High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing.

Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.

Ion-Mediated Recombination Dynamics in Perovskite-Based Memory Light-Emitting Diodes for Neuromorphic Control Systems.

Neuromorphic devices can help perform memory-heavy tasks more efficiently due to the co-localization of memory and computing. In biological systems, fast dynamics are necessary for rapid communication, while slow dynamics aid in the amplification of signals over noise and regulatory processes such as adaptation- such dual dynamics are key for neuromorphic control systems. Halide perovskites exhibit much more complex phenomena than conventional semiconductors due to their coupled ionic, electronic, and optical properties which result in modulatable drift, diffusion of ions, carriers, and radiative recombination dynamics. This is exploited to engineer a dual-emitter tandem device with the requisite dual slow-fast dynamics. Here, a perovskite-organic tandem light-emitting diode (LED) capable of modulating its emission spectrum and intensity owing to the ion-mediated recombination zone modulation between the green-emitting quasi-2D perovskite layer and the red-emitting organic layer is introduced. Frequency-dependent response and high dynamic range memory of emission intensity and spectra in a LED are demonstrated. Utilizing the emissive read-out, image contrast enhancement as a neuromorphic pre-processing step to improve pattern recognition capabilities is illustrated. As proof of concept using the device's slow-fast dynamics, an inhibition of the return mechanism is physically emulated.

Telehealth as a Component of One Health: a Position Paper.

INTRODUCTION: One Health (OH) refers to the integration of human, animal, and ecosystem health within one framework in the context of zoonoses, antimicrobial resistance and stewardship, and food security. Telehealth refers to distance delivery of healthcare. A systems approach is central to both One Health and telehealth, and telehealth can be a core component of One Health. Here we explain how telehealth might be integrated into One Health. METHODS: We have considered antimicrobial resistance (AMR) as a use case where both One Health and telehealth can be used for coordination among the farming sector, the veterinary services, and human health providers to mitigate the risk of AMR. We conducted a narrative review of the literature to develop a position on the inter-relationships between telehealth and One Health. We have summarised how telehealth can be incorporated within One Health. RESULTS: Clinicians have used telehealth to address antimicrobial resistance, zoonoses, food borne infection, improvement of food security and antimicrobial stewardship. We identified little existing evidence in support of the usage of telehealth within a One Health paradigm, although in isolation, both are useful for the same purpose, i.e., mitigation of the significant public health risks posed by zoonoses, food borne infections, and antimicrobial resistance. CONCLUSIONS: It is possible to integrate telehealth within a One Health framework to develop effective inter-sectoral communication essential for the mitigation and addressing of zoonoses, food security, food borne infection containment and antimicrobial stewardship. More research is needed to substantiate and investigate this model of healthcare.

A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-Time Systems.

Deep learning inference that needs to largely take place on the "edge" is a highly computational and memory intensive workload, making it intractable for low-power, embedded platforms such as mobile nodes and remote security applications. To address this challenge, this article proposes a real-time, hybrid neuromorphic framework for object tracking and classification using event-based cameras that possess desirable properties such as low-power consumption (5-14 mW) and high dynamic range (120 dB). Nonetheless, unlike traditional approaches of using event-by-event processing, this work uses a mixed frame and event approach to get energy savings with high performance. Using a frame-based region proposal method based on the density of foreground events, a hardware-friendly object tracking scheme is implemented using the apparent object velocity while tackling occlusion scenarios. The frame-based object track input is converted back to spikes for TrueNorth (TN) classification via the energy-efficient deep network (EEDN) pipeline. Using originally collected datasets, we train the TN model on the hardware track outputs, instead of using ground truth object locations as commonly done, and demonstrate the ability of our system to handle practical surveillance scenarios. As an alternative tracker paradigm, we also propose a continuous-time tracker with C ++ implementation where each event is processed individually, which better exploits the low latency and asynchronous nature of neuromorphic vision sensors. Subsequently, we extensively compare the proposed methodologies to state-of-the-art event-based and frame-based methods for object tracking and classification, and demonstrate the use case of our neuromorphic approach for real-time and embedded applications without sacrificing performance. Finally, we also showcase the efficacy of the proposed neuromorphic system to a standard RGB camera setup when simultaneously evaluated over several hours of traffic recordings.

Trajectories of Successful Aging Among Older Adults in Aotearoa New Zealand.

Successful aging was defined as having no multimorbidity, high functional capacity, active life engagement, and good health-related quality of life. This study analyzed data from 1433 older adults who were followed up for 12 years across seven waves from the New Zealand Health, Work and Retirement study by examining the trajectories of successful aging. Latent growth curve modeling was used to assess the growth factors of successful aging trajectories of older adults. The mean successful aging score was 3.53 (range: 0-6) in 2006 and linearly declined by 0.064 units every year. Those with higher successful aging scores at baseline had a slower decline. Successful aging scores were lower among females, Maori, and those aged 65 years and above at baseline. The findings from this study suggest that gender and ethnic inequalities play significant roles in successful aging among older adults in New Zealand.

A cross-tabulated analysis for the influence of climate conditions on the incidence of dengue fever in Jeddah City, Saudi Arabia during 2006-2009.

Objective: Increased temperature and humidity across the world and emergence of mosquito-borne diseases, notably dengue both continue to present public health problems, but their relationship is not clear as conflicting evidence abound on the association between climate conditions and risk of dengue fever. This characterization is important as mitigation of climate change-related variables will contribute toward efficient planning of health services. The purpose of this study was to determine whether humidity in addition to high temperatures increase the risk of dengue transmission. Methods: We have assessed the joint association between temperature and humidity with the incidence of dengue fever at Jeddah City in Saudi Arabia. We obtained weekly data from Jeddah City on temperature and humidity between 2006 and 2009 for 200 weeks starting week 1/2006 and ending week 53/2009. We also collected incident case data on dengue fever in Jeddah City. Results: The cross-tabulated analysis showed an association between temperature or humidity conditions and incident cases of dengue. Our data found that hot and dry conditions were associated with a high risk of dengue incidence in Jeddah City. Conclusion: Hot and dry conditions are risk factors for dengue fever.

Telehealth as a Means of Enabling Health Equity.

OBJECTIVE: The goal of this paper is to provide a consensus review on telehealth delivery prior to and during the COVID-19 pandemic to develop a set of recommendations for designing telehealth services and tools that contribute to system resilience and equitable health. METHODS: The IMIA-Telehealth Working Group (WG) members conducted a two-step approach to understand the role of telehealth in enabling global health equity. We first conducted a consensus review on the topic followed by a modified Delphi process to respond to four questions related to the role telehealth can play in developing a resilient and equitable health system. RESULTS: Fifteen WG members from eight countries participated in the Delphi process to share their views. The experts agreed that while telehealth services before and during COVID-19 pandemic have enhanced the delivery of and access to healthcare services, they were also concerned that global telehealth delivery has not been equal for everyone. The group came to a consensus that health system concepts including technology, financing, access to medical supplies and equipment, and governance capacity can all impact the delivery of telehealth services. CONCLUSION: Telehealth played a significant role in delivering healthcare services during the pandemic. However, telehealth delivery has also led to unintended consequences (UICs) including inequity issues and an increase in the digital divide. Telehealth practitioners, professionals and system designers therefore need to purposely design for equity as part of achieving broader health system goals.

Tobacco and ADHD: A Role of MAO-Inhibition in Nicotine Dependence and Alleviation of ADHD Symptoms.

Attention-deficit/hyperactivity disorder (ADHD) is a relatively commonly occurring neurodevelopmental disorder affecting approximately 5% of children and young people. The neurobiological mechanisms of ADHD are proposed to particularly center around increased dopamine receptor availability related to associated symptoms of reduced attention regulation and impulsivity. ADHD is also persistent across the lifespan and associated with a raft of impulsive and health-risk behaviors including substance abuse and smoking. Research highlighting the potentially significant levels of monoamine oxidase (MAO) inhibitory properties in tobacco smoke and e-cigarettes may provide a mechanism for increased tobacco smoke dependence among those with ADHD, in addition to the role of nicotine. Aim: This scoping review aimed to establish evidence for the above neurobiological pathway between smoking and ADHD symptom-alleviation or "self-medication" with the inclusion of the mechanism of MAO-inhibitors indirect increasing dopamine in the brain. Methodology: Scoping review methodologies were employed in this review selected to synthesize multiple sources of empirical research to identify current gaps in the knowledge base and identify key characteristics of research data related to a phenomenon. Databases searched included OVID MEDLINE(R), Embase, Cochrane, PsycINFO and SCOPUS limited to 2000 onward and empirically validated, peer-reviewed research. Findings: There is support for the role of MAO-inhibition on greater reinforcement of smoking for individuals with ADHD through a greater impact on dopaminergic availability than nicotine; potentially moderating ADHD symptoms. Conclusion: Greater support for a "self-medication" model of ADHD and smoking includes not only nicotine but also MAO-inhibitors as dopamine agonists contained in cigarettes and e-cigarettes.

Dendritic Computing: Branching Deeper into Machine Learning.

In this paper, we discuss the nonlinear computational power provided by dendrites in biological and artificial neurons. We start by briefly presenting biological evidence about the type of dendritic nonlinearities, respective plasticity rules and their effect on biological learning as assessed by computational models. Four major computational implications are identified as improved expressivity, more efficient use of resources, utilizing internal learning signals, and enabling continual learning. We then discuss examples of how dendritic computations have been used to solve real-world classification problems with performance reported on well known data sets used in machine learning. The works are categorized according to the three primary methods of plasticity used-structural plasticity, weight plasticity, or plasticity of synaptic delays. Finally, we show the recent trend of confluence between concepts of deep learning and dendritic computations and highlight some future research directions.

Physical Activity, Mental Health and Wellbeing during the First COVID-19 Containment in New Zealand: A Cross-Sectional Study.

Strategies implemented worldwide to contain COVID-19 outbreaks varied in severity across different countries, and established a new normal for work and school life (i.e., from home) for many people, reducing opportunities for physical activity. Positive relationships of physical activity with both mental and physical health are well recognised, and therefore the aim was to ascertain how New Zealand's lockdown restrictions impacted physical activity, mental health and wellbeing. Participants (n = 4007; mean ± SD: age 46.5 ± 14.7 years, 72% female, 80.7% New Zealand European) completed (10-26 April 2020) an online amalgamated survey (Qualtrics): International Physical Activity Questionnaire: Short Form; Depression, Anxiety and Stress Scale-9; World Health Organisation-Five Well-Being Index; Stages of Change Scale. Positive dose-response relationships between physical activity levels and wellbeing scores were demonstrated for estimates that were unadjusted (moderate activity OR 3.79, CI 2.88-4.92; high activity OR 8.04, CI 6.07-10.7) and adjusted (confounding variables: age, gender, socioeconomic status, time sitting and co-morbidities) (moderate activity 1.57, CI 1.11-2.52; high activity 2.85, CI 1.97-4.14). The study results support previous research demonstrating beneficial effects of regular physical activity on mental health and wellbeing. Governments may use these results to promote meeting physical activity guidelines in order to protect mental health and wellbeing during the ongoing COVID-19 restrictions and future pandemics.

Halide perovskite memristors as flexible and reconfigurable physical unclonable functions.

Physical Unclonable Functions (PUFs) address the inherent limitations of conventional hardware security solutions in edge-computing devices. Despite impressive demonstrations with silicon circuits and crossbars of oxide memristors, realizing efficient roots of trust for resource-constrained hardware remains a significant challenge. Hybrid organic electronic materials with a rich reservoir of exotic switching physics offer an attractive, inexpensive alternative to design efficient cryptographic hardware, but have not been investigated till date. Here, we report a breakthrough security primitive exploiting the switching physics of one dimensional halide perovskite memristors as excellent sources of entropy for secure key generation and device authentication. Measurements of a prototypical 1 kb propyl pyridinium lead iodide (PrPyr[PbI3]) weak memristor PUF with a differential write-back strategy reveals near ideal uniformity, uniqueness and reliability without additional area and power overheads. Cycle-to-cycle write variability enables reconfigurability, while in-memory computing empowers a strong recurrent PUF construction to thwart machine learning attacks.

Improving the prescribing practice of junior doctors through interprofessional collaboration and evidence-based education.

The last decade (2010-2019) has seen calls to action to improve the prescribing practice of junior doctors. An in-depth investigation into the causes of prescribing errors by foundation trainees in relation to their medical education (the EQUIP study) in the UK reported a prescription error rate of 8.9% for all prescribed medicines, and although that is a UK study, there are similarities with New Zealand prevocational training programmes. The EQUIP study revealed that existing teaching strategies are not working. To believe a single intervention will prevent most prescribing errors is simplistic, and for improvement to occur, new prescribers need to learn from their mistakes. Traditionally, the education of junior doctors has focused on their competence and professional registration requirements. Working in healthcare is collective and multidisciplinary, and errors occur through human and system factors.

Telehealth and the COVID-19 Pandemic: International Perspectives and a Health Systems Framework for Telehealth Implementation to Support Critical Response.

OBJECTIVES: Telehealth implementation is a complex systems-based endeavour. This paper compares telehealth responses to (COrona VIrus Disease 2019) COVID-19 across ten countries to identify lessons learned about the complexity of telehealth during critical response such as in response to a global pandemic. Our overall objective is to develop a health systems-based framework for telehealth implementation to support critical response. METHODS: We sought responses from the members of the International Medical Informatics Association (IMIA) Telehealth Working Group (WG) on their practices and perception of telehealth practices during the times of COVID-19 pandemic in their respective countries. We then analysed their responses to identify six emerging themes that we mapped to the World Health Organization (WHO) model of health systems. RESULTS: Our analysis identified six emergent themes. (1) Government, legal or regulatory aspects of telehealth; (2) Increase in telehealth capacity and delivery; (3) Regulated and unregulated telehealth; (4) Changes in the uptake and perception of telemedicine; (5) Public engagement in telehealth responses to COVID-19; and (6) Implications for training and education. We discuss these themes and then use them to develop a systems framework for telehealth support in critical response. CONCLUSION: COVID-19 has introduced new challenges for telehealth support in times of critical response. Our themes and systems framework extend the WHO systems model and highlight that telemedicine usage in response to the COVID-19 pandemic is complex and multidimensional. Our systems-based framework provides guidance for telehealth implementation as part of health systems response to a global pandemic such as COVID-19.

HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses.

The hardware-software co-optimization of neural network architectures is a field of research that emerged with the advent of commercial neuromorphic chips, such as the IBM TrueNorth and Intel Loihi. Development of simulation and automated mapping software tools in tandem with the design of neuromorphic hardware, whilst taking into consideration the hardware constraints, will play an increasingly significant role in deployment of system-level applications. This paper illustrates the importance and benefits of co-design of convolutional neural networks (CNN) that are to be mapped onto neuromorphic hardware with a crossbar array of synapses. Toward this end, we first study which convolution techniques are more hardware friendly and propose different mapping techniques for different convolutions. We show that, for a seven-layered CNN, our proposed mapping technique can reduce the number of cores used by 4.9-13.8 times for crossbar sizes ranging from 128 × 256 to 1,024 × 1,024, and this can be compared to the toeplitz method of mapping. We next develop an iterative co-design process for the systematic design of more hardware-friendly CNNs whilst considering hardware constraints, such as core sizes. A python wrapper, developed for the mapping process, is also useful for validating hardware design and studies on traffic volume and energy consumption. Finally, a new neural network dubbed HFNet is proposed using the above co-design process; it achieves a classification accuracy of 71.3% on the IMAGENET dataset (comparable to the VGG-16) but uses 11 times less cores for neuromorphic hardware with core size of 1,024 × 1,024. We also modified the HFNet to fit onto different core sizes and report on the corresponding classification accuracies. Various aspects of the paper are patent pending.

Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics.

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm. The proposed system comprises self-healable materials and memtransistors as enabling technologies for the implementation of neuromorphic nociceptors, spiking local associative learning and communication. Configuring memtransistors as gated-threshold and -memristive switches, the demonstrated system features in-memory edge computing with minimal hardware circuitry and wiring, and enhanced fault tolerance and robustness.

Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks.

Shallow feed-forward networks are incapable of addressing complex tasks such as natural language processing that require learning of temporal signals. To address these requirements, we need deep neuromorphic architectures with recurrent connections such as deep recurrent neural networks. However, the training of such networks demand very high precision of weights, excellent conductance linearity and low write-noise- not satisfied by current memristive implementations. Inspired from optogenetics, here we report a neuromorphic computing platform comprised of photo-excitable neuristors capable of in-memory computations across 980 addressable states with a high signal-to-noise ratio of 77. The large linear dynamic range, low write noise and selective excitability allows high fidelity opto-electronic transfer of weights with a two-shot write scheme, while electrical in-memory inference provides energy efficiency. This method enables implementing a memristive deep recurrent neural network with twelve trainable layers with more than a million parameters to recognize spoken commands with >90% accuracy.

Ethics in Telehealth: Comparison between Guidelines and Practice-based Experience -the Case for Learning Health Systems.

OBJECTIVES: To understand ethical issues within the tele-health domain, specifically how well established macro level telehealth guidelines map with micro level practitioner perspectives. METHODS: We developed four overarching issues to use as a starting point for developing an ethical framework for telehealth. We then reviewed telemedicine ethics guidelines elaborated by the American Medical Association (AMA), the World Medical Association (WMA), and the telehealth component of the Health Professions council of South Africa (HPCSA). We then compared these guidelines with practitioner perspectives to identify the similarities and differences between them. Finally, we generated suggestions to bridge the gap between ethics guidelines and the micro level use of telehealth. RESULTS: Clear differences emerged between the ethics guidelines and the practitioner perspectives. The main reason for the differences were the different contexts where telehealth was used, for example, variability in international practice and variations in the complexity of patient-provider interactions. Overall, published guidelines largely focus on macro level issues related to technology and maintaining data security in patient-provider interactions while practitioner concern is focused on applying the guidelines to specific micro level contexts. CONCLUSIONS: Ethics guidelines on telehealth have a macro level focus in contrast to the micro level needs of practitioners. Work is needed to close this gap. We recommend that both telehealth practitioners and ethics guideline developers better understand healthcare systems and adopt a learning health system approach that draws upon different contexts of clinical practice, innovative models of care delivery, emergent data and evidence-based outcomes. This would help develop a clearer set of priorities and guidelines for the ethical conduct of telehealth.

Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning.

The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms. We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reliable anomaly detection. Moreover, we devise a local log quantization strategy for model weights to reduce the memory footprint for deployment in memory constrained systems such as wearable devices. The proposed hybrid CNN-RNN model achieves a score of [Formula: see text] on four-class classification of breathing cycles for ICBHI'17 scientific challenge respiratory sound database. When the model is re-trained with patient specific data, it produces a score of [Formula: see text] for leave-one-out validation. The proposed weight quantization technique achieves ≈ 4 × reduction in total memory cost without loss of performance. The main contribution of the paper is as follows: Firstly, the proposed model is able to achieve state of the art score on the ICBHI'17 dataset. Secondly, deep learning models are shown to successfully learn domain specific knowledge when pre-trained with breathing data and produce significantly superior performance compared to generalized models. Finally, local log quantization of trained weights is shown to be able to reduce the memory requirement significantly. This type of patient-specific re-training strategy can be very useful in developing reliable long-term automated patient monitoring systems particularly in wearable healthcare solutions.

YY1 control of mitochondrial-related genes does not account for regulation of immunoglobulin class switch recombination in mice.

Immunoglobulin class switch recombination (CSR) occurs in activated B cells with increased mitochondrial mass and membrane potential. Transcription factor Yin Yang 1 (YY1) is critical for CSR and for formation of the DNA loops involved in this process. We therefore sought to determine if YY1 knockout impacts mitochondrial gene expression and mitochondrial function in murine splenic B cells, providing a potential mechanism for regulating CSR. We identified numerous genes in splenic B cells differentially regulated when cells are induced to undergo CSR. YY1 conditional knockout caused differential expression of 1129 genes, with 59 being mitochondrial-related genes. ChIP-seq analyses showed YY1 was directly bound to nearly half of these mitochondrial-related genes. Surprisingly, at the time when YY1 knockout dramatically reduces DNA loop formation and CSR, mitochondrial mass and membrane potential were not significantly impacted, nor was there a significant change in mitochondrial oxygen consumption, extracellular acidification rate, or mitochondrial complex I or IV activities. Our results indicate that YY1 regulates numerous mitochondrial-related genes in splenic B cells, but this does not account for the impact of YY1 on CSR or long-distance DNA loop formation.