Signal Variations Research Articles

Discrete-Target Prosthesis Control Using Uncertainty-Aware Classification for Smooth and Efficient Gross Arm Movement.

Current control approaches for gross prosthetic arm movement mainly regulate movement over a continuous range of target poses. However, these methods suffer from output fluctuation caused by input signal variations during gross arm movements. Prosthesis control approaches with a finite number of discrete target poses can address this issue and reduce the complexity of the pose control process. However, it remains under-explored in the literature and suffers from the consequences of misclassifying the target poses. Here, we propose a novel Uncertainty-Aware Discrete-Target Prosthesis Control (UA-DPC) approach. This approach consists of (1) an uncertainty-aware classification scheme to reduce unintended pose switches caused by misclassifications, and (2) real-time trajectory planning that adjusts motion to be rapid or conservative based on low or high quantified uncertainty, respectively. By addressing the impact of misclassification, this approach facilitates more efficient and smooth movements. Human-in-the-loop experiments were conducted in a virtual reality environment with 12 non-disabled participants. The participants controlled a transhumeral prosthesis using three approaches: the proposed UA-DPC, a discrete-target approach based on a traditional off-the-shelf classifier, and a continuous-target approach. The results demonstrate the superior performance of UA-DPC, which provides more efficient task completion with fewer misclassification instances as well as smoother residual limb and prosthesis movement.

Feasibility of Diffusion-weighted Imaging (DWI) for Assessing Cerebrospinal Fluid Dynamics: DWI-fluidography in the Brains of Healthy Subjects.

The present study aimed to investigate whether diffusion-weighted imaging (DWI) can qualify and quantify cerebrospinal fluid (CSF) dynamics in the brains of healthy subjects. For this purpose, we developed new DWI-based fluidography and compared the CSF dynamics seen on the fluidography with two apparent diffusion coefficients obtained with different DWI signal models at anatomical spaces filled by CSF. DWI with multiple b values was performed for 10 subjects using a 7T MRI scanner. DWI-fluidography based on the DWI signal variations in different motion probing gradient directions was developed for visualizing the CSF dynamics voxel-by-voxel. DWI signals were measured using an ROI in the representative CSF-filled anatomical spaces in the brain. For the multiple DWI signals, the mono-exponential and kurtosis models were fitted and two kinds of apparent diffusion coefficients (ADCC and ADCK) were estimated in each space using the Gaussian and non-Gaussian diffusion models, respectively. DWI-fluidography could qualitatively represent the features of CSF dynamics in each anatomical space. ADCs indicated that the motions at the foramen of Monro, the cistern of the velum interpositum, the quadrigeminal cistern, the Sylvian cisterns, and the fourth ventricle were more drastic than those at the subarachnoid space and anterior horns of the lateral ventricle. Those results seen in ADCs were identical to the findings on DWI-fluidography. DWI-fluidography based on the features of DWI signals could show differences of CSF dynamics among anatomical spaces.

Stability and reproducibility of the amperometric sensors for oxygen concentration analysis in the nitrogen gas mixtures

The paper presents experimentally obtained results on the long-term stability tests of the solid electrolyte amperometric sensor output signal during the operation in the O2 + N2 gas mixtures. These data prove the stability and reproducibility of the output signal when measuring the oxygen concentration in air for 8000 hours. The output signal variation during the tests did not exceed ± 2 %. We performed four heating / cooling cycles of different durations, which did not influence either sensor integrity or operation characteristics. The structure of the solid electrolyte sensor and the solid electrolyte/electrode interfacial layer remained unchanged during the tests. Dynamic characteristics of the sensor, including the response time, were stable.

DNAzyme-mediated fluorescence signal variation of DNA-Ag nanoclusters and construction of an aptasensor for ATP.

DNA-templated silver nanoclusters (DNA-AgNCs) are novel nanomaterials with unique fluorescence characteristics. DNAzyme is a functional oligonucleotide that can catalyze the disruption of nucleic acid substrates. In this research, the effect of DNAzyme digestion on the fluorescence property of DNA-AgNCs was explored for the first time. A significant reduction in the fluorescence intensity of DNA-AgNCs after cleavage by DNAzyme was discovered. Further research found that the DNAzyme-catalyzed cleavage reduced the stability of DNA-AgNCs and led to their aggregation, accounting for a decline in fluorescence intensity up to 84%. Inspired by the above finding, a fluorescent aptasensor that integrates the benefits of DNA-AgNCs, exonuclease III (Exo III)-assisted signal amplification and DNAzyme was developed for sensitive detection of adenosine triphosphate (ATP). Under optimal conditions, the linear range was from 25 μM to 1000 μM and the detection limit was estimated to be 4.46 μM. Furthermore, this fluorescent aptasensor was effectively employed to quantify ATP levels in human serum samples, demonstrating its practicality in detecting ATP in biological matrices. The elucidation of DNAzyme-based fluorescence characteristic variation of DNA-AgNCs may provide insights into the interactions between DNAzyme and nanomaterials and has great prospects in the construction of fluorescent biosensors.

Use of MR signal intensity variations to highlight structures at risk along brain biopsy trajectories.

Postoperative intracerebral hemorrhages are significant complications following brain stereotactic biopsy. They can derive from anatomical structure (sulci, vessels) damage that is missed during stereotactic trajectory planning. In this study, the authors investigated the ability to detect contact between structures at risk and stereotactic trajectories using signal analysis from MRI obtained during clinical practice, with the aim to propose a visual tool to highlight areas with anatomical structures at risk of damage along the biopsy trajectory. The authors retrospectively analyzed actual stereotactic trajectories using intraoperative imaging (intraoperative 2D radiographs in the exploratory data set and intraoperative 3D scans in the confirmatory data set). The MR signal variation along each biopsy trajectory was matched with the patient's anatomy. In the exploratory data set (n = 154 patients), 32 contacts between the actual biopsy trajectory and an anatomical structure at risk were identified along 28 (18.2%) biopsy trajectories, corresponding to 8 preventable intracerebral hemorrhages. Variations of the mean derivative of the MR signal intensity were significantly different between trajectories with and without contact (the pathological threshold of the mean derivative of the MR signal intensity was defined as ± 0.030 arbitrary units; p < 0.0001), with a sensitivity of 89.3% and specificity of 74.6% to detect a contact. In the confirmatory data set (n = 73 patients), the sensitivity and specificity of the 0.030 threshold to detect a contact between the actual stereotactic trajectory and an anatomical structure at risk were 81.3% and 68.4%, respectively. Variations of the mean derivative of the MR signal intensity can be converted into a green/red color code along the planned biopsy trajectory to highlight anatomical structures at risk, which can help neurosurgeons during the surgical planning of stereotactic procedures.

Multi-spectroscopic study of electrochemically-formed oxide-derived gold electrodes.

Oxide-derived metals are produced by reducing an oxide precursor. These materials, including gold, have shown improved catalytic performance over many native metals. The origin of this improvement for gold is not yet understood. In this study, operando non-resonant sum frequency generation (SFG) and ex situ high-pressure X-ray photoelectron spectroscopy (HP-XPS) have been employed to investigate electrochemically-formed oxide-derived gold (OD-Au) from polycrystalline gold surfaces. A range of different oxidizing conditions were used to form OD-Au in acidic aqueous medium (H3PO4, pH = 1). Our electrochemical data after OD-Au is generated suggest that the surface is metallic gold, however SFG signal variations indicate the presence of subsurface gold oxide remnants between the metallic gold surface layer and bulk gold. The HP-XPS results suggest that this subsurface gold oxide could be in the form of Au2O3 or Au(OH)3. Furthermore, the SFG measurements show that with reducing electrochemical treatments the original gold metallic state can be restored, meaning the subsurface gold oxide is released. This work demonstrates that remnants of gold oxide persist beneath the topmost gold layer when the OD-Au is created, potentially facilitating the understanding of the improved catalytic properties of OD-Au.

Weighted and Well-Balanced Nonlinear TV-Based Time-Dependent Model for Image Denoising

The partial differential equation (PDE)-based models are widely used to remove additive Gaussian white noise and preserve edges, and one of the most widely used methods is the total variation denoising algorithm. Total variation (TV) denoising algorithm-based time-dependent models have seen considerable success in the field of image-denoising and edge detection. TV denoising algorithm is based on that signals with spurious detail have a high total variation and reduction of unwanted signals to achieve noise-free images. It is a constrained optimization-type algorithm. The Lagrange multiplier and gradient descent method are used to solve the TV algorithm to reach the PDE-based time dependent model. To eliminate additive noise and preserve edges, we investigate a class of weighted time-dependent model in this study. The proposed method is investigated in a well-balanced flow form that extends the time-dependent model with an adaptive fidelity element. Adaptive function is fusing into the regularization term of the classical time-dependent model which successfully enhances the intensity of the regularizer function. We maintain the ability of the time-dependent model without any oscillation effects. Furthermore, we want to prove the viscosity solution of our weighted and well balanced time-dependent model, demonstrating its existence and uniqueness. The finite difference method is applied to discretize the nonlinear time-dependent models. The numerical results are expressed as a statistic known as the peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). Numerical experiments demonstrate that the proposed model yields good performance compared with the previous time-dependent model.

Unusual Raman Enhancement Effect of Ultrathin Copper Sulfide.

In surface-enhanced Raman spectroscopy (SERS), 2D materials are explored as substrates owing to their chemical stability and reproducibility. However, they exhibit lower enhancement factors (EFs) compared to noble metal-based SERS substrates. This study demonstrates the application of ultrathin covellite copper sulfide (CuS) as a cost-effective SERS substrate with a high EF value of 7.2×104 . The CuS substrate is readily synthesized by sulfurizing a Cu thin film at room temperature, exhibiting a Raman signal enhancement comparable to that of an Au noble metal substrate of similar thickness. Furthermore, computational simulations using the density functional theory are employed and time-resolved photoluminescence measurements are performed to investigate the enhancement mechanisms. The results indicate that polar covalent bonds (Cu─S) and strong interlayer interactions in the ultrathin CuS substrate increase the probability of charge transfer between the analyte molecules and the CuS surface, thereby producing enhanced SERS signals. The CuS SERS substrate demonstrates the selective detection of various dye molecules, including rhodamine 6G, methylene blue, and safranine O. Furthermore, the simplicity of CuS synthesis facilitates large-scale production of SERS substrates with high spatial uniformity, exhibiting a signal variation of less than 5% on a 4-inch wafer.

A three-modal fluorescent sensor harnessing diverse luminescent mechanisms for the purpose of segmented Baijiu identification

The classification and verification of segmented Baijiu hold significant importance as they profoundly influence the blending and overall quality of the Baijiu. Our scholarly investigation yielded a fluorescent sensor with three luminescent modes by integrating Tb3+ and RHB into UiO-66. The interplay between carboxyl-containing compounds and RHB/Tb@TLU-2 orchestrates a harmonious molecular association, where the convergence of carboxyl groups with Tb3+ yields a resonating impact on the antenna effect of BDC-SO3-. Furthermore, the acidity and alkalinity of reactants induced a charge transfer interaction between BDC-NH2 and Zr4+ and led to structural changes in RHB/Tb@TLU-2, resulting in observable fluorescence signal variations across the three emission centers. The sensor array successfully identified eight organic acids, achieving an impressive 97.5 % accuracy in discerning segmented Baijiu samples from four Baijiu pits. This meticulous methodology prioritizes simplicity, swiftness, and effectiveness, paving the path for comprehensive segmented Baijiu analysis in the esteemed realm of Brewing production.

Temporal and Spectral Models as Correlates to Auditory-Perceptual Judgments of Overall Severity and Listener Comfort in Tracheoesophageal Voice.

This study pursued two objectives: (1) to determine the potential association between listener (n = 51) judgments of 20 male tracheoesophageal speaker samples for two auditory-perceptual dimensions of voice, overall severity (OS) and listener comfort (LC); and (2) to assess the temporal and spectral acoustic correlates for these auditory-perceptual dimensions. Three separate correlation analyses were performed to evaluate the association between OS and LC. First, scores of OS and LC from all listeners were pooled together, and then the correlation between OS and LC was computed. Second, scores of OS and LC were averaged over all listeners to derive a single estimate of OS and LC for each TE speaker sample; the correlation between the average OS and LC was then computed. Third, listener-to-listener variability in the association between OS and LC was evaluated by computing the correlation between OS and LC scores from each listener across all TE samples. Finally, two stepwise multiple regression models were created to relate the average LC score to spectral and temporal variation in the acoustic signal. While the pooled OS and LC scores had a moderate positive correlation (r = 0.66, p < 0.00001), the averaged OS and LC exhibited a near perfect positive correlation (r = 0.99, p < 0.00001). The significant differences between the pooled and averaged scores were explained by significant listener-to-listener variability in the association between OS and LC. OS and LC scores from 5 listeners had non-significant correlations, 10 had moderate correlations (r < 0.7), 35 listeners had high correlations (0.7 < r < 0.9), and 1 listener had a very high correlation (r < 0.9 < 1). Finally, the acoustic models created based on the spectral and temporal variations in the signal were able to account for 87.7% and 61.8% of variation in the average LC score. The strong correlations between OS and LC suggest that LC may, in fact, provide a more comprehensive auditory-perceptual surrogate for the voice quality of TE speakers. Although OS and LC are distinct conceptual dimensions, LC appears to have the advantage of assessing the social impact and potential communication disability that may exist in interactions between TE speakers and listeners.

Ammonia Toxicity and Associated Protein Oxidation: A Single-Cell Surface Enhanced Raman Spectroscopy Study.

Ammonia (NH3) is a commonly used industrial chemical to which exposure at high concentrations can result in severe skin damage. Moreover, high levels of ammonia in the human body can lead to hyperammonemia conditions and enhanced cancer metabolism. In this work, the toxicity mechanism of NH3 has been studied against human dermal fibroblast (HDF) cells using surface-enhanced Raman spectroscopy (SERS). For this purpose, gold nanoparticles of size 50 nm have been prepared and used as probes for Raman signal enhancement, after being internalized inside HDF cells. Following the exposure to ammonia, HDF cells showed a significant variation in the protein ternary structure's signals, demonstrating their denaturation and oxidation process, together with early signs of apoptosis. Meaningful changes were observed especially in the Raman vibrations of sulfur-containing amino acids (cysteine and methionine) together with aromatic residues. Fluorescence microscopy revealed the formation of reactive oxygen and nitrogen species in cells, which confirmed their stressed condition and to whom the causes of protein degradation can be attributed. These findings can provide new insights into the mechanism of ammonia toxicity and protein oxidation at a single-cell level, demonstrating the high potential of the SERS technique in investigating the cellular response to toxic compounds.

Inversion of the Chiroptical Responses of Chiral Gold Nanoparticles with a Gold Film.

The deposition of chiral nanoparticles (NPs) onto various substrates is crucial for the fabrication of high-density photonic devices. Understanding the interaction of chiral light and chiral NPs supported on substrates is essential for developing optical sensors and modulators. However, the chiroptical responses of plasmonic chiral NPs on substrates have remained elusive. Here we provide an important understanding of the correlation between the substrate material and the chiroptical response. The scattering dissymmetry factors of individual chiral Au nanocubes are inverted and enhanced with a gold film. Qualitative theories are proposed to analyze the observed variations in the chiroptical signals of chiral NPs on different substrates. Our results offer an encouraging route for modulating and amplifying the chiroptical signals in the use of chiral NPs in light control, light-based quantum technologies, and sensing.

Quantitative method for evaluating corrosion defects and residual bearing capacity of bridge structural steel via MMM technique

The quantitative assessment of corrosion defects and residual bearing capacity is essential to ensure the safe operation of bridge steel structures. In this study, Q345qD steel was selected as the research object, and specimens with different corrosion levels were prepared using the electrochemical corrosion method. Metal magnetic memory (MMM) detection experiments and uniaxial tensile experiments on corroded steel plates were performed. A three-dimensional magnetic charge model of a corroded steel plate considering the end effect was built to explain the variation of the MMM signals. The finite element simulation results of the corroded steel plates were consistent with the experimental and theoretical results. Based on experimental and finite element data, the quantitative relationship between the corrosion depth h and the magnetic characteristic parameter Hn(z) is established with a relative error within 15 %. A quantitative relationship between the nominal tensile strength σu and the magnetic characteristic parameter Hn(z) is proposed with an evaluation accuracy within ±5 MPa. It is suggested that the proposed evaluation method is feasible in quantitatively evaluating the defects and residual tensile strength of corroded steel plates using the MMM technique.

Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system.

Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices. In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms. Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage. This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.

A novel method for distinction heart rate variability during meditation using LSTM recurrent neural networks based on visibility graph

Detection of dynamical complexity variations in heart rate signals during meditation practice can be considered as an important criterion for further understanding cardiovascular functions. This paper introduces a computational framework for categorizing the heart rate variability (HRV) differences of subjects during meditation compared to before meditation using information from heart rate signals. Heart rate data are obtained from the Physionet database. Because of the dynamic and nonlinear nature of heart rate signals, visibility graph features were utilized for analysis. The recurrent neural network (RNN) based on long short-term memory (LSTM) was used to classify subjects into two different states before and during meditation relying on its performance in terms of the VG-based features. The results show that the proposed approach managed to categorize the heart rate signals into two groups: before and during meditation, with a high accuracy of 99.25%. Moreover, the proposed approach enjoys the potential to extremely ease the adoption of the running situation of the cardiac system and physiological functions by disclosing remarkable differences between the HRV reactions of the two mentioned states.

Lithium Isotope Determination in Spodumene by fs‐LA‐MC‐ICP‐MS with Non‐Matrix‐Matched Calibration: Insights into ICP Operating Conditions and Data Evaluation

Lithium isotope measurement in spodumene by femtosecond LA‐MC‐ICP‐MS was investigated and the influence of plasma operating conditions and data reduction strategy on accuracy and precision was studied. It was found that “hot” plasma conditions led to an unstable baseline signal and substantial variations in the Li isotope ratios. By adding a constant amount of water to the carrier gas, a stable baseline was achieved and isotope ratios became reproducible and were consistent with data from solution‐based MC‐ICP‐MS. The resulting biases were within ± 0.51‰ and the reproducibility was better than 0.09‰. Comparison of Li isotope ratios resulting from different data evaluation schemes showed that the mean of the transient intensity ratios, integration of the entire or a user‐defined period of the ion signals resulted in good agreement with solution‐based data, while linear regression underestimated the Li isotope ratios. It was also found that “cool” plasma operation produced a stable baseline signal, but the Li isotope results were biased by up to ‐4.3%, irrespective of water introduction and the data evaluation scheme. With the optimised “hot‐wet” conditions, the Li isotope ratios in eleven spodumene materials were determined which successfully allowed distinguishing regional deposits and partially veins of the available samples.

Direct Determination of Ca, K, and Mg in Soy Leaf Samples Using Laser-Induced Breakdown Spectroscopy.

This study was dedicated to developing analytical methods for determining macronutrients (Ca, K, and Mg) in soy leaf samples with and without petioles. The study's primary purpose was to present Laser-induced breakdown spectroscopy (LIBS) as a viable alternative for directly analyzing leaf samples using chemometric tools to interpret the data obtained. The instrumental condition chosen for LIBS was 70 mJ of laser pulse energy, 1.0 µs of delay time, and 100 µm of spot size, which was applied to 896 samples: 305 of soy without petioles and 591 of soy with petioles. The reference values of the analytes for the proposition of calibration models were obtained using inductively coupled plasma optical emission spectroscopy (ICP-OES) technique. Twelve normalization modes and two calibration strategies were tested to minimize signal variations and sample matrix microheterogeneity. The following were studied: multivariate calibration using partial least squares and univariate calibration using the area and height of several selected emission lines. The notable normalization mode for most models was the Euclidean norm. No analyte showed promising results for univariate calibrations. Micronutrients, P and S, were also tested, and no multivariate models presented satisfactory results. The models obtained for Ca, K, and Mg showed good results. The standard error of calibration ranged from 2.3 g/kg for Ca in soy leaves without petioles with two latent variables to 5.0 g/kg for K in soy leaves with petioles with two latent variables.

Chemiluminescent plate assay of microRNA-155 coupled with catalytic hairpin assembly with oligonucleotide release (CHAOR)

A heterogeneous sensitive microRNA-155 assay based on a new isothermal amplification method, called catalytic hairpin assembly with oligonucleotide release (CHAOR), was developed. The principle of CHAOR was studied by non-denaturing electrophoresis. To detect the amplification product, a polyperoxidase-streptavidin conjugate (molar ratio 1:80) and an enhanced chemiluminescence reaction were used, which made it possible to increase assay sensitivity. The detection limit of microRNA-155 assay was 0.4 pM. The coefficient of variation of the chemiluminescent signal, formed upon the heterogeneous determination of miRNA-155, was less than 12 % within the working range. The efficiency of CHAOR as an amplification method was similar to that of traditional CHA, as miRNA-155 assays based on CHAOR and CHA had similar analytical parameters. In addition, the proposed assay was highly specific. Contrary to traditional CHA, CHAOR, one of whose products is a single-stranded oligonucleotide, can be used in analytical methods based on cascade amplification.

Breath-hold BOLD fMRI without CO2 sampling enables estimation of venous cerebral blood volume: potential use in normalization of stimulus-evoked BOLD fMRI data

BOLD fMRI signal has been used in conjunction with vasodilatory stimulation as a marker of cerebrovascular reactivity (CVR): the relative change in cerebral blood flow (CBF) arising from a unit change in the vasodilatory stimulus. Using numerical simulations, we demonstrate that the variability in the relative BOLD signal change induced by vasodilation is strongly influenced by the variability in deoxyhemoglobin-containing cerebral blood volume (CBV), as this source of variability is likely to be more prominent than that of CVR. It may, therefore, be more appropriate to describe the relative BOLD signal change induced by an isometabolic vasodilation as a proxy of deoxygenated CBV (CBVdHb) rather than CVR. With this in mind, a new method was implemented to map a marker of CBVdHb, termed BOLD-CBV, based on the normalization of voxel-wise BOLD signal variation by an estimate of the intravascular venous BOLD signal from voxels filled with venous blood. The intravascular venous BOLD signal variation, recorded during repeated breath-holding, was extracted from the superior sagittal sinus in a cohort of 27 healthy volunteers and used as a regressor across the whole brain, yielding maps of BOLD-CBV. In the same cohort, we demonstrated the potential use of BOLD-CBV for the normalization of stimulus-evoked BOLD fMRI by comparing group-level BOLD fMRI responses to a visuomotor learning task with and without the inclusion of voxel-wise vascular covariates of BOLD-CBV and the BOLD signal change per mmHg variation in end-tidal carbon dioxide (BOLD-CVR). The empirical measure of BOLD-CBV accounted for more between-subject variability in the motor task-induced BOLD responses than BOLD-CVR estimated from end-tidal carbon dioxide recordings. The new method can potentially increase the power of group fMRI studies by including a measure of vascular characteristics and has the strong practical advantage of not requiring experimental measurement of end-tidal carbon dioxide, unlike traditional methods to estimate BOLD-CVR. It also more closely represents a specific physiological characteristic of brain vasculature than BOLD-CVR, namely blood volume.

Underwater Biomimetic Lateral Line Sensor Based on Triboelectric Nanogenerator for Dynamic Pressure Monitoring and Trajectory Perception.

Developing desirable sensors is crucial for underwater perceptions and operations. The perceiving organs of marine creatures have greatly evolved to react accurately and promptly underwater. Inspired by the fish lateral line, this study proposes a triboelectric dynamic pressure sensor for underwater perception. The biomimetic lateral line sensor (BLLS) has high sensitivity to the disturbance amplitude/frequency, good adaptability to underwater environments and (relative) low cost. The sensors are deployed at the bottom of the test basin to perceive various moving objects, such as a robotic fish, robotic seal, etc. By analyzing the electrical signal of the sensor, the motion parameters of the objects passed over can be obtained. By monitoring signal variations across multiple sensors, the ability to sense different disturbance movement trajectories, including linear and angular trajectories, is achievable. The study will prove significant in forming an unconventional underwater perceiving method, which can back-up the sonic/optical sensors when are impaired in complex underwater environments.