Central Processing Research Articles

Knowledge mobilisation of rapid evidence reviews to inform health and social care policy and practice in a public health emergency: Appraisal of the Wales COVID-19 Evidence Centre processes and impact, 2021-23.

The Wales COVID-19 Evidence Centre (WCEC) was established from 2021-23 to ensure that the latest coronavirus (COVID-19) relevant research evidence was readily available to inform health and social care policy and practice decision-makers. Although decisions need to be evidence-based, ensuring that accessible and relevant research evidence is available to decision-makers is challenging, especially in a rapidly evolving pandemic environment when timeframes for decision-making are days or weeks rather than months or years. We set up knowledge mobilisation processes to bridge the gap between evidence review and informing decisions, making sure that the right information reaches the right people at the right time. To describe the knowledge mobilisation processes used by the WCEC, evaluate the impact of the WCEC rapid evidence reviews, and share lessons learned. Our knowledge mobilisation methods were flexible and tailored to meet stakeholders' needs. They included stakeholder co-production in our rapid evidence review processes, stakeholder-informed and participatory knowledge mobilisation, wider dissemination of outputs and associated activities including public engagement, capacity building and sharing of methodologies. Feedback on processes and evidence of impact was collected via stakeholder engagement and a stakeholder survey. Findings indicate that knowledge mobilisation processes successfully enabled use of the WCEC's rapid evidence reviews to inform policy and practice decision-makers during the COVID-19 pandemic in Wales. Realising actual public and patient benefit from this 'pathway to impact' work will take further time and resources. The WCEC knowledge mobilisation processes successfully supported co-production and use of rapid evidence review findings by scientific advisors and policy and practice decision-makers during the COVID-19 pandemic. Identified barriers and facilitators are of potential relevance to wider evidence initiatives, for setting up similar Centres during crisis situations, and supporting future evidence-based policy and practice decision-making.

A Data-Loader Tunable Knob to Shorten GPU Idleness for Distributed Deep Learning

Deep Neural Networks (DNNs) have been applied as an effective machine learning algorithm to tackle problems in different domains. However, the endeavor to train sophisticated DNN models can stretch from days into weeks, presenting substantial obstacles in the realm of research focused on large-scale DNN architectures. Distributed Deep Learning (DDL) contributes to accelerating DNN training by distributing training workloads across multiple computation accelerators, for example, graphics processing units (GPUs). Despite the considerable amount of research directed toward enhancing DDL training, the influence of data loading on GPU utilization and overall training efficacy remains relatively overlooked. It is non-trivial to optimize data-loading in DDL applications that need intensive central processing unit (CPU) and input/output (I/O) resources to process enormous training data. When multiple DDL applications are deployed on a system (e.g., Cloud and High-Performance Computing (HPC) system), the lack of a practical and efficient technique for data-loader allocation incurs GPU idleness and degrades the training throughput. Therefore, our work first focuses on investigating the impact of data-loading on the global training throughput. We then propose a throughput prediction model to predict the maximum throughput for an individual DDL training application. By leveraging the predicted results, A-Dloader is designed to dynamically allocate CPU and I/O resources to concurrently running DDL applications and use the data-loader allocation as a knob to reduce GPU idle intervals and thus improve the overall training throughput. We implement and evaluate A-Dloader in a DDL framework for a series of DDL applications arriving and completing across the runtime. Our experimental results show that A-Dloader can achieve a 28.9% throughput improvement and a 10% makespan improvement compared with allocating resources evenly across applications.

Simulation study of pharmaceutical supply chains based on system dynamics

Focusing on the multi-stage complex logistics distribution process of distribution centers, this study proposes and establishes a characteristic state model, analysis, and control methods for logistics distribution centers. These are combined with system dynamics simulation methods for logistics systems to conduct an in-depth study on the optimization and control of distribution center operations. Based on actual distribution process data from a pharmaceutical logistics distribution center, the proposed theories and methods are validated against real-world engineering backgrounds.

Environmental Condition Aware Super-Resolution Acceleration Framework in Server-Client Hierarchies

In the current landscape, high-resolution (HR) videos have gained immense popularity, promising an elevated viewing experience. Recent research has demonstrated that the video super-resolution (SR) algorithm, empowered by deep neural networks (DNNs), can substantially enhance the quality of HR videos by processing low-resolution (LR) frames. However, the existing DNN models demand significant computational resources, posing challenges for the deployment of SR algorithms on client devices. While numerous accelerators have proposed solutions, their primary focus remains on client-side optimization. In contrast, our research recognizes that the HR video is originally stored in the cloud server and presents an untapped opportunity for achieving both high accuracy and performance improvements. Building on this insight, this article introduces an end-to-end video CODEC-assisted super-resolution (E 2 SR+) algorithm, which tightly integrates the cloud server with the client device to deliver a seamless and real-time video viewing experience. We propose the motion vector search algorithm executed in a cloud server, which can search the motion vectors and residuals for a part of the HR video frames and then pack them as add-ons. We also design an auto-encoder algorithm to down-sample the residuals to save the bitstream cost while guaranteeing the quality of the residuals. Lastly, we propose a reconstruction algorithm performed in the client to quickly reconstruct the corresponding HR frames using the add-ons to skip part of the DNN computations. To implement the E 2 SR+ algorithm, we design corresponding E 2 SR+ architecture in the client, which achieves significant speedup with minimal hardware overhead. Given that the environmental condition varies in the server–client hierarchies, we believe that simply applying E 2 SR+ to all frames is irrational. Accordingly, we offer an environmental condition–aware system to chase the best performance while adapting to the diverse environment. In the system, we design a linear programming (LP) model to simulate the environment and allocate frames to three existing mechanisms. Our experimental results demonstrate that the E 2 SR+ algorithm enhances the peak signal-to-noise ratio by 1.2, 2.5, and 2.3 compared with the state-of-the-art (SOTA) methods EDVR, BasicVSR, and BasicVSR++, respectively. In terms of performance, the E 2 SR+ architecture offers significant improvements over existing SOTA methods. For instance, while BasicVSR++ requires 98 ms on an NVIDIA V100 graphics processing unit (GPU) to generate a 1,280 × 720 HR frame, the E 2 SR+ architecture reduces the execution time to just 39 ms, highlighting the efficiency and effectiveness of our proposed method. Overall, the E 2 SR+ architecture respectively achieves 1.4×, 2.2×, 4.6×, and 442.0× performance improvement compared with ADAS, ISRAcc, the NVIDIA V100 GPU, and a central processing unit. Lastly, the proposed system showcases its superiority and surpasses all the existing mechanisms in terms of execution time when varying environmental conditions.

Virtualization vs. Containerization, a Comparative Approach for Application Deployment in the Computing Continuum Focused on the Edge

With the emergence of containerization 10 years ago, we saw a compact, convenient and portable way of running apps directly concurrently with virtualization. The major difference is in the architecture. Containers share the same kernel as the guest and then do not virtualize low-layer components like the Central Processing Unit (CPU). On the one hand, they are lighter and more flexible than virtual machines (VMs). On the other hand, VMs can more precisely meet the low-layer needs and are completely autonomous systems. Nowadays, what is the best architecture to use to develop an application? In this paper, we will study the two main virtual methods of deploying this. We will compare both methods on several criteria: compatibility based on user experience and the ease of installation/deployment, scalability based on the automatic elasticity facing the workload and energy efficiency in terms of energy and computer resources. After the tests, we conclude that the containerization option is the most ecologically advantageous option in terms of energy consumption.

Impacts of Double Rotating Cylinders on the Forced Convection of Nanoencapsulated Phase Change Material

ABSTRACTMany engineering and industrial applications rely on heat transfer (HT) as a basic and central process. Therefore, engineers and researchers place significant emphasis on optimizing HT rates. Here, we numerically attempt to maximize the heat transmission rate of mixed convection of nanoencapsulated phase change material in a square compartment. The compartment is differentially heated and incorporates two cold rotating cylinders. The Galerkin finite element approach is utilized for addressing the system governing equations. A range of various factors affecting the thermal activity in the compartment were considered. These factors include the speed of spinning cylinders (Re = 0–1000), the porousness of the compartment (Da = 10−5–10−2), the magnetic field intensity (Ha = 0–100), and the concentration of nanoadditives (ϕ = 0%–8%). The obtained numerical findings demonstrated that the thermal activity inside the compartment is positively correlated to the speed of the spinning cylinders, the concentration of nanoadditives, and the porousness of the compartment. In contrast, increasing the intensity of the magnetic field obstructs the heat transmission. It was noted that at the highest Re number, the average Nusselt number augmented by 257% and 13.6% when increasing Da and ϕ, respectively.

Human perception of self-motion and orientation during galvanic vestibular stimulation and physical motion

Galvanic vestibular stimulation (GVS) is an emergent tool for stimulating the vestibular system, offering the potential to manipulate or enhance processes relying on vestibular-mediated central pathways. However, the extent of GVS’s influence on the perception of self-orientation pathways is not understood, particularly in the presence of physical motions. Here, we quantify roll tilt perception impacted by GVS during passive whole-body roll tilts in humans (N = 11). We find that GVS systematically amplifies and attenuates perceptions of roll tilt during physical tilt, dependent on the GVS waveform. Subsequently, we develop a novel computational model that predicts 6DoF self-motion and self-orientation perceptions for any GVS waveform and motion by modeling the vestibular afferent neuron dynamics modulated by GVS in conjunction with an observer central processing model. This effort provides a means to systematically alter spatial orientation perceptions using GVS during concurrent physical motion, and we find that irregular afferent dynamics alone best describe resultant perceptions.

Utilization Fraction of Ambulatory Hand Procedures: Cost-Reduction Through Surgical Instrument Tray Optimization.

Our objective is to evaluate the utilization fraction (UF) of surgical instruments during a commonly performed ambulatory hand surgery case as an avenue for cost reduction, increased operating room efficiency, and systems quality improvement. The total number of instruments opened at the start of the case was recorded followed by instruments being divided into those used and not used during the procedure. Total sterile processing costs were estimated at $1.56 per instrument according to data from our institution's central sterilization processing (CSP) department. Nineteen hand procedures performed by 2 surgeons were included in this study. An average of 120.1 ± 10.9 instruments were opened at the start of each case, while an average of 12.6 ± 5.4 instruments were used per case (Figure 1). This yielded an UF of 10.7% ± 4.8%. Using our internal CSP estimate, we calculated an annual cost of $16 863 to reprocess the current hand tray (Figure 2). Using literature data, this cost ranged from $5 513 to $34 484 annually. The same cost calculations were performed for the theoretical optimized tray (incorporating instruments used at least 20% of the time when opened) containing 23.2 instruments. The annual reprocessing cost of this new tray according to CSP data was $3 260, demonstrating a cost-reduction of $13 603 or 80.7% (Figure 2). Evaluation of pre- and peri-operative processes is a valuable technique to mitigate increasing healthcare costs and reduce unnecessary healthcare spending, with broad applicability to multiple surgical subspecialties and procedures.

Rapid sensorimotor adaptation to auditory midbrain silencing in free-flying bats

Echolocating bats rely on rapid processing of auditory information to guide moment-to-moment decisions related to echolocation call design and flight path selection. The fidelity of sonar echoes, however, can be disrupted in natural settings due to occlusions, noise, and conspecific jamming signals. Behavioral sensorimotor adaptation to external blocks of relevant cues has been studied extensively, but little is known about adaptations that mitigate internal sensory flow interruption. How do bats modify their sensory-guided behaviors in natural tasks when central auditory processing is interrupted? Here, we induced internal sensory interruptions by reversibly inactivating excitatory neurons in the inferior colliculus (IC) using ligand-activated inhibitory designer receptors exclusively activated by designer drugs (DREADDs). Bats were trained to navigate through one of three open windows in a curtain to obtain a food reward, while their echolocation and flight behaviors were quantified with synchronized ultrasound microphone and stereo video recordings. Under control conditions, bats reliably steered through the open window, only occasionally contacting the curtain edge. Suppressing IC excitatory activity elevated hearing thresholds, disrupted overall performance in the task, increased the frequency of curtain contact, and led to striking compensatory sensorimotor adjustments. DREADDs-treated bats modified flight trajectories to maximize returning echo information and adjusted sonar call design to boost detection of obstacles. Sensorimotor adaptations appeared immediately and did not change over successive trials, suggesting that these behavioral adaptations are mediated through existing neural circuitry. Our findings highlight the remarkable rapid adaptive strategies bats employ to compensate for internal sensory interruptions to effectively navigate their environments.

A Survey on Design Space Exploration Approaches for Approximate Computing Systems

Approximate Computing (AxC) has emerged as a promising paradigm to enhance performance and energy efficiency by allowing a controlled trade-off between accuracy and resource consumption. It is extensively adopted across various abstraction levels, from software to architecture and circuit levels, employing diverse methodologies. The primary objective of AxC is to reduce energy consumption for executing error-resilient applications, accepting controlled and inherently acceptable output quality degradation. However, harnessing AxC poses several challenges, including identifying segments within a design amenable to approximation and selecting suitable AxC techniques to fulfill accuracy and performance criteria. This survey provides a comprehensive review of recent methodologies proposed for performing Design Space Exploration (DSE) to find the most suitable AxC techniques, focusing on both hardware and software implementations. DSE is a crucial design process where system designs are modeled, evaluated, and optimized for various extra-functional system behaviors such as performance, power consumption, energy efficiency, and accuracy. A systematic literature review was conducted to identify papers that ascribe their DSE algorithms, excluding those relying on exhaustive search methods. This survey aims to detail the state-of-the-art DSE methodologies that efficiently select AxC techniques, offering insights into their applicability across different hardware platforms and use-case domains. For this purpose, papers were categorized based on the type of search algorithm used, with Machine Learning (ML) and Evolutionary Algorithms (EAs) being the predominant approaches. Further categorization is based on the target hardware, including Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), general-purpose Central Processing Units (CPUs), and Graphics Processing Units (GPUs). A notable observation was that most studies targeted image processing applications due to their tolerance for accuracy loss. By providing an overview of techniques and methods outlined in existing literature pertaining to the DSE of AxC designs, this survey elucidates the current trends and challenges in optimizing approximate designs.

Inferring TLB Configuration with Performance Tools

Modern computing systems are primarily designed for maximum performance, which inadvertently introduces vulnerabilities at the micro-architecture level. While cache side-channel analysis has received significant attention, other Central Processing Units (CPUs) components like the Translation Lookaside Buffer (TLB) can also be exploited to leak sensitive information. This paper focuses on the TLB, a micro-architecture component that is vulnerable to side-channel attacks. Despite the coarse granularity at the page level, advancements in tools and techniques have made TLB information leakage feasible. The primary goal of this study is not to demonstrate the potential for information leakage from the TLB but to establish a comprehensive framework to reverse engineer the TLB configuration, a critical aspect of side-channel analysis attacks that have previously succeeded in extracting sensitive data. The methodology involves detailed reverse engineering efforts on Intel CPUs, complemented by analytical tools to support TLB reverse engineering. This study successfully reverse-engineered the TLB configurations for Intel CPUs and introduced visual tools for further analysis. These results can be used to explore TLB vulnerabilities in greater depth. However, when attempting to apply the same methodology to the IBM Power9, it became clear that the methodology was not transferable, as mapping functions and performance counters vary across different vendors.

Role of Alternative Splicing in Oncogenesis

Over the past two decades, much evidence has accumulated that confirms the crucial role of alternative splicing in the process of tumorigenesis. A more detailed study of splicing mechanisms revealed that targeting the central process for atypical cells could be a potential new approach in the treatment of malignant neoplasms. Firstly, specific protein isoforms that are formed as a result of alternative splicing and are involved in tumorigenesis can potentially act as a target for the treatment of malignant neoplasms. Second, high rates of cell proliferation presumably make tumor cells highly dependent on a functional spliceosome, creating potential hypersensitivity to global splicing modulation. The study of the role of alternative splicing in tumorigenesis and the search for therapeutic targets contributed not only to the development of a more promising direction in oncology, but also to the search for new drugs that have a targeted effect on the development of malignant neoplasms.

Linarin Identified as a Bioactive Compound of Lycii Cortex Ameliorates Insulin Resistance and Inflammation Through the c-FOS/ARG2 Signaling Axis.

Insulin resistance (IR) is a central pathophysiological process underlying numerous chronic metabolic disorders, including type 2 diabetes and obesity. Lycii Cortex, a widely used traditional Chinese herb, has demonstrated potential benefits in preventing and managing diabetes and IR. Whereas, the specific bioactive compounds responsible for these protective effects and their underlying mechanisms of action remain elusive. This study aimed to identify the bioactive components within Lycii Cortex that contribute to its anti-diabetic effects and to elucidate the molecular mechanisms underlying its beneficial actions on insulin resistance. Network pharmacology and molecular docking analyses were employed to identify the potential active compounds in Lycii Cortex and their corresponding target proteins. An invitro model of IR was established using palmitic acid (PA)-treated HepG2 cells. Cell viability was assessed using the CCK-8 assay, while glucose uptake was evaluated by 2-NBDG staining and extracellular glucose measurement. To validate the invitro findings, an invivo model of obesity-induced IR was established using high-fat diet (HFD)-fed mice. The network pharmacology analysis preliminarily identified 13 candidate chemicals and 10 hub LyC and IR-related genes (LIRRGs). Molecular docking analysis demonstrates that Linarin as the potential active component exhibits the greatest potential to target c-FOS for preventing obesity-induced IR. Enrichment analysis suggested that Linarin-targeted pathways are correlated with inflammation. Invitro experimental validation demonstrated that Linarin was capable of protecting against PA-induced IR in HepG2 cells evidenced by improving glucose uptake ability and reducing extracellular glucose content. Additionally, we found that Linarin ablated PA-induced increase in the expression of c-FOS and inflammatory cytokines. Furthermore, in PA-treated cells, silencing c-FOS markedly improved glucose consumption, and reduced inflammation and Arginase 2 (ARG2) expression. Similarly, as exposure to PA, silencing ARG2 also ameliorated glucose uptake and inflammation, while not affecting c-FOS expression. Invivo experiments further showed that Linarin administration remarkably improved glucose tolerance and insulin sensitivity, and reduced the fat mass and body weight in HFD-induced obese mice. In this study, Linarin has been identified as the bioactive compound of Lycii Cortex to ameliorate obesity-related IR and inflammation through the c-FOS/ARG2 signaling cascade. These findings underscore the therapeutic potential of Linarin and provide valuable insights into developing novel intervention strategies for type 2 diabetes therapy.

Lower Energy Consumption in Multi-CPU Cell-Free Massive MIMO Systems

Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the feasibility of strict phase synchronization, CF systems require a multi-CPU setup and perform coherent transmission at a smaller scale. Moreover, conventional CF systems typically operate in time-division duplex (TDD) mode and utilize statistical channel state information (CSI) for downlink (DL) decoding, but the channel hardening effect is not significant. These factors reduce downlink spectral efficiency (SE) and increase DL transmission time, leading to higher energy consumption in CF systems. To address these issues, we introduce downlink channel estimation (DLCE) in multi-CPU CF systems and derive the approximate achievable DL SE. To reduce DL pilot overhead, we propose an uplink–pilot-reuse-constrained DL pilot allocation principle. Based on this principle, we develop a farthest distance pilot allocation (FDPA) algorithm to mitigate pilot contamination. In addition, leveraging the characteristics of the heuristic distributed power allocation algorithm, we propose two access point (AP) clustering algorithms: one based on CSI (BCSI) and the other based on coherent group size (BCGS). Simulation results indicate that the introduction of DLCE significantly improves DL SE in multi-CPU CF massive MIMO systems, while the proposed FDPA algorithm further enhances DL SE. The BCSI and BCGS algorithms also effectively improve DL SE and help reduce energy consumption. By combining DLCE, the FDPA algorithm, and the proposed AP clustering algorithms, the energy consumption of multi-CPU CF systems can be significantly reduced.

Central auditory processing and cognitive abilities after mild traumatic brain injury in school-aged children

Objective The current study aimed to explore (1) school-aged children’s auditory and cognitive abilities and (2) their perceptions (self-reported and parents) of listening behaviours and symptoms following mild traumatic brain injury (mTBI). Design Prospective cohort study. Study Sample Twenty-one children aged 8 to 12 participated in this study. Eleven children with mTBI were diagnosed in the emergency department, and 10 were matched controls. They were assessed twice, in the acute (≤14 days) and chronic (≥3 months) stages post-injury, using six central auditory behavioural tests, a cognitive screening battery, and questionnaires. ANOVA and correlation analysis were performed. Results Group differences were present in three auditory tests as children with mTBI obtained poorer scores than controls (Pitch Pattern Sequence Tests - PPST: p = 0.037; Gap In Noise test - GIN threshold: p = 0.046; Duration Pattern Test - DPT: p = 0.053), but not for perceived listening behaviours and cognitive abilities. Correlations were found only between perceived listening behaviours and reported mTBI symptoms for parents (p =0.014) and children (p =0.002). Conclusions Auditory abilities, mostly those related to temporal processing, were affected following mTBI, independently of cognitive abilities. Better identification of auditory processing difficulties following mTBI will improve treatment and resource allocation for children.

Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning.

mRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. We combined biophysical models and machine learning to develop steady-state and kinetic decay models of mRNA stability with high accuracy and generalizability, utilizing transcription rate models to identify mRNA isoforms and translation rate models to calculate ribosome protection. Overall, the developed model quantifies the key interactions that collectively control mRNA stability in bacterial operons and predicts how changing mRNA sequence alters mRNA stability, which is important when studying and engineering bacterial genetic systems.

Characterizing Hardware Utilization on Edge Devices when Inferring Compressed Deep Learning Models

Implementing edge AI involves running AI algorithms near the sensors. Deep Learning (DL) Model has successfully tackled image classification tasks with remarkable performance. However, their requirements for huge computing resources hinder the implementation of edge devices. Compressing the model is an essential task to allow the implementation of the DL model on edge devices. Post-training quantization (PTQ) is a compression technique that reduces the bit representation of the model weight parameters. This study looks at the impact of memory allocation on the latency of compressed DL models on Raspberry Pi 4 Model B (RPi4B) and NVIDIA Jetson Nano (J. Nano). This research aims to understand hardware utilization in central processing units (CPU), graphics processing units (GPU),and memory. This study focused on the quantitative method, which controls memory allocation and measures warm-up time, latency, CPU, and GPU utilization. Speed comparison among inference of DL models on RPi4B and J. Nano. This paper observes the correlation between hardware utilization versus the various DL inference latencies. According to our experiment, we concluded that smaller memory allocation led to high latency on both RPi4B and J. Nano. CPU utilization on RPi4B. CPU utilization in RPi4B increases along with the memory allocation; however, the opposite is shown on J. Nano since the GPU carries out the main computation on the device. Regarding computation, thesmaller DL Size and smaller bit representation lead to faster inference (low latency), while bigger bit representation on the same DL model leads to higher latency.

New ways to repurpose salmeterol in an animal model of fibromyalgia.

Fibromyalgia (FM) is a syndrome of pervasive chronic pain accompanied by low mood, sleep disorders, and cognitive decline. The dysfunction of central pain processing systems along with neurotransmitter disturbances are possible contributing mechanisms. Genetic polymorphism of the 𝛽2 adrenergic receptors is reported in FM patients. It is reported that chronic β2 agonists administration is effective for neuropathic pain alleviation. No current information, however, exists on their potential to alleviate nociplastic pain, such as FM. Therefore, the purpose of the current study is to examine salmeterol's potential antiallodynic effects in experimentally produced FM and explore some of the possible contributing mechanisms. Thirty rats are allocated into three groups (n = 10): a normal group, a reserpine group that received reserpine (1 mg/kg; s.c.) for 3 days, and a reserpine + salmeterol group that received salmeterol (1 mg/kg; i.p.) for 21 consecutive days following last reserpine injection. Reserpine administration resulted in behavioral and biochemical changes consistent with FM, including thermal and mechanical hyperalgesia, depressive behavior, and motor incoordination. This is coupled with disturbed spinal monoamine levels, depressed cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) signaling, disturbed mitochondrial function/dynamics, and compromised blood-nerve barrier integrity. Treatment with salmeterol conceivably reversed these effects. β2 receptor agonists such as salmeterol could be regarded as a promising strategy for the management of FM.

Conditioned Pain Modulation Differences in Central and Peripheral Burning Mouth Syndrome (BMS) Patients.

To evaluate conditioned pain modulation (CPM) in burning mouth syndrome (BMS) patients with different pain mechanisms. Twenty BMS patients (52.0 ± 6.8 years, 17 women and 3 men) and age- and gender-matched 22 healthy controls were enrolled in this randomised controlled trial. The patients received an active lingual nerve block (lidocaine) and a placebo injection (saline) randomly with an interval of 1 week in a double-blinded manner. Patients evaluated their pain intensity on a 0- to 10-cm visual analogue scale (VAS) before and after each injection, with or without CPM. Based on the anaesthesia effect, BMS patients were divided into two groups with presumed different pain mechanisms; a 'central subgroup (n = 11)' with pain relief less than 1 cm and 'peripheral subgroup (n = 9)' with pain relief more than 1 cm on the VAS. Mechanical pain threshold (MPT) and wind-up ratio (WUR) were investigated at two oral mucosa regions: the region with most intense symptoms and a control region for the patient group; tongue and buccal region for the control group. CPM was induced by immersing the left hand into cold water. A moderate level of pain (around five on the VAS) was obtained by adjusting the water temperature. MPT and WUR were measured twice for all the participants with and without CPM, which was analysed and presented as relative change in MPT and WUR. Differences between groups were analysed using two-way ANOVA. Differences within group between tests were assessed by paired t-test. At baseline, there were no significant group differences for MPT or WUR between BMS patients and healthy controls (p ≥ 0.156). The mean bath temperature to evoke moderate pain for the BMS group was significantly lower than that for the healthy control group (8.9°C vs. 11.9°C, p = 0.003). The CPM evoked an inhibitory modulation in 18.2%-44.4% of BMS patients, while for the healthy group, the ratio was 68.2%-81.8%. Central BMS patients had smaller CPM effects than healthy participants at the painful site and control site, which indicated a decreased CPM function (p ≤ 0.034). Peripheral BMS patients had lower CPM effects than healthy participants only at the painful site (p = 0.037). The present findings documented impairment of central nociceptive inhibition processing in BMS patients which was more extensive in central BMS than peripheral BMS. These findings add to the suggestion that BMS may a heterogeneous pain condition with at least two different phenotypes.

Analog Implementation of a Spiking System for Performing Arithmetic Logic Operations on Mixed‐Signal Neuromorphic Processors

In recent years, physical limitations in the integration of transistors in computers have forced the search for low‐computational‐power alternatives in hardware design. Although doubts may arise regarding the limit of the relationship between performance and power consumption in computers, these disappear when considering the brain, which is one of the most efficient computing systems. In this way, bioinspired applications try to benefit from the low‐power consumption present in the biological nervous system. Previous work has shown the feasibility of implementing spiking neural networks that operate in a Boolean manner on digital platforms, such as SpiNNaker, using basic logic gates and a spiking memory, which suggests the potential for constructing a low‐power consumption spiking computer. This work takes a first step in the implementation of a spiking central processing unit by developing an arithmetic logic unit, which is an essential block for instruction execution, demonstrating its correct operation on Dynap‐SE1. The results confirm the feasibility of using this Boolean approach on this platform, despite certain limitations in the number of inputs and operating frequencies of the blocks, and pave the way for the construction of a spiking computer.