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The existing global health crisis characterized by limited resources, including health personnel, has prompted the adoption of telemedicine and telehealth, especially in the post-pandemic era. The COVID-19 pandemic accelerated the adoption of these technologies, and as the world navigates beyond the crisis, it is essential to assess the extent of utilization of telehealth and telemedicine. This review study aims to assess the extent to which teleservices have been implemented worldwide across different continents. Peer-reviewed telehealth and telemedicine articles were reviewed across Web of Science, Scopus, Cochrane Library and Pubmed/Medline databases. The exclusion criteria comprised all articles published before 1 December 2019, any other databases, duplicates, and grey literature. The inclusion criteria for this study encompassed articles published on or after December 1, 2019. This timeframe allowed us to focus on the pandemic and post-pandemic era. A total of 381 publications were identified for inclusion, which were screened based on reviewing the titles, abstracts and full-text content down to 102 relevant articles. Utilization trends were identified amongst the different countries across the continents, and these were classified into advanced, developed, developing and emerging adoption stages. The respective characteristics utilized in differentiating these adoption stages were identified, encompassing the inclusivity of teleservices in administration, disease diagnosis, treatment, patient follow-ups, pharmacy services and electronic health records transversely. According to this review, of the countries surveyed (n = 77), (n = 27) 13.8 % are at an advanced adoption stage, (n = 20) 10.3 % are at a developed level, (n = 24) 12.3 % are at the developed stage, and (n = 6) 3 % are at the emerging stage, as percentages of all the countries in the world (N = 195). In conclusion, this study demonstrated the extent to which various nations have adopted telehealth and telemedicine from the onset of the COVID-19 pandemic to 2023.

Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. Error backpropagation is presently regarded as the most effective method for training SNNs, but in a twist of irony, when training on modern graphics processing units this becomes more expensive than non-spiking networks. The emergence of Graphcore’s intelligence processing units (IPUs) balances the parallelized nature of deep learning workloads with the sequential, reusable, and sparsified nature of operations prevalent when training SNNs. IPUs adopt multi-instruction multi-data parallelism by running individual processing threads on smaller data blocks, which is a natural fit for the sequential, non-vectorized steps required to solve spiking neuron dynamical state equations. We present an IPU-optimized release of our custom SNN Python package, snnTorch , which exploits fine-grained parallelism by utilizing low-level, pre-compiled custom operations to accelerate irregular and sparse data access patterns that are characteristic of training SNN workloads. We provide a rigorous performance assessment across a suite of commonly used spiking neuron models, and propose methods to further reduce training run-time via half-precision training. By amortizing the cost of sequential processing into vectorizable population codes, we ultimately demonstrate the potential for integrating domain-specific accelerators with the next generation of neural networks.

Despite the impressive demonstrations with silicon and oxide memristors, realizing efficient roots of trust for resource-constrained hardware remains a challenge. Here, the authors exploit switching behavior in one dimensional perovskite memristors to design security primitives for key generation and device authentication.

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.AimThis 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.MethodologyScoping 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.FindingsThere 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.ConclusionGreater 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.

Sensory information processing in robots relies on a centralized approach with issues of wiring, fault-tolerance and latency. Here, the authors report a decentralized neuromorphic approach with self-healable memristive elements enabling intelligent sensations in a prototypical robotic nervous system.

Accomplishing complex cognitive tasks such as speech recognition calls for artificial intelligence hardware with high computing precision. John et al. propose deep recurrent neural networks based on optoelectronic transition metal dichalcogenide memristors with high weight precision for in-memory computing.

Neuromorphic computing provides a promising energy-efficient alternative to von-Neumann-type computing and learning architectures. However, the best neuromorphic hardware is useless without suitable inference and learning algorithms that can fully exploit hardware advantages. Such algorithms often have to deal with challenging constraints posed by neuromorphic hardware such as massive parallelism, sparse asynchronous communication, and analog and/or unreliable computing elements. This Focus Issue presents advances on various aspects of algorithms for neuromorphic computing. The collection of articles covers a wide range from very fundamental questions about the computational properties of the basic computing elements in neuromorphic systems, algorithms for continual learning, semantic segmentation, and novel efficient learning paradigms, up to algorithms for a specific application domain.

Few topics are currently as polarizing as the appropriate limits, and perceived dangers, of free speech on university campuses. A side effect of this polarized environment is that students themselves may be reluctant to speak publicly on politically sensitive topics. Indeed, recent surveys by the Heterodox Academy (HxA) revealed that a majority of American university students thought their campus was not conducive to the free expression of ideas, and a substantial minority were personally reluctant to discuss “hot topics” like politics or sexual orientation in class. To see whether these results are uniquely American phenomena, we reran the HxA’s survey on 791 students, recruited via advertisements, enrolled in New Zealand universities. As in the original survey, participants answered questions, administered online, about their comfort sharing their opinions on issues related to gender, politics, religion, and sexual orientation, as well as their estimates of other groups’ discomfort. Despite significant sociopolitical differences between the two countries, our results, generally speaking, bear out those in the United States. In both countries, politics elicited the most reluctance to speak, followed by religion, and then gender and sexual orientation (which were equivalent), and New Zealanders were more reluctant than Americans to speak on the latter two topics. Other similarities and differences between the two data sets are discussed, but it is clear that chilled campus speech is not confined to the United States.

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.

While Polo-like kinase 1 (PLK1) inhibitors have shown promise in clinical settings for treating triple-negative breast cancer tumors and other solid tumors, they are limited by their ability to bind non-selectively to the ATP kinase domain. Therefore, we sought to develop a PLK1 allosteric inhibitor targeting the PLK1 T-loop (a switch responsible for activation) and evaluate its effects in triple-negative breast cancer cells. A novel compound, RK-10, was developed based on an in silico model, and its effects on specificity, viability, migration, and cell cycle regulation in MCF-10A and MDA-MB 231 cells were evaluated. When MDA-MB 231 cells were treated with 0–50 µg/mL RK-10, phospho-PLK1 (Thr-210) was decreased in cells cultured adherently and cells cultured as mammospheres. RK-10 significantly inhibited viability after 24 h; however, by 48 h, 25–50 µM RK-10 caused >50% reduction. RK-10 attenuated wound healing by up to 99.7% and caused S and G2/M cell cycle arrest, which was associated with increased p21 expression. We developed a novel allosteric inhibitor which mediates anti-proliferative and anti-migratory properties through targeting phospho-PLK1 (Thr-210) in mammospheres and causing S phase and G2/M cell cycle arrest. Further development of PLK1 allosteric inhibitors may be a promising approach for TNBC treatment.

Asynchronous event-based sensors, or “silicon retinae,” are a new class of vision sensors inspired by biological vision systems. The output of these sensors often contains a significant number of noise events along with the signal. Filtering these noise events is a common preprocessing step before using the data for tasks such as tracking and classification. This paper presents a novel spiking neural network-based approach to filtering noise events from data captured by an Asynchronous Time-based Image Sensor on a neuromorphic processor, the IBM TrueNorth Neurosynaptic System. The significant contribution of this work is that it demonstrates our proposed filtering algorithm outperforms the traditional nearest neighbor noise filter in achieving higher signal to noise ratio (~10 dB higher) and retaining the events related to signal (~3X more). In addition, for our envisioned application of object tracking and classification under some parameter settings, it can also generate some of the missing events in the spatial neighborhood of the signal for all classes of moving objects in the data which are unattainable using the nearest neighbor filter.

This paper presents a real-time, low-complexity neuromorphic speech recognition system using a spiking silicon cochlea, a feature extraction module and a population encoding method based Neural Engineering Framework (NEF)/Extreme Learning Machine (ELM) classifier IC. Several feature extraction methods with varying memory and computational complexity are presented along with their corresponding classification accuracies. On the N-TIDIGITS18 dataset, we show that a fixed bin size based feature extraction method that votes across both time and spike count features can achieve an accuracy of 95% in software similar to previously report methods that use fixed number of bins per sample while using ~3× less energy and ~25× less memory for feature extraction (~1.5× less overall). Hardware measurements for the same topology show a slightly reduced accuracy of 94% that can be attributed to the extra correlations in hardware random weights. The hardware accuracy can be increased by further increasing the number of hidden nodes in ELM at the cost of memory and energy.

The United States Pharmacopeia-National Formulary (USP-NF) suggests beyond use dating ((BUD) for compounded topical preparations containing active pharmaceutical ingredients (API) [1]. The beyond the use dates of the preparations are based on the conservative and empirical guidelines of USP 795. Hydrocortisone (HC) content compounded in the VersaPro™ base cream was quantified using a HPLC method at time zero and after 30 and 60 days of storage at both room temperature and 4°C. The analysis suggests that the compounded preparations retain > 95% of the stated initial potency of HC regardless of storage conditions. Thus HC creams prepared in VersaPro™ cream base can be safely assigned a beyond the use date of two months when stored at room temperature. Type: Original Research