Search Results (15)

Accurate diagnosis of PM and precise histologic subtyping are critical for optimal therapeutic decision-making, as treatment strategies—including chemotherapy, immunotherapy, or multimodality approaches—are largely subtype-dependent. Because of the several-decade latency between fiber inhalation and symptom onset, many cases are diagnosed at an advanced stage, when patients are already in poor clinical condition. As observed across multiple solid malignancies, earlier-stage diagnosis is associated with improved prognosis and expanded therapeutic options. However, the rarity of PM, the absence of validated screening strategies, and its nonspecific clinical and radiologic presentation—often mimicking both benign and metastatic pleural conditions, frequently result in diagnostic delay. Furthermore, the lack of pathognomonic histopathologic markers further complicates timely and definitive diagnosis. This review aims to delineate the epidemiologic, clinical, radiologic, and pathologic barriers that hinder accurate and early detection of PM. Current clinical evidence points to an urgent need to develop novel, validated biomarkers in PM, which will require a multidisciplinary approach. Full article

The growing demand for energy efficiency and sustainability in smart buildings necessitates advanced AI-driven methods for adaptive control and predictive maintenance. This study explores the application of Spiking Neural Networks (SNNs) to event-driven processing, real-time anomaly detection, and edge computing-based optimization in building automation. In contrast to conventional deep learning models, SNNs provide low-power, high-efficiency computation by mimicking biological neural processes, making them particularly suitable for real-time, edge-deployed decision-making. The proposed SNN based on Reward-Modulated Spike-Timing-Dependent Plasticity (STDP) and Bayesian Optimization (BO) integrates occupancy and ambient condition monitoring to dynamically manage assets such as appliances while simultaneously identifying anomalies for predictive maintenance. Experimental evaluations show that our BO-STDP-SNN framework achieves notable reductions in both energy consumption by 27.8% and power requirements by 70%, while delivering superior accuracy in anomaly detection compared with CNN, RNN, and LSTM based baselines. These results demonstrate the potential of SNNs to enhance the efficiency and resilience of smart building systems, reduce operational costs, and support long-term sustainability through low-latency, event-driven intelligence. Full article

The increasing deployment of internet of things(IoT) systems across critical domains has broadened the threat landscape, and being the catalyst for a variety of security concerns, including very stealthy slow denial of service (slow DoS) attacks. These exploit the hypertext transfer protocol’s (HTTP) application-layer protocol to either close down service requests or degrade responsiveness while closely mimicking legitimate traffic. Current available datasets fail to capture the more stealthy operational profiles of slow DoS attacks or account for the presence of genuine slow nodes (SN), which are devices experiencing high latency. These can significantly degrade detection accuracy since slow DoS attacks closely emulate SN. This paper addresses these problems by synthesising a realistic HTTP slow DoS dataset derived from a live IoT network, that incorporates both stealth-tuned slow DoS traffic and legitimate SN traffic, with the three main slow DoS variants of slow GET, slow Read, and slow POST being critically evaluated under these network conditions. A limited packet capture (LPC) strategy is adopted which focuses on just two metadata attributes, namely packet length ($lp$) and packet inter-arrival time ($\mathrm{\Delta }t$). Using a resource lightweight decision tree classifier, the proposed model achieves over 96% accuracy while incurring minimal computational overheads. Experimental results in a live IoT network reveal the negative classification impact of including SN traffic, thereby underscoring the importance of modelling stealthy attacks and SN latency in any slow DoS detection framework. Finally, a MPerf (Modelling Performance) is presented which quantifies and balances detection accuracy against processing costs to facilitate scalable deployment of low-cost detection models in resource-constrained IoT networks. This represents a practical solution to improving IoT resilience against stealthy slow DoS attacks whilst pragmatically balancing the resource-constraints of IoT nodes. By analysing the impact of SN on detection performance, a robust reliable model has been developed which can both measure and fine tune the accuracy-efficiency nexus. Full article

Reactive astrogliosis and acidosis, common features of epileptogenic lesions, express a high level of astrocytic acid-sensing ion channel-1a (ASIC1a), a proton-gated cation channel and key mediator of responses to neuronal injury. This study investigates the role of astrocytic ASIC1a in cognitive impairment following epilepsy. Status epilepticus (SE) in C57/BL6 mice was induced using lithium–pilocarpine; the impact of ASIC1a on astrocytes was assessed using rAAV–ASIC1a–NC and rAAV–ASIC1a–shRNA, injected in the CA3 region of mice. Behavioral assessments were conducted using the Morris water maze (MWM). Western blotting and immunofluorescence were applied to evaluate ASIC1a and Gfap expression while analyzing intracellular calcium and extracellular glutamate (Glu) concentrations in primary cultured astrocytes isolated from the brains of 1 to 3-day-old mice and treated LPS. Results showed enhanced astrocyte proliferation and ASIC1a expression in the dentate gyrus of epileptic mice 7, 21, and 28 days post-SE (all p < 0.05). Escape latency in the MWM further suggested that ASIC1a regulates cognitive function in mice with chronic epilepsy. LPS stimulation in vitro mimicked inflammatory responses, increasing ASIC1a after 24 h, which increased the concentration of intracellular calcium and extracellular expression of Glu; inhibition of ASIC1a expression reversed this process. To sum up, these data confirm that astrocytic ASIC1a may facilitate cognitive dysfunction post-epilepsy, presenting a potential therapeutic target. Full article

Digital twin (DT) technology has become a cornerstone in the simulation and analysis of real-world systems, offering unparalleled insights into the lifecycle management of physical assets. By providing a real-time synchronized replica of the physical entity, DTs enable predictive maintenance, performance optimization, and lifecycle extension, which are pivotal for industries aiming for digital transformation. This paper presents a comprehensive comparative study of DT development of a robotic arm using two prominent simulation platforms: Unity and Gazebo. Unity, with its roots in the gaming industry, offers robust real-time rendering and a user-friendly interface, making it a versatile choice for various industries. Gazebo, traditionally used in robotics, provides detailed physics simulations and sensor data emulation, which is ideal for precise engineering applications. We explored the performance of both platforms in creating accurate and dynamic digital replicas. Through qualitative and quantitative analyses, this study evaluates each platform’s strengths and limitations. The study assesses these platforms across key performance metrics such as accuracy, latency, graphic quality, and integration with the Robot Operating System (ROS). The DTs were developed using a consistent physical setup and communication layer to ensure fair comparisons. The results indicate that Unity performed better in terms of accurately mimicking the robotic arm with lower latency, making it ideal for applications requiring high-fidelity visualizations and real-time responsiveness. However, Gazebo excels in its ease of ROS integration and cost-effectiveness, making it a suitable choice for smaller robotics and automation projects. This study conducts an empirical comparison of these platforms in terms of their performance in creating DTs of robotic arms which is not readily available. This paper aims to guide developers and organizations in selecting the appropriate platform for their DT initiatives, ensuring efficient resource utilization and optimal outcomes. Full article

Bovine alpha-herpesvirus 1 (BoHV-1) is a significant problem for the cattle industry, in part because the virus establishes latency, and stressful stimuli increase the incidence of reactivation from latency. Sensory neurons in trigeminal ganglia and unknown cells in pharyngeal tonsils are important
sites for latency. Reactivation from latency can lead to reproductive problems in pregnant cows, virus transmission to young calves, suppression of immune responses, and bacterial pneumonia. BoHV-1 is also a significant cofactor in bovine respiratory disease (BRD). Stress, as mimicked by the synthetic corticosteroid dexamethasone, reproducibly initiates reactivation from latency. Stress-mediated activation of the glucocorticoid receptor (GR) stimulates viral replication and transactivation of viral promoters that drive the expression of infected cell protein 0 (bICP0) and bICP4. Notably, GR and Krüppel-like factor 15 (KLF15) form a feed-forward transcription loop that cooperatively transactivates immediate early transcription unit 1 (IEtu1 promoter). Two  pioneer transcription factors, GR and KLF4, cooperatively transactivate the bICP0 early promoter. Pioneer transcription factors bind silent viral  heterochromatin, remodel chromatin, and activate gene expression. Thus, we
predict that these novel transcription factors mediate early stages of BoHV-1 reactivation from latency. Full article

Traditional frame-based cameras, despite their effectiveness and usage in computer vision, exhibit limitations such as high latency, low dynamic range, high power consumption, and motion blur. For two decades, researchers have explored neuromorphic cameras, which operate differently from traditional frame-based types, mimicking biological vision systems for enhanced data acquisition and spatio-temporal resolution. Each pixel asynchronously captures intensity changes in the scene above certain user-defined thresholds, and streams of events are captured. However, the distinct characteristics of these sensors mean that traditional computer vision methods are not directly applicable, necessitating the investigation of new approaches before being applied in real applications. This work aims to fill existing gaps in the literature by providing a survey and a discussion centered on the different application domains, differentiating between computer vision problems and whether solutions are better suited for or have been applied to a specific field. Moreover, an extensive discussion highlights the major achievements and challenges, in addition to the unique characteristics, of each application field. Full article

Our current understanding of HSV latency is based on a variety of clinical observations, and in vivo, ex vivo, and in vitro model systems, each with unique advantages and drawbacks. The criteria for authentically modeling HSV latency include the ability to easily manipulate host genetics and biological pathways, as well as mimicking the immune response and viral pathogenesis in human infections. Although realistically modeling HSV latency is necessary when choosing a model, the cost, time requirement, ethical constraints, and reagent availability are also equally important. Presently, there remains a pressing need for in vivo models that more closely recapitulate human HSV infection. While the current in vivo, ex vivo, and in vitro models used to study HSV latency have limitations, they provide further insights that add to our understanding of latency. In vivo models have shed light on natural infection routes and the interplay between the host immune response and the virus during latency, while in vitro models have been invaluable in elucidating molecular pathways involved in latency. Below, we review the relative advantages and disadvantages of current HSV models and highlight insights gained through each. Full article

Event cameras are bio-inspired devices that have revolutionized the acquisition of visual information by mimicking the neural architecture of the eye. These cameras respond asynchronously to changes in scene illumination at the pixel level, providing high-precision time information with low latency, typically in the order of microseconds. In this work, we experimentally evaluate an optical camera communication (OCC) link using an LED-based transmitter and an event camera as the receiver. We propose n-pulse modulation to encode data, adapting the system to the specific characteristics and operational principles of event cameras. The proposed scheme significantly reduces the demodulation complexity compared to other alternatives found in the literature. Furthermore, a set of experiments considering different camera bias sensitivities, encoding duty cycles, and LED radiant fluxes were carried out. The results showed that the BER performance was strongly dependent on the received optical power and the bias sensitivity. In addition, duty cycles between 0.3 and 0.7 at a 200 Hz transmission frequency presented the best performance, with a BER below $1.25\times {10}^{-4}$, which is under the forward error correction (FEC) limit. This work showcases the cutting-edge capabilities of event-camera-based OCC technology and contributes to the ongoing revolution in optical wireless communication (OWC). Full article

The human brain is arguably the most complex “machine” to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain’s structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain’s logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced—under pertinent simplifications—via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations. Full article

Although electromyography (EMG) remains the standard, researchers have begun using automated facial action coding system (FACS) software to evaluate spontaneous facial mimicry despite the lack of evidence of its validity. Using the facial EMG of the zygomaticus major (ZM) as a standard, we confirmed the detection of spontaneous facial mimicry in action unit 12 (AU12, lip corner puller) via an automated FACS. Participants were alternately presented with real-time model performance and prerecorded videos of dynamic facial expressions, while simultaneous ZM signal and frontal facial videos were acquired. Facial videos were estimated for AU12 using FaceReader, Py-Feat, and OpenFace. The automated FACS is less sensitive and less accurate than facial EMG, but AU12 mimicking responses were significantly correlated with ZM responses. All three software programs detected enhanced facial mimicry by live performances. The AU12 time series showed a roughly 100 to 300 ms latency relative to the ZM. Our results suggested that while the automated FACS could not replace facial EMG in mimicry detection, it could serve a purpose for large effect sizes. Researchers should be cautious with the automated FACS outputs, especially when studying clinical populations. In addition, developers should consider the EMG validation of AU estimation as a benchmark. Full article

Sleep disturbances, including insomnia and excessive daytime sleepiness, are highly prevalent in patients with ischemic stroke (IS), which severely impacts recovery and rehabilitation efforts. However, how IS induces sleep disturbances is unclear. Three experiments were performed on middle-aged C57BL/6J mice, instrumented with sleep recording electrodes and/or subjected to 1 h of middle cerebral artery (MCAO; Stroke group) or sham (Sham group) occlusion to induce IS. After 48 h of reperfusion (a) experiment 1 verified sensorimotor deficit (using Garcia scale) and infarction (using TTC staining) in this mouse model; (b) experiment 2 examined the effects of IS on the quality (sleep latency and NREM delta power) and quantity (duration) of sleep; and (c) experiment 3 determined the effects of IS on sleep homeostasis using sleep deprivation (SD) and recovery sleep (RS) paradigm. Stroke mice display (a) a significant correlation between sensorimotor deficit and cerebral infarction; (b) insomnia-like symptoms (increased sleep latency, reduced NREM duration and delta power) during the light (inactive) period and daytime sleepiness-like symptoms during the dark (active) period mimicking sleep in IS patients; and (c) impairments in the markers of sleep pressure (during SD) and sleep dissipation (during RS). Our results suggest that IS disrupts sleep homeostasis to cause sleep disturbances. Full article

Herpes simplex virus 1 (HSV-1) is wide-spread virus that triggers painful and recurrent infections, as herpes labialis, causing blister lesions on the lip. HSV-1 infection can be a lifelong condition starting from childhood due to the latency of the virus hidden in the trigeminal ganglia. Despite the use of antiviral treatments, there is not a resolutive cure for herpes. In our study, we tested blue light against HSV-1 in a neuronal cellular model, aimed at mimicking the neuronal tropism of HSV-1. Two laser protocols employing continuous wave and pulse modalities were delivered to infected cell cultures and to the virus alone. A significant reduction of viral replication was observed when the beam was directly applied to the virus, along with an increase in cell survival. Our findings, considering the limitation of the still-unknown mechanisms by which the blue light acts on the virus, suggested a potential use of photobiomodulation therapy for clinical applications against herpes labialis in pediatric patients. Full article

Despite being discovered and isolated more than one hundred years ago, tuberculosis (TB) remains a global public health concern arch. Our inability to eradicate this bacillus is strongly related with the growing resistance, low compliance to current drugs, and the capacity of the bacteria to coexist in a state of asymptomatic latency. This last state can be sustained for years or even decades, waiting for a breach in the immune system to become active again. Furthermore, most current therapies are not efficacious against this state, failing to completely clear the infection. Over the years, a series of experimental methods have been developed to mimic the latent state, currently used in drug discovery, both in vitro and in vivo. Most of these methods focus in one specific latency inducing factor, with only a few taking into consideration the complexity of the granuloma and the genomic and proteomic consequences of each physiological factor. A series of targets specifically involved in latency have been studied over the years with promising scaffolds being discovered and explored. Taking in account that solving the latency problem is one of the keys to eradicate the disease, herein we compile current therapies and diagnosis techniques, methods to mimic latency and new targets and compounds in the pipeline of drug discovery. Full article

Taking inspiration from biology to solve engineering problems using the organizing principles of biological neural computation is the aim of the field of neuromorphic engineering. This field has demonstrated success in sensor based applications (vision and audition) as well as in cognition and actuators. This paper is focused on mimicking the approaching detection functionality of the retina that is computed by one type of Retinal Ganglion Cell (RGC) and its application to robotics. These RGCs transmit action potentials when an expanding object is detected. In this work we compare the software and hardware logic FPGA implementations of this approaching function and the hardware latency when applied to robots, as an attention/reaction mechanism. The visual input for these cells comes from an asynchronous event-driven Dynamic Vision Sensor, which leads to an end-to-end event based processing system. The software model has been developed in Java, and computed with an average processing time per event of 370 ns on a NUC embedded computer. The output firing rate for an approaching object depends on the cell parameters that represent the needed number of input events to reach the firing threshold. For the hardware implementation, on a Spartan 6 FPGA, the processing time is reduced to 160 ns/event with the clock running at 50 MHz. The entropy has been calculated to demonstrate that the system is not totally deterministic in response to approaching objects because of several bioinspired characteristics. It has been measured that a Summit XL mobile robot can react to an approaching object in 90 ms, which can be used as an attentional mechanism. This is faster than similar event-based approaches in robotics and equivalent to human reaction latencies to visual stimulus. Full article