Signal Distribution Research Articles

Latent alignment in deep learning models for EEG decoding.

Brain-computer interfaces (BCIs) face a significant challenge due to variability in EEG signals across individuals. While recent approaches have focused on standardizing input signal distributions, we propose that aligning distributions in the deep learning model's feature space is more effective for classification.
Approach: We introduce the Latent Alignment method, which won the Benchmarks for EEG Transfer Learning (BEETL) competition. This method can be formulated as a deep set architecture applied to trials from a given subject, introducing deep sets to EEG decoding for the first time. We compare Latent Alignment to recent statistical domain adaptation techniques, carefully considering class-discriminative artifacts and the impact of class distributions on classification performance.
Main Results: Our experiments across motor imagery, sleep stage classification, and P300 event-related potential tasks validate Latent Alignment's effectiveness. We identify a trade-off between improved classification accuracy when alignment is performed at later modeling stages and increased susceptibility to class imbalance in the trial set used for statistical computation.
Significance: Latent Alignment offers consistent improvements to subject-independent deep learning models for EEG decoding when relevant practical considerations are addressed. This work advances our understanding of statistical alignment techniques in EEG decoding and provides insights for their effective implementation in real-world BCI applications, potentially facilitating broader use of BCIs in healthcare, assistive technologies, and beyond.
The model code is available at https://github.com/StylianosBakas/LatentAlignment.

On the partial volume effect in magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is an emerging tomographic 'hot spot' imaging modality with potential to visualize superparamagnetic iron oxide nanoparticle tracer distributions with high sensitivity and quantitative accuracy. MPI shares many similarities with positron emission tomography (PET), where the partial volume effect (PVE) can result in signal under- and over-quantification due to spill-over of signal arising from limited resolution. While the PVE has been alluded to in the MPI literature it has not been previously studied nor characterized. The objective of this study was to systematically characterize this PVE in MPI.Approach.This contribution characterizes the PVE using models of varying size and shape filled with a uniform concentration of tracer. The effect of object size on signal distribution was analyzed after application of a new image post-processing filter.Main results.As object size increased, signal distribution increased to a maximum signal value independent of object geometry and proportional to tracer concentration. Furthermore, for small objects with characteristic dimensions below the resolution of the tracer at the scanning conditions used, signal suppression was observed. These results are consistent with foundational observations of PVE in PET, suggesting that approaches to overcome the PVE in PET may be applicable to MPI.Significance.This finding has significant impact on the MPI field by demonstrating the presence of the PVE phenomenon that can directly influence imaging results.

Tissue-to-fluid water-exchange imaging using T2-selective saturation labeling.

To propose and implement a new method to study water exchange between brain tissue and fluids. An MLEV T2-preparation combined with a cerebrospinal fluid (CSF) nulling inversion recovery was implemented in combination with an ultralong-echo time (TE) three-dimensional fast spin-echo readout. To handle systematic imperfections and isolate the exchange signal, T2-prepared images were subtracted from one of two control images. The first control turned off the T2 preparation and adjusted inversion timing to correct for relaxation. The second control used the same T2 preparation but shifted in time. Preparations were implemented on a 3T scanner and tested in 14 healthy volunteers. We evaluated the exchange signal magnitude and distribution, as well as robustness against B1 imperfection and intrasession reproducibility. We also compared the signal to that measured with ultralong-TE arterial spin labeling, another suggested marker of water exchange. Initial experiments using the T2-preparation off control demonstrated a detectable exchange signal especially in the choroid plexus, but with substantial residual signal in CSF spaces, suggesting imperfect subtraction of non-exchanging spins. When using the time shifted control, we greatly reduced subtraction errors. Signal was consistently measured in the choroid plexus and at the boundaries between cortex and CSF with much higher signal-to-noise ratio and spatial resolution than ultralong-TE arterial spin labeling. The measured water exchange distribution appears consistent with the localization of aquaporin channels at the CSF boundary. Because aquaporin activity may reflect CSF production and glymphatic clearance, our method may provide a noninvasive marker of these functions.

Multimodal information density is highest in question beginnings, and early entropy is associated with fewer but longer visual signals

ABSTRACT When engaged in spoken conversation, speakers convey meaning using both speech and visual signals, such as facial expressions and manual gestures. An important question is how information is distributed in utterances during face-to-face interaction when information from visual signals is also present. In a corpus of casual Dutch face-to-face conversations, we focus on spoken questions in particular because they occur frequently, thus constituting core building blocks of conversation. We quantified information density (i.e. lexical entropy and surprisal) and the number and relative duration of facial and manual signals. We tested whether lexical information density or the number of visual signals differed between the first and last halves of questions, as well as whether the number of visual signals occurring in the less-predictable portion of a question was associated with the lexical information density of the same portion of the question in a systematic manner. We found that information density, as well as number of visual signals, were higher in the first half of questions, and specifically lexical entropy was associated with fewer, but longer visual signals. The multimodal front-loading of questions and the complementary distribution of visual signals and high entropy words in Dutch casual face-to-face conversations may have implications for the parallel processes of utterance comprehension and response planning during turn-taking.

A Statistical Characterization of Dynamic Brain Functional Connectivity.

This study examined the statistical underpinnings of dynamic functional connectivity in mental disorders, using resting-state fMRI signals. Notably, there has been an absence of research demonstrating the non-stationarity of the empirical probability distribution of functional connectivity. This gap has prompted debate on the existence of dynamic functional connectivity, leading skeptics to question its relevance and the reliability of research findings. Our aim was to fill this gap by conducting a comprehensive empirical distribution analysis of functional connectivity, using Pearson's correlation as a measure. We conducted our analysis on a set of preprocessed resting-state fMRI samples obtained from 186 subjects selected from the UCLA Consortium for Neuropsychiatric Phenomics dataset. Departing from conventional methods that aggregated signals over voxels within a region of interest, our approach leveraged individual voxel signals. Specifically, our approach offered a precise characterization of the empirical probability distribution of resting-state fMRI signals by evaluating the temporal variations and non-stationarity in dynamic functional connectivity, as measured by Pearson's correlation. Our study investigated functional connectivity patterns across 49 regions of interest, comparing healthy control subjects with patients diagnosed with ADHD, bipolar disorder, and schizophrenia. Our analysis revealed that (1) the empirical distribution of the correlation coefficient exhibited non-stationarity, (2) the beta distribution was an accurate approximation of the exact correlation coefficient distribution, and (3) the empirical distribution of means derived from the fitted beta distributions, unraveled distinctive dynamic functional connectivity patterns with potential as biomarkers associated with different mental disorders. A key contribution of our study was the presentation of the first comprehensive empirical distribution analysis of dynamic functional connectivity, thus providing compelling evidence for its existence. Overall, our study presented an innovative statistical approach that advances our understanding of the dynamic nature of functional connectivity patterns derived from resting-state fMRI. Our examination of the empirical distribution of dynamic functional connectivity provided solid evidence supporting its existence. The distinctive dynamic functional connectivity patterns we identified across various mental disorders hold promise as potential biomarkers for further development.

Interferon signaling is enhanced by ATR inhibition in glioblastoma cells irradiated with X-rays, protons or carbon ions.

Interferon (IFN) signaling plays an important role in antitumor immune responses. Inhibitors of the DNA damage response, such as ATR inhibitors, can increase IFN signaling upon conventional radiotherapy with X-rays. However, it is not known whether such inhibitors also enhance IFN signaling after irradiation with high linear energy transfer (LET) particles. Human glioblastoma U-251 and T98G cells were irradiated with X-rays, protons (LET: 4.8 and 41.9keV/µm) and carbon ions (LET: 28 and 73keV/µm), with and without ATR inhibitor (VE-822) or ATM inhibitor (AZD1390). DNA damage signaling and cell cycle distribution were analyzed by immunoblotting and flow cytometry, and radiosensitivity was assessed by clonogenic survival assay. IFN-β secretion was measured by ELISA, and STAT1 activation was examined by immunoblotting. High-LET protons and carbon ions caused stronger activation of the DNA damage response compared to low-LET protons and X-rays at similar radiation doses. G2 checkpoint arrest was abrogated by the ATR inhibitor and prolonged by the ATM inhibitor after all radiation types. The inhibitors increased radiosensitivity, as measured after X- and carbon ion irradiation. ATR inhibition increased IFN signaling following both low-LET and high-LET irradiation. ATM inhibition also increased IFN signaling, but to a lesser extent. Notably, both cell lines secreted significantly more IFN-β when the inhibitors were combined with high-LET compared to low-LET irradiation. These findings indicate that DNA damage response inhibitors can enhance IFN signaling following X-, proton and carbon ion irradiation, with a strong positive dependency on LET.

Quantitative non-destructive evaluation analysis and characterisation of microcrack detection based on carbon steel weld under alternating current field measurement

ABSTRACT Nondestructive testing of microcrack in welding seams is a key and challenging task in nuclear industry to evaluate material properties. The real magnetic characteristics of welds and stress redistribution have not been considered in the previous numerical simulation of welds and base metal materials, which leads to conservative calculation values and fails to meet actual engineering expectations. In this work, based on the difference between weld and base material structure, a non-uniform weld magnetic charge model was established and the stress distribution characteristics of weld cracks were analysed. The characteristics and gradient distribution of magnetic signals at weld and weld crack are calculated. The work quantitatively studied and characterised the influence of the offset weld position of the detection probe, the distance between the crack and the probe, the crack cluster, and the crack at the interface between the weld and the base metal on the detection signal. The microcrack detection of base metal, weld, and interface under various working conditions was compared and analysed. The difficulty of the AC electromagnetic method in weld inspection is solved, and the weld microcrack identification is realised. The results provide theoretical guidance for the identification of potential weld microcracks in spent fuel pools.

A physically modelled selection function for compact binary mergers in the LIGO-Virgo O3 run and beyond

Abstract Despite the observation of nearly 100 compact binary coalescence (CBC) events up to the end of the Advanced gravitational-wave (GW) detectors’ third observing run (O3), there remain fundamental open questions regarding their astrophysical formation mechanisms and environments. Population analysis should yield insights into these questions, but requires careful control of uncertainties and biases. GW observations have a strong selection bias: this is due first to the dependence of the signal amplitude on the source’s (intrinsic and extrinsic) parameters, and second to the complicated nature of detector noise and of current detection methods. In this work, we introduce a new physically-motivated model of the sensitivity of GW searches for CBC events, aimed at enhancing the accuracy and efficiency of population reconstructions. In contrast to current methods which rely on re-weighting simulated signals (injections) via importance sampling, we model the probability of detection of binary black hole (BBH) mergers as a smooth, analytic function of source masses, orbit-aligned spins, and distance, fitted to accurately match injection results. The estimate can thus be used for population models whose signal distribution over parameter space differs significantly from the injection distribution. Our method has already been used in population studies such as reconstructing the BBH merger rate dependence on redshift.

Impact of Combined Neuromuscular Electrical Stimulation (Comb-NMES) on Glucose Signaling and Muscle Myofiber Distribution in a Patient with Acute Spinal Cord Injury and Lower Motor Neuron Lesion

Introduction: This case report examines the impact of a novel combined neuromuscular electrical stimulation (Comb-NMES) regimen on muscle glucose signaling, fiber type distribution, and metabolic function in a patient with acute spinal cord injury (SCI) and lower motor neuron lesions (LMNLs). Case Report: A 32-year-old male with complete T9 SCI underwent a ten-session Comb-NMES intervention targeting the quadriceps. Muscle biopsies and blood samples were analyzed pre- and post-intervention to evaluate changes in muscle fiber types, key metabolic proteins, fasting insulin, glucose, and lipid profiles. Results: The intervention led to a 74.7% and 28.2% reduction in fasting insulin and glucose, respectively. Muscle analysis showed significant increases in CaMK II, Hexokinase II, and IRS-1, indicating improved glucose metabolism. Conclusion: Comb-NMES training markedly improved metabolic control and muscle glucose metabolism in a patient with acute SCI and LMNLs. Enhanced insulin sensitivity and glucose utilization were evidenced by upregulated metabolic proteins, which suggests that Comb-NMES is a promising intervention for improving muscle and metabolic health in SCI patients with LMNLs. Further studies are needed to confirm these benefits and explore the long-term effects.

INFORMATION DESIGN IN ALLOCATION WITH COSTLY VERIFICATION

AbstractWe study information design in a single‐agent allocation problem with costly verification, where the principal has a positive reservation value. The agent learns privately a signal about the principal's allocation value, drawn from a distribution controlled by an information designer. Given the signal distribution, the principal designs a mechanism to maximize her net value. We show that the agent‐optimal information pools high values at a signal just worth verification, whereas the principal‐optimal information features full disclosure. Moreover, any agent‐optimal information is principal‐worst, despite their partially aligned interests and the absence of transfers.

The ECOLANG Multimodal Corpus of adult-child and adult-adult Language

Communication comprises a wealth of multimodal signals (e.g., gestures, eye gaze, intonation) in addition to speech and there is a growing interest in the study of multimodal language by psychologists, linguists, neuroscientists and computer scientists. The ECOLANG corpus provides audiovisual recordings and ELAN annotations of multimodal behaviours (speech transcription, gesture, object manipulation, and eye gaze) by British and American English-speaking adults engaged in semi-naturalistic conversation with their child (N = 38, children 3-4 years old, face-blurred) or a familiar adult (N = 31). Speakers were asked to talk about objects to their interlocutors. We further manipulated whether the objects were familiar or novel to the interlocutor and whether the objects could be seen and manipulated (present or absent) during the conversation. These conditions reflect common interaction scenarios in real-world communication. Thus, ECOLANG provides ecologically-valid data about the distribution and co-occurrence of multimodal signals across these conditions for cognitive scientists and neuroscientists interested in addressing questions concerning real-world language acquisition, production and comprehension, and for computer scientists to develop multimodal language models and more human-like artificial agents.

Ppb-Level Photoacoustic Detection of Chloroform Using Four-Microphone Array.

The photoacoustic spectroscopy (PAS) system commonly enhances the efficiency of optical-acoustic-electrical energy conversion by increasing the laser power, optimizing the resonance characteristics of the photoacoustic cell (PAC), and improving the sensitivity of acoustic sensors. However, conventional systems using a single-microphone or a dual-microphone differential setup for point sampling of the photoacoustic signal fail to account for its spatial distribution, leading to a loss of spatial gain. Drawing on microphone array theory derived from sonar technology, this study, for the first time, presents a PAS sensing system based on a four-microphone array, which is applied to detect chloroform gas. The microphones are positioned at 90° intervals around the PAC resonance chamber wall, enhancing the spatial sampling rate of the signals. A digital phase-locked algorithm demodulates the combined signals from the four microphones into the concentration data. Experimental results show that, compared to a single-microphone system, the four-microphone array system increases sensitivity by a factor of 4, doubles the signal-to-noise ratio, and achieves a minimum detection limit of 69 ppb, demonstrating a significant improvement in sensitivity by capturing the spatial distribution of PA signals.

Metabolic-Immune Suppression Mediated by the SIRT1-CX3CL1 Axis Induces Functional Enhancement of Regulatory T Cells in Colorectal Carcinoma.

Metabolic reprogramming of tumor cells dynamically reshapes the distribution of nutrients and signals in the tumor microenvironment (TME), affecting intercellular interactions and resulting in metabolic immune suppression. Increased glucose uptake and metabolism are characteristic of many tumors. Meanwhile, the progression of colorectal carcinoma (CRC) relies on lipid metabolism. Therefore, investigating the role of glucolipid metabolic reprogramming on tumor immunity contributes to identifying new targets for immune suppression intervention in CRC. Our previous work demonstrated that SIRT1 is the hub gene involved in glucolipid metabolic conversion in CRC. Here, it is found that upregulated SIRT1 in CRC cells increases Treg functionality by promoting the secretion of CX3CL1. The CX3CL1-CX3CR1 signaling activated transcription factors SATB1 and BTG2, promoting the differentiation of TCF7+ Treg cells into functionally enhanced TNFRSF9+ Treg cells. Multiplex immunofluorescence (mIHC) analysis of a CRC tissue microarray confirmed the promoting effect of CX3CL1 on Treg infiltration. Additionally, the therapeutic efficacy of CX3CR1 inhibitor monotherapy and combination therapy is validated with the PD-1 antibody in the humanized subcutaneous CRC mouse model. This study elucidates a potential mechanism that metabolic reprogramming of cancer cells collaborates with subsequent immunosuppression to promote CRC progression.

Study on frequency spectrum characteristics and attenuation law of buried PE pipeline leakage infrasound signal

This article addresses the insufficient application of infrasound in the detection of nonmetallic pipeline leaks. Through a combination of numerical simulations and experimental verification, we analyze the sound field distribution, spectral characteristics, and attenuation laws of infrasound leakage signals under varying pressures and leakage apertures. The results indicate that the amplitude of the sound pressure level of the leakage infrasound signal consistently exceeds that of the non-leakage sound pressure level. Additionally, the sound pressure level exhibits rapid attenuation with increasing frequency, pressure, and leakage aperture, particularly in proximity to the leakage hole.

A review of NEST models for liquid xenon and an exhaustive comparison with other approaches

This paper discusses the microphysical simulation of interactions in liquid xenon, the active detector medium in many leading rare-event searches for new physics, and describes experimental observables useful for understanding detector performance. The scintillation and ionization yield distributions for signal and background are presented using the Noble Element Simulation Technique (NEST), a toolkit based on experimental data and simple empirical formulas, which mimic previous microphysics modeling but are guided by data. The NEST models for light and charge production as a function of the particle type, energy, and electric field are reviewed, along with models for energy resolution and final pulse areas. NEST is compared with other models or sets of models and validated against real data, with several specific examples drawn from XENON, ZEPLIN, LUX, LZ, PandaX, and table-top experiments used for calibrations.

An Upgrade of Radio Frequency Reference Generation and Distribution Modules for FLASH2020+

Free-Electron Laser in Hamburg (FLASH), first launched in 2005, was the first free-electron laser that provided ultrashort radiation pulses in extreme ultraviolet and soft X-ray spectral range. In 2017, it was decided to improve the existing FLASH facility within the FLASH2020+ project, which led to upgrading the existing linac with variable gap tunable undulators in the FLASH1 line and refurbishing two cryomodules to achieve a beam energy increase to 1.35 GeV. It was also a perfect opportunity to completely redesign and rebuild the radio frequency (RF) phase reference generation and distribution system. This paper presents the design and parameters of new, custom-made phase reference signal generation and distribution modules, successfully installed in FLASH. These are the main oscillator, the RF distribution module, and the frequency conversion modules. The new instrumentation presents a significant improvement in terms of RF reference signal parameters, state-of-the-art phase noise performance (an improvement in the total jitter of the 1.3 GHz RF signal from 55.9 fs to 10.7 fs in the integration range from 10 Hz to 1 MHz), module compactness (size reduction from three fully occupied rack cabinets to four 19″ modules only), and serviceability. The presented Main Oscillator system design is foreseen for easy modifications, making it suitable for applications in other accelerator facilities or hardware platforms.

In Vivo Head-to-Head Comparison of [18F]GTP1 with [18F]MK-6240 and [18F]PI-2620 in Alzheimer Disease.

Alzheimer disease (AD) is characterized by the accumulation of tau neurofibrillary tangles that can be labeled with PET tracers. Multiple tau PET tracers have been used in clinical studies, including [18F]GTP1, [18F]PI-2620, and [18F]MK-6240. Standardized harmonization scales for comparing tau PET signals across tracers are currently under development and can be informed by comparisons of signals between tracers in both target and off-target regions of the brain. Methods: We conducted a head-to-head study comparing [18F]GTP1 with [18F]PI-2620 and [18F]MK-6240 in terms of dynamic range, magnitude of uptake, and correlation between tracers in participants with normal cognition and prodromal to mild AD. Results: [18F]GTP1 exhibited retention patterns that correlated with [18F]PI-2620 and [18F]MK-6240 for all Braak regions (except Braak II). Differences in tracer binding in AD target regions were relatively small, and off-target binding profiles were unique to each tracer. Conclusion: Our findings indicate that [18F]GTP1, [18F]PI-2620, and [18F]MK-6240 display similar uptake patterns in AD patients, suggesting that they detect the same tau pathology. However, the tracer-specific off-target signal distribution may impact their direct comparability, and for some use cases, tracer-specific considerations should be taken into account in the development of a standardized harmonization scale for tau PET.

Comparing different segments in shut-in pressure signals: new insights into frequency range and energy distribution

Comparing different segments in shut-in pressure signals: new insights into frequency range and energy distribution

Direct quantification of the plasmon dephasing time in ensembles of gold nanorods through two-dimensional electronic spectroscopy.

In this study, we used two-dimensional electronic spectroscopy to examine the early femtosecond dynamics of suspensions of colloidal gold nanorods with different aspect ratios. In all samples, the signal distribution in the 2D maps at this timescale shows a distinctive dispersive behavior, which can be explained by the interference between the exciting field and the field produced on the nanoparticle's surface by the collective motion of electrons when the plasmon is excited. Studying this interference effect, which is active only until the plasmon has been dephased, allows for a direct estimation of the dephasing time of the plasmon of an ensemble of colloidal particles. Our findings provide insight into the fundamental behavior of plasmonic states and highlight the potential of two-dimensional electronic spectroscopy in uncovering ultrafast and coherent optical phenomena at the nanoscale.

Time-Delay Measurement Method Based on the Frequency-Spectrum Distribution of Laser Intensity or Phase Noise Signal

Time-Delay Measurement Method Based on the Frequency-Spectrum Distribution of Laser Intensity or Phase Noise Signal