination bonds has recently been in the limelight because of the hydrogels' ease of gelation, interesting self-healing, self-recovery, adhesiveness, and pH-responsiveness, as well as shear-thinning and mechanical properties. Mussel inspired hydrogels take advantage of catechols, for example, DOPA in the blue mussel, to undergo catecholatometal gelation through coordination chemistry. This review explores the latest developments in the fabrication of such hydrogels using catecholato-metal coordination bonds, and discusses their potential applications in sensors, flexible electronics, tissue engineering, and wound dressing. Moreover, current challenges and prospects of such hydrogels are discussed. The main focus of this paper is on providing a deeper understanding of this growing field in terms of chemistry, physics, and associated properties.

Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives

Advancements in materials science and fabrication techniques have contributed to the significant growing attention to a wide variety of sensors for digital healthcare. While the progress in this area is tremendously impressive, few wearable sensors with the capability of real-time blood pressure monitoring are approved for clinical use. One of the key obstacles in the further development of wearable sensors for medical applications is the lack of comprehensive technical evaluation of sensor materials against the expected clinical performance. Here, we present an extensive review and critical analysis of various materials applied in the design and fabrication of wearable sensors. In our unique transdisciplinary approach, we studied the fundamentals of blood pressure and examined its measuring modalities while focusing on their clinical use and sensing principles to identify material functionalities. Then, we carefully reviewed various categories of functional materials utilized in sensor building blocks allowing for comparative analysis of the performance of a wide range of materials throughout the sensor operational-life cycle. Not only this provides essential data to enhance the materials' properties and optimize their performance, but also, it highlights new perspectives and provides suggestions to develop the next generation pressure sensors for clinical use.

Multi-predator assemblages, dive type, bathymetry and sex influence foraging success and efficiency in African penguins

Marine predators adapt their hunting techniques to locate and capture prey in response to their surrounding environment. However, little is known about how certain strategies influence foraging success and efficiency. Due to the miniaturisation of animal tracking technologies, a single individual can be equipped with multiple data loggers to obtain multi-scale tracking information. With the addition of animal-borne video data loggers, it is possible to provide context-specific information for movement data obtained over the video recording periods. Through a combination of video data loggers, accelerometers, GPS and depth recorders, this study investigated the influence of habitat, sex and the presence of other predators on the foraging success and efficiency of the endangered African penguin, Spheniscus demersus, from two colonies in Algoa Bay, South Africa. Due to limitations in the battery life of video data loggers, a machine learning model was developed to detect prey captures across full foraging trips. The model was validated using prey capture signals detected in concurrently recording accelerometers and animal-borne cameras and was then applied to detect prey captures throughout the full foraging trip of each individual. Using GPS and bathymetry information to inform the position of dives, individuals were observed to perform both pelagic and benthic diving behaviour. Females were generally more successful on pelagic dives than males, suggesting a trade-off between manoeuvrability and physiological diving capacity. By contrast, males were more successful in benthic dives, at least for Bird Island (BI) birds, possibly due to their larger size compared to females, allowing them to exploit habitat deeper and for longer durations. Both males at BI and both sexes at St Croix (SC) exhibited similar benthic success rates. This may be due to the comparatively shallower seafloor around SC, which could increase the likelihood of females capturing prey on benthic dives. Observation of camera data indicated individuals regularly foraged with a range of other predators including penguins and other seabirds, predatory fish (sharks and tuna) and whales. The presence of other seabirds increased individual foraging success, while predatory fish reduced it, indicating competitive exclusion by larger heterospecifics. This study highlights novel benthic foraging strategies in African penguins and suggests that individuals could buffer the effects of changes to prey availability in response to climate change. Furthermore, although group foraging was prevalent in the present study, its influence on foraging success depends largely on the type of heterospecifics present.

Dynamic Mussel-Inspired Chitin Nanocomposite Hydrogels for Wearable Strain Sensors

It is an ongoing challenge to fabricate an electroconductive and tough hydrogel with autonomous self-healing and self-recovery (SELF) for wearable strain sensors. Current electroconductive hydrogels often show a trade-off between static crosslinks for mechanical strength and dynamic crosslinks for SELF properties. In this work, a facile procedure was developed to synthesize a dynamic electroconductive hydrogel with excellent SELF and mechanical properties from starch/polyacrylic acid (St/PAA) by simply loading ferric ions (Fe3+) and tannic acid-coated chitin nanofibers (TA-ChNFs) into the hydrogel network. Based on our findings, the highest toughness was observed for the 1 wt.% TA-ChNF-reinforced hydrogel (1.43 MJ/m3), which is 10.5-fold higher than the unreinforced counterpart. Moreover, the 1 wt.% TA-ChNF-reinforced hydrogel showed the highest resistance against crack propagation and a 96.5% healing efficiency after 40 min. Therefore, it was chosen as the optimized hydrogel to pursue the remaining experiments. Due to its unique SELF performance, network stability, superior mechanical, and self-adhesiveness properties, this hydrogel demonstrates potential for applications in self-wearable strain sensors.

A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient's chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

Effects of Topology Optimization in Multimaterial 3D Bioprinting of Soft Actuators

Recently, there has been a proliferation of soft robots and actuators that exhibit improved capabilities and adaptability through three-dimensional (3D) bioprinting. Flexibility and shape recovery attributes of stimuli-responsive polymers as the main components in the production of these dynamic structures enable soft manipulations in fragile environments, with potential applications in biomedical and food sectors. Topology optimization (TO), when used in conjunction with 3D bioprinting with optimal design features, offers new capabilities for efficient performance in compliant mechanisms. In this paper, multimaterial TO analysis is used to improve and control the bending performance of a bioprinted soft actuator with electrolytic stimulation. The multimaterial actuator performance is evaluated by the amplitude and rate of bending motion and compared with the single material printed actuator. The results demonstrated the efficacy of multimaterial 3D bioprinting optimization for the rate of actuation and bending.

Effects of Topology Optimization in Multimaterial 3D Bioprinting of Soft Actuators

Recently, there has been a proliferation of soft robots and actuators that exhibit improved capabilities and adaptability through three-dimensional (3D) bioprinting. Flexibility and shape recovery attributes of stimuli-responsive polymers as the main components in the production of these dynamic structures enable soft manipulations in fragile environments, with potential applications in biomedical and food sectors. Topology optimization (TO), when used in conjunction with 3D bioprinting with optimal design features, offers new capabilities for efficient performance in compliant mechanisms. In this paper, multimaterial TO analysis is used to improve and control the bending performance of a bioprinted soft actuator with electrolytic stimulation. The multimaterial actuator performance is evaluated by the amplitude and rate of bending motion and compared with the single material printed actuator. The results demonstrated the efficacy of multimaterial 3D bioprinting optimization for the rate of actuation and bending.