Significant Stimuli Research Articles

Architecture of Molecular Logic Gates: From Design to Application as Optical Detection Devices

Three decades after A. P. de Silva’s seminal paper introduced the concept of logic gates at the molecular level, the field of molecular logic gates (MLGs) has witnessed significant advancements. MLGs are devices designed to perform logical operations, utilizing one or more physical or chemical stimulus signals (inputs) to generate an output response. Notably, MLGs have found diverse applications, with optical detection of analytes emerging as a notable evolution of traditional chemosensors. Organic synthesis methods are pivotal in crafting molecular architectures tailored as optical devices capable of analyte detection through logical functions. This review delves into the fundamental aspects and physical–chemical properties of MLGs, with a particular emphasis on synthetic strategies driving their design.

Do microsaccades vary with discriminability around the visual field?

Microsaccades-tiny fixational eye movements-improve discriminability in high-acuity tasks in the foveola. To investigate whether they help compensate for low discriminability at the perifovea, we examined microsaccade characteristics relative to the adult visual performance field, which is characterized by two perceptual asymmetries: horizontal-vertical anisotropy (better discrimination along the horizontal than vertical meridian) and vertical meridian asymmetry (better discrimination along the lower than upper vertical meridian). We investigated whether and to what extent microsaccade directionality varies when stimuli are at isoeccentric locations along the cardinals under conditions of heterogeneous discriminability (Experiment 1) and homogeneous discriminability, equated by adjusting stimulus contrast (Experiment 2). Participants performed a two-alternative forced-choice orientation discrimination task. In both experiments, performance was better on trials without microsaccades between ready signal onset and stimulus offset than on trials with microsaccades. Across the trial sequence, the microsaccade rate and directional pattern were similar across locations. Our results indicate that microsaccades were similar regardless of stimulus discriminability and target location, except during the response period-once the stimuli were no longer present and target location no longer uncertain-when microsaccades were biased toward the target location. Thus, this study reveals that microsaccades do not flexibly adapt as a function of varying discriminability in a basic visual task around the visual field.

Adaptive Gait Acquisition through Learning Dynamic Stimulus Instinct of Bipedal Robot.

Standard alternating leg motions serve as the foundation for simple bipedal gaits, and the effectiveness of the fixed stimulus signal has been proved in recent studies. However, in order to address perturbations and imbalances, robots require more dynamic gaits. In this paper, we introduce dynamic stimulus signals together with a bipedal locomotion policy into reinforcement learning (RL). Through the learned stimulus frequency policy, we induce the bipedal robot to obtain both three-dimensional (3D) locomotion and an adaptive gait under disturbance without relying on an explicit and model-based gait in both the training stage and deployment. In addition, a set of specialized reward functions focusing on reliable frequency reflections is used in our framework to ensure correspondence between locomotion features and the dynamic stimulus. Moreover, we demonstrate efficient sim-to-real transfer, making a bipedal robot called BITeno achieve robust locomotion and disturbance resistance, even in extreme situations of foot sliding in the real world. In detail, under a sudden change in torso velocity of -1.2 m/s in 0.65 s, the recovery time is within 1.5-2.0 s.

Relapse after intermittent access to cocaine: Discriminative cues more effectively trigger drug seeking than do conditioned cues.

When people with drug addiction encounter cues associated with drug use, this can trigger cravings and relapse. These cues can include conditioned stimuli (CSs) signaling drug delivery and discriminative stimuli (DSs) signaling drug availability. Compared to CS effects, DS effects are less explored in preclinical studies on cue-induced relapse. We compared CS and DS effects on reward seeking following abstinence from intermittent-access cocaine (or sucrose) self-administration. During 15-20 intermittent-access sessions, rats self-administered i.v. cocaine or sucrose pellets paired with a light-tone CS. Cocaine/sucrose was available for 5-min (signalled by a light; DS+) and unavailable for 25min (signalled by different lighting conditions; DS-), and this cycled for 4h/session. Following abstinence, we measured cocaine/sucrose seeking under extinction triggered by CS and DS presentation, and instrumental responding reinforced by these cues. Across intermittent-access sessions, rats increased lever pressing for cocaine or sucrose during DS+ periods and decreased responding during DS- periods. On days 2 and 21 of abstinence, only presentation of the DS+ or DS+ and CS combined elicited increased cocaine/sucrose-seeking behaviour (i.e., increased active lever presses). Presenting the DS- alongside the DS+ suppressed the increased cocaine-seeking behaviour otherwise produced by the DS+ . Finally, on day 21 of abstinence, rats showed equivalent levels of lever pressing reinforced by the DS+ , CS and by the DS+ and CS combined, suggesting comparable conditioned reinforcing value. After intermittent self-administration, cocaine-associated DSs and CSs acquire similar conditioned reinforcing properties, but DSs more effectively trigger increases in drug seeking.

Abstract PO4-28-01: Molecular profiling revealed activation of cytokine signaling and suppression of lipid metabolism as the hallmarks of doxorubicin-resistant TNBC

Abstract Background: Chemotherapeutic resistance observed in triple-negative breast cancer (TNBC) poses a substantial clinical hurdle, emphasizing the imperative to enhance our comprehension of the underlying mechanisms with the aim of potentially mitigating or reversing this phenomenon. Methods: Doxorubicin-resistant (DoxR) TNBC models (MDA-MB-231 and BT-549) were established through continuous exposure to increasing concentrations of doxorubicin. RNA-Seq was conducted using the Illumina platform, while bioinformatics analyses were performed using CLC genomics workbench 20.2 and iDEP.91. Colony forming unit (CFU), flow cytometry and fluorescent microscopy were used to assess cell proliferation, cell cycle distribution, and cell death, respectively. Western blotting was used to confirm the expression and phosphorylation of protein targets. Ingenuity pathway analysis (IPA) and STRING database v 11.5 were used for network and pathway analyses, while Kaplan-Meier Plotter was used for survival analyses. Results: Herein, we provide a comprehensive functional and transcriptomic characterization of DoxR TNBC models, revealing multiple affected signaling networks and functional categories. The defense and immune response, response to stress, response to cytokine and external stimulus and cytokine-mediated signaling pathways were among the top activated, while suppression of cholesterol, sterol and lipid biosynthesis and metabolism were the hallmarks of DoxR cells. Upstream regulator analysis revealed IL-1B cytokine network activation in DoxR cells. Concordantly, disease and function analysis on the downregulated genes in DoxR TNBC cells revealed the most pronounced enrichment in pathways associated with cell invasion, colony formation, as well as lipid and carbohydrate metabolism. A number of upregulated genes were validated by qRT-PCR, which correlated with unfavorable overall survival (OS). Functionally, DoxR cells exhibited remarkable suppression of cell proliferation, as evidenced by diminished CFU potential, sphere formation and growth under 3D organoid culture, and cell migration. Although DoxR TNBC cells exhibited a slow cell cycling under normal culture conditions, wild-type TNBC cells exhibited remarkable arrest in the G2M phase when exposed to doxorubicin, which was less evident in DoxR TNBC models, suggesting DoxR cells undergo a quiescent state in response to chemotherapeutic challenge. In agreement with those data, increased phosphorylation of CHK2 and p53 proteins was remarkable in DoxR cells, thus promoting cellular dormancy. Conclusion: Our data revealed cellular dormancy and suppression of lipid metabolism as the hallmark associated with doxorubicin-resistant TNBC cells. On the other hand, cytokine signaling and stress response were the most enriched functional categories in DoxR TNBC cells. Therapeutic targeting to reverse the quiescent state or suppression of cytokine signaling networks could enhance the efficacy of doxorubicin-based therapeutics. Citation Format: Vishnubalaji Radhakrishnan, Nehad Alajez. Molecular profiling revealed activation of cytokine signaling and suppression of lipid metabolism as the hallmarks of doxorubicin-resistant TNBC [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-28-01.

Changes in the intestinal microbiota of multiple myeloma patients living in high‑altitude and cold regions analyzed using 16s rRNA high‑throughput sequencing.

Multiple myeloma (MM) is a plasma cell clonal disease and these plasma cells can survive in the gut. The intestinal microbiota is a complex ecosystem and its dysfunction can release persistent stimulus signals that trigger genetic mutations and clonal evolution in the gut. The present study analyzed the intestinal microbiota in fecal samples of MM patients in high-altitude and cold regions of China using 16s rRNA sequencing and analyzed significantly enriched species at the phylum and genus levels. Although no significant difference in the alpha diversity was observed between the MM and control groups, a significant difference was noted in the beta diversity. A total of 15 significant differential bacteria at the genus level were found between the two groups, among which Bacteroides, Streptococcus, Lactobacillus and Alistipes were significantly enriched in the MM group. The present study also constructed a disease diagnosis model using Random Forest analysis and verified its accuracy using receiver operating characteristic analysis. In addition, using correlation analysis, it demonstrated that the composition of the intestinal microbiota in patients with MM was associated with complement levels. Notably, the present study predicted that the signaling and metabolic pathways of the intestinal microbiota affected MM progression through Kyoto Encyclopedia of Genes and Genomes functional analysis. The present study provides a new approach for the prevention and treatment of MM, in which the intestinal microbiota may become a novel therapeutic target for MM.

Adaptive TS-ANFIS neuro-fuzzy controller based single phase shunt active power filter to mitigate sensitive power quality issues in IoT devices

The emergence of Internet of Things (IoT) offers numerous functions, such as intelligent sensor integration, remote sensing, and high-speed data transmission, which have found widespread applications in the smart industry and commercial applications. The associative nonlinear effects of a variety of undesirable power quality concerns were resolved by using harmonic mitigation in nonlinear loads and high-performance converters were built on power electronics in conventional systems. Among other performance objectives, total harmonic (THD) distortion analysis and higher order harmonics mitigation is given main concern in smart electronics equipment. This paper proposes an approach to minimize higher-order harmonics due to nonlinear load disturbances in smart IoT devices using the Takagi–Sugeno (TS) Neuro Fuzzy (TS-ANFIS) supervised shunt APF. The proposed work elicits the novel adaptive harmonic mitigation technique in hybrid IoT embedded systems to protect from malfunctioning and to deal with the uncertainty of harmonic signal stimuli in sensitive sensors-based IoT systems. Hysteresis current control is used for the ignition of reference signal under uncertainty of harmonic stimuli. The single phase shunt APF is used to mitigate higher-order harmonics from supply mains, while the implementation of TS-ANFIS supervises the controller action to generate a trigger signal for adequate single phase APF gate excitation. The higher order harmonic current data set is used for error deviation for training neural networks and adaptive control. The estimated adoption of the neuron's empirical weight reduces the total THD to a significant reduction rate of 72.8 % to 0.78 %. The control mechanism is feasible for a range of smart IoT systems to adhere to the standard of 519 (IEEE).

Identification of PTPN12 Phosphatase as a Novel Negative Regulator of Hippo Pathway Effectors YAP/TAZ in Breast Cancer.

The Hippo pathway plays crucial roles in governing various biological processes during tumorigenesis and metastasis. Within this pathway, upstream signaling stimuli activate a core kinase cascade, involving MST1/2 and LATS1/2, that subsequently phosphorylates and inhibits the transcriptional co-activators YAP and its paralog TAZ. This inhibition modulates the transcriptional regulation of downstream target genes, impacting cell proliferation, migration, and death. Despite the acknowledged significance of protein kinases in the Hippo pathway, the regulatory influence of protein phosphatases remains largely unexplored. In this study, we conducted the first gain-of-functional screen for protein tyrosine phosphatases (PTPs) regulating the Hippo pathway. Utilizing a LATS kinase biosensor (LATS-BS), a YAP/TAZ activity reporter (STBS-Luc), and a comprehensive PTP library, we identified numerous novel PTPs that play regulatory roles in the Hippo pathway. Subsequent experiments validated PTPN12, a master regulator of oncogenic receptor tyrosine kinases (RTKs), as a previously unrecognized negative regulator of the Hippo pathway effectors, oncogenic YAP/TAZ, influencing breast cancer cell proliferation and migration. In summary, our findings offer valuable insights into the roles of PTPs in the Hippo signaling pathway, significantly contributing to our understanding of breast cancer biology and potential therapeutic strategies.

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers.

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

Comparative transcriptome analysis reveals nicotine metabolism is a critical component for enhancing stress response intensity of innate immunity system in tobacco.

The pyridine alkaloid nicotine acts as one of best-studied plant resistant traits in tobacco. Previous research has shown that NtERF199 and NtERF189, acting as master regulators within the NIC1 and NIC2 locus, quantitatively contribute to nicotine accumulation levels in N. tabacum. Genome editing-created Nic1(Nterf199) and Nic2 (Nterf189) double mutant provides an ideal platform for precisely dissecting the defensive role of nicotine and the connection between the nicotine biosynthetic pathway with other putative metabolic networks. Taking this advantage, we performed a comparative transcriptomic analysis to reevaluate the potential physiological and metabolic changes in response to nicotine synthesis defect by comparing the nic1nic2 and NIC1NIC2 plants. Our findings revealed that nicotine reduction could systematically diminishes the expression intensities of genes associated with stimulus perception, signal transduction and regulation, as well as secondary metabolic flux. Consequently, this global expression reduction might compromise tobacco adaptions to environmental fitness, herbivore resistances, and plant growth and development. The up-regulation of a novel set of stress-responsive and metabolic pathway genes might signify a newly established metabolic reprogramming to tradeoff the detrimental effect of nicotine loss. These results offer additional compelling evidence regarding nicotine's critical defensive role in nature and highlights the tight link between nicotine biosynthesis and gene expression levels of quantitative resistance-related genes for better environmental adaptation.

Effects of outcome revaluation on attentional prioritisation of reward-related stimuli.

Stimuli associated with rewards can acquire the ability to capture our attention independently of our goals and intentions. Here, we examined whether attentional prioritisation of reward-related cues is sensitive to changes in the value of the reward itself. To this end, we incorporated an instructed outcome devaluation (Experiment 1a), "super-valuation" (Experiment 1b), or value switch (Experiment 2) into a visual search task, using eye-tracking to examine attentional prioritisation of stimuli signalling high- and low-value rewards. In Experiments 1a and 1b, we found that prioritisation of high- and low-value stimuli was insensitive to devaluation of a previously high-value outcome, and super-valuation of a previously low-value outcome, even when participants were provided with further experience of receiving that outcome. In Experiment 2, following a value-switch manipulation, we found that prioritisation of a high-value stimulus could not be overcome with knowledge of the new values of outcomes alone. Only when provided with further experience of receiving the outcomes did patterns of attentional prioritisation of high- and low-value stimuli switch, in line with the updated values of the outcomes they signalled. To reconcile these findings, we suggest that participants were motivated to engage in effortful updating of attentional control settings when there was a relative difference between reward values at test (Experiment 2) but that previous settings were allowed to persist when both outcomes had the same value at test (Experiments 1a and 1b). These findings provide a novel framework to further understand the role of cognitive control in driving reward-modulated attention and behaviour.

Auditory Steady-State Evoked Potentials in Post Traumatic Stress Disorder: Introduction of a Potential Biomarker.

Objective: The lack of steady-state evoked potential (SSEP) studies on post-traumatic stress disorder (PTSD) has led to undiscovered useful information about the pathophysiology of the disorder. Thus, we explored SSEP patterns in PTSD patients during a stop-signal task to disclose possible impairments in these informative brain potentials. Method : 25 adult patients with PTSD and 25 healthy adults participated in this research. Subjects were assessed with electroencephalography while the tone signal stimuli at 40 Hz were used to evoke SSEPs and subjects performed a stop-signal task. The amplitude and phase of SSEPs were then computed in different brain regions. The subjects were also evaluated using the Mississippi PTSD questionnaire. Appropriate statistical methods such as repeated measure ANOVA were used to compare the two groups, and the correlation between SSEPs and clinical symptoms was assessed using Pearson correlation analysis. Results: Patients showed considerably poorer performance in the cognitive task (P < 0.01), accompanied by raised SSEP phase and amplitude in the anterior and midline regions compared to healthy controls (P < 0.05). The Mississippi total score was positively correlated with the SSEP amplitude in the midline region (r = 0.62, P < 0.05). Furthermore, based on ROC analysis, the SSEP amplitude in the midline region provided an excellent AUC value (AUC = 0.850) for distinguishing patients with PTSD from normal subjects. Conclusion: Current findings suggest that abnormalities in the anterior and midline cortical neural networks are involved in the pathophysiology of PTSD. Importantly, midline abnormalities may provide a clinically-relevant measure for researchers wishing to assess the use of biomarkers for early diagnosis of PTSD as well as to evaluate new therapeutic and management approaches in the treatment of PTSD.

Non-invasive in-utero quantification of vascular reactivity in human placenta.

Placental vascular reactivity (PlVR) indicates the ability of the placental vasculature to match blood supply to fetal demand. Many pregnancy disorders alter the characteristics of PlVR, resulting in suboptimal oxygen delivery, although current understanding is limited by the lack of non-invasive, repeatable methods to measure PlVR in utero. Our objective was to quantify PlVR by measuring the placental response to transient changes in maternal carbon dioxide (CO2) using blood-oxygen-level-dependent (BOLD) magnetic resonance imaging (MRI). We hypothesized that PlVR will increase with gestational age to meet the changing demands of a growing fetus, and that PlVR will be driven by a maternal response to changes in CO2 concentration. This was a cross-sectional study of 35 women with a healthy singleton pregnancy, of whom 31 were included in the analysis. The median gestational age was 32.6 (range, 22.6-38.4) weeks. Pregnant women were instructed to follow audiovisual breathing cues during a MRI scan. Maternal end-tidal CO2 (EtCO2) was measured concurrently with resting placental BOLD MRI for a total of 7-8 min. Preprocessing of magnetic resonance images consisted of manual delineation of placental anatomy and motion correction. In each placental voxel, vascular reactivity was computed using a coherence-weighted general linear model between MRI signal and EtCO2 stimulus. Global PlVR was computed as the mean of voxel-wise PlVR values across the placenta. PlVR, quantified by the placental response to induced, transient changes in maternal CO2, was consistently measured in utero using BOLD MRI. PlVR increased non-linearly with advancing gestational age (P < 0.001) and was higher on the fetal side of the placenta. PlVR was associated positively with fetal brain volume after accounting for gestational age. PlVR did not show any significant associations with maternal characteristics. We present, for the first time, a non-invasive paradigm to quantify PlVR in ongoing human pregnancies without the use of exogenous gases or contrast agents. Our findings suggest that PlVR is driven by a fetal response to changes in maternal CO2. Ease of translation to the clinical setting makes PlVR a promising biomarker for the identification and management of high-risk pregnancies. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Ionic plasmon-polariton in application to neurosignaling.

The diffusive ion current is insufficient to explain the fast saltatory conduction observed in myelinated axons and in pain-sensing C fibers in the human nervous system, where the stimulus signal exhibits a velocity two orders of magnitude greater than the upper limit of ion diffusion velocity, even when the diffusion is accelerated by myelin, as in the discrete cable model including the Hodgkin-Huxley mechanism. The agreement with observations has been achieved in a wave-type model of stimulus signal kinetics via synchronized ion local density oscillations propagating as a wave in axons periodically corrugated by myelin segments in myelinated axons, or by periodically distributed rafts with clusters of Na^{+} channels in C fibers. The resulting so-called plasmon-polariton model for saltatory conduction reveals also the specific role of myelin, which is different from what was previously thought. This can be important for identifying a new target for the future treatment of demyelination diseases.

Emotion recognition with EEG-based brain-computer interfaces: a systematic literature review

Electroencephalography (EEG)-based Brain-Computer Interface (BCI) systems for emotion recognition have the potential to assist the enrichment of human–computer interaction with implicit information since they can enable understanding of the cognitive and emotional activities of humans. Therefore, these systems have become an important research topic today. This study aims to present trends and gaps on this topic by performing a systematic literature review based on the 216 published scientific literature gathered from various databases including ACM, IEEE Xplore, PubMed, Science Direct, and Web of Science from 2016 to 2020. This review gives an overview of all the components of EEG based BCI system from the signal stimulus module which includes the employed device, signal stimuli, and data processing modality, to the signal processing module which includes signal acquisition, pre-processing, feature extraction, feature selection, classification algorithms, and performance evaluation. Thus, this study provides an overview of all components of an EEG-based BCI system for emotion recognition and examines the available evidence in a clear, concise, and systematic way. In addition, the findings are aimed to inform researchers about the issues on what are research trends and the gaps in this field and guide them in their research directions.

The effects of preceding sound on psychoacoustic tuning curves measured in simultaneous and forward masking

The medial olivocochlear reflex (MOCR) decreases the gain of the cochlear active process in response to sound. We have used psychoacoustic techniques to show behavioral effects of gain reduction, which could be consistent with the MOCR. We have used paradigms understood to measure frequency selectivity and the input/output function at the level of the cochlea using stimuli (masker and signal) that should be too short to evoke the MOCR. A precursor sound is then presented before these stimuli to evoke the MOCR. Our most recent studies have used forward masking, to avoid the complicating effects of suppression. The current study was designed to examine the effects of suppression, by comparing the effects of a precursor on frequency selectivity measured using forward masking and simultaneous masking. Psychoacoustic tuning curves were measured using simultaneous and forward masking, with and without a precursor. This allowed us to measure the change in tuning with and without the presence of suppression. Broadband precursors and tonal precursors at masker frequencies were measured because previous studies have shown broadening with broadband precursors and sharpening when tonal maskers are the precursors. Results will be discussed in the context of current understanding of the MOCR.

Waste Cotton-Derived Fiber-Based Thermoelectric Aerogel for Wearable and Self-Powered Temperature–Compression Strain Dual-Parameter Sensing

The rapid development of the global economy and population growth are accompanied by the production of numerous waste textiles. This leads to a waste of limited resources and serious environmental pollution problems caused by improper disposal. The rational recycling of wasted textiles and their transformation into high-value-added emerging products, such as smart wearable devices, is fascinating. Here, we propose a novel roadmap for turning waste cotton fabrics into three-dimensional elastic fiber-based thermoelectric aerogels by a one-step lyophilization process with decoupled self-powered temperature–compression strain dual-parameter sensing properties. The thermoelectric aerogel exhibits a fast compression response time of 0.2 s, a relatively high Seebeck coefficient of 43 μV·K−1, and an ultralow thermal conductivity of less than 0.04 W·m−1·K−1. The cross-linking of trimethoxy(methyl)silane (MTMS) and cellulose endowed the aerogel with excellent elasticity, allowing it to be used as a compressive strain sensor for guessing games and facial expression recognition. In addition, based on the thermoelectric effect, the aerogel can perform temperature detection and differentiation in self-powered mode with the output thermal voltage as the stimulus signal. Furthermore, the wearable system, prepared by connecting the aerogel-prepared array device with a wireless transmission module, allows for temperature alerts in a mobile phone application without signal interference due to the compressive strains generated during gripping. Hence, our strategy is significant for reducing global environmental pollution and provides a revelatory path for transforming waste textiles into high-value-added smart wearable devices.

Optimization of Patient Specific Stimulus for Deep Brain Stimulation Using Spatially Distributed Neural Sources

Deep brain stimulation (DBS) becomes the therapy of choice in the later stages of Parkinson's disease (PD) due to the medication's side effects. For effective DBS treatment, it is important to have a controlled dosage of DBS. DBS dosage is administered using the tuning of electrical parameters of the stimulus signal. Since this tuning process is tedious, time-consuming and patient-specific, there is a need to study the properties of DBS stimulation signal for proper dose administration. We propose a simulation framework to define an optimized DBS stimulus using Electroencephalogram (EEG) signals. The objective is to provide a simulation environment inspired by a realistic brain. The framework uses spiking neurons in a reservoir modelled after real brain anatomy and is trained using a biologically inspired spike-time-dependent-plasticity learning algorithm. This reservoir is initially set to OFF-medication state and forced to drift to the ON-medication state by optimizing the synaptic changes. In later testing, the generalization of this framework is verified with EEG-inverse solutions like sLORETA which utilize time-domain EEG signals to estimate neural activations. The stimulus signal is generated by accumulating the variations in synaptic weights in the neural reservoir in the target brain region. We analyze this signal and show that the application of this signal as stimulus results in decreased <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> -band power in subthalamic-nucleus local-field-potential compared to OFF-medication local-field-potential without stimulation. Using SIM4LIFE simulation software, we show that the simulation increases chaos in the local-field-potential of subthalamic-nucleus neurons and shows that neuron weight variations follow specific trajectories in reconstructed state-space. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Impact Statement:</i> The primary contribution of this paper is to present a framework that utilizes the association between target signals of any modality (EEG, MEG, fMRI) and their respective post-synaptic activations to optimize the deep brain stimulation (DBS) stimulus signal. This framework combines coordinates of neural sources with patients' EEG data to optimize the stimulus of DBS. While defining this framework, we initially proposed the usage of spiking neurons in a biological neural reservoir to estimate the internal activations for given EEG data. Later, we extended our testing to incorporate inverse EEG algorithms that estimate neural activations directly from EEG data without any conversion. This is a novel framework, and there are not many examples available in the literature for similar work. This framework is in its early stages, yet it provides an adequate starting point for an under-explored area of study. This framework can potentially evolve into a useful tool for doctors in assisting the DBS tuning process before the surgical procedure. This framework's accuracy depends on the underlying technique to estimate the neural activations from the target signals (like EEG etc.).

Graphene-based flexible temperature/pressure dual-mode sensor as a finger sleeve for robotic arms

The design of sensors with multiple sensing functions has become a new challenge to meet the measurement of multiple stimulus signals in people's productive lives. In this article, a sandwich type dual-mode flexible sensor is designed to achieve simultaneous detection of pressure and temperature by constructing different sensing model structures. The dual-mode flexible sensor essentially comprises graphene, melamine sponge and Ag nanoparticle ink. Graphene is coated on the melamine sponge to form a 3D conductive sponge using the dip coating method, while Ag nanoparticle ink is printed on PET to form a serpentine electrode through inkjet printing technology. The sensor has high sensitivity (1.937 × 10−4 Pa−1, R2 = 0.953), a wide operating range (from 0 to 40 kPa), speedy response time (248 ms) and remarkable stability in pressure measurement. It also shows high linearity (TCR = 1.035 × 10−3 °C−1, R2 = 0.987) from 10 to 60 °C and is pressure insensitive in temperature measurement. Experimental results show that the different sensing mechanisms and structural configurations allow the sensors to detect pressure and temperature signals independently or simultaneously without signal coupling. The fabricated sensor has applications in health monitoring, rehabilitation therapy, human-machine interaction and intelligent prosthetics.

Enhancing plasticity in optoelectronic artificial synapses: A pathway to efficient neuromorphic computing

The continuous growth in artificial intelligence and high-performance computing has necessitated the development of efficient optoelectronic artificial synapses crucial for neuromorphic computing (NC). Ga2O3 is an emerging wide-bandgap semiconductor with high deep ultraviolet absorption, tunable persistent photoconductivity, and excellent stability toward electric fields, making it a promising component for optoelectronic artificial synapses. Currently reported Ga2O3 optoelectronic artificial synapses often suffer from complex fabrication processes and potential room for improvement due to plasticity. To address the issue of low device plasticity and practical application scenarios, we present an amorphous Ga2O3 (α-GaOx) flexible optoelectronic artificial synapse. This synapse modulates light stimulus signals using electron/oxygen vacancies and optical stimulation and operates as a visual storage device for information processing. We investigate the improvement of the optoelectronic synapses' plasticity by controlling the number of oxygen vacancies via a plasma treatment method and demonstrate its effective application in a three-layer backpropagation neural network for handwritten digit classification. Under the same stimulus conditions, the synaptic weight of samples treated with Ar plasma exhibits a higher rate of change, with the current levels increasing by 2–3 orders of magnitude, achieving greater plasticity. The improved optoelectronic synapses achieved an accuracy of 93.34%/94%, demonstrating their potential as efficient computing solutions and insights for future applications in NC chips.