Signal Window Research Articles

Global forecasts in reservoir computers.

A reservoir computer is a machine learning model that can be used to predict the future state(s) of time-dependent processes, e.g., dynamical systems. In practice, data in the form of an input-signal are fed into the reservoir. The trained reservoir is then used to predict the future state of this signal. We develop a new method for not only predicting the future dynamics of the input-signal but also the future dynamics starting at an arbitrary initial condition of a system. The systems we consider are the Lorenz, Rossler, and Thomas systems restricted to their attractors. This method, which creates a global forecast, still uses only a single input-signal to train the reservoir but breaks the signal into many smaller windowed signals. We examine how well this windowed method is able to forecast the dynamics of a system starting at an arbitrary point on a system's attractor and compare this to the standard method without windows. We find that the standard method has almost no ability to forecast anything but the original input-signal while the windowed method can capture the dynamics starting at most points on an attractor with significant accuracy.

Evaluating Fixed Single-Point Parameters When Applied to Vestibular Evoked Myogenic Potentials: The Effect of Single Point and Signal Window.

Several studies have applied a common objective detection algorithm (fixed single point [ Fsp ]) for detection of the vestibular evoked myogenic potential (VEMP). However, fundamental parameters of Fsp , such as establishing the location and duration of a signal window, have not been examined. In addition, Fsp criterion values used for response detection have not been established for cervical VEMPs (cVEMPs) or ocular VEMPs (oVEMPs). The purpose of this article was to investigate the effect of various single points and signal windows on Fsp , as well as determining Fsp criteria to determine response presence for cVEMP and oVEMP in a group of young healthy participants. Twenty young healthy adults under the age of 30 and with no history of hearing or balance concerns were enrolled in the study protocol. Air-conducted cVEMPs and oVEMPs were evoked using 500 Hz tone bursts at 123 dB pSPL recorded at a fixed electromyography activation of 50 µV for cVEMPs and 35° gaze angle for oVEMPs. Responses were analyzed off-line using visual and objective detection. Fsp was applied to cVEMPs and oVEMPs using a range of single points and signal windows. Noise variance was lowest for cVEMPs at the latency of P1, and for oVEMPs noise variance was not significantly different across the single-point latencies. On average, extending the length of the signal window lowered the Fsp value in cVEMPs and oVEMPs. An Fsp value of 2.0 was chosen as the criterion cutoff associated with the 95th percentile during no-response conditions using group data for cVEMPs and oVEMPs, respectively. Fsp values for cVEMPs and oVEMPs were not significantly different from each other. This study established single-point latency and time-window parameters for VEMP-related applications of the Fsp detection algorithm. Fsp criteria values were established for cVEMP and oVEMP. Using these parameters, responses were detected in all participants.

Can You Understand Why I Am Crying? A Decision-making System for Classifying Infants’ Cry Languages Based on DeepSVM Model

Scientific and therapeutic advances in perinatology and neonatology have improved the survival prospects of preterm and extremely-low-birth-weight infants. Infants’ cries are a valuable noninvasive tool for monitoring their neurologic health, especially if they are premature. Automatic acoustic analysis and data mining are employed in this study to determine the discriminative features of preterm and full-term infant cries. The use of machine learning for recognizing sounds in a newborn's cry language has received less attention than previous methods for analyzing the sounds. Moreover, to extract appropriate features from infant cries, adequate knowledge and appropriate signal descriptors are required. Accordingly, to analyze infant cry language, we propose an approach that uses fractal descriptors to extract discriminant features from spectrograms of windowed signals, followed by iterative neighborhood component analysis (iNCA) to select appropriate features. Additionally, the improved deep support vector machine (DeepSVM) is used to classify the infants’ crying types and their meanings. The proposed method is verified using a newborn sound dataset. According to the classification of five types of crying perception based on various characteristics, 98.34% of all crying perceptions have been recognized. Although there are many classes examined, the feature extraction method based on the fractal method and our optimal classification have a much higher diagnostic accuracy compared with similar methods for analyzing baby crying language. The proposed method can overcome many problems associated with analyzing babies’ crying sounds and understanding their language, such as uncertainty and unusual errors in classification.

Broadband sub-wavelength terahertz subsurface imaging using a solid-immersion lens

We demonstrate a broadband sub-wavelength terahertz (THz) imaging system by combining a solid-immersion lens (SIL) with a THz time-domain spectrometer (TDS). The imaging resolution of the system is enhanced in a wide spectral range (from 0.3 to 2.0 THz) with the aid of the SIL. The beam size is within 120–280 μm (0.28–0.8 λ) in the measured frequency range. Experimentally achieved imaging resolution enhancement results are consistent with the numerical simulation results. Unlike the previous reports on imaging systems using SIL, the beam size reduction by the SIL is more than a factor of nSIL (3.42) for all frequencies near the focal spot. An analytical calculation is employed to obtain insights into the beam size reduction mechanism and estimate the beam size in a given experimental condition. THz image of the WR1.9 waveguide is measured to highlight the broadband sub-wavelength imaging capability. The increased reflection from the waveguide near the cutoff frequency for TE10 and TE01 modes, inaccessible with other THz-TDS-based or continuous wave (CW) source-based THz imaging systems, is experimentally observed. In addition, we show the multispectral sub-wavelength images of subsurface structures by selecting the appropriate measurement time window of the temporal THz signal. THz images of a credit card show the sub-wavelength details of the card, including an IC chip, invisible due to the plastic cover. The experimental results highlight the potential applications of the imaging system in non-contact, non-destructive testing of the sub-wavelength scale structures in various fields.

Barycenter Calibration With High Order Spectra of Windowed Delay-Doppler Signals for OTFS Based ISAC Systems

Barycenter Calibration With High Order Spectra of Windowed Delay-Doppler Signals for OTFS Based ISAC Systems

Chaotic bursting patterns induced by transient chaos in a smooth three-dimensional dynamic model

This paper aims to report the transient-chaos-induced chaotic bursting oscillations in a smooth three-dimensional dynamic model with a low-frequency excitation when an order gap exists between the excitation frequency and the natural frequency. Based on the Nosé-Hoover system, a periodically driven nonlinear system is modelled, in which the transient chaos induced by boundary crisis route can be observed. Through theoretical analysis and numerical simulation, the stability conditions of the equilibrium point and six transient-chaos-induced chaotic bifurcation structures are obtained. Six typical cases in which different routes of the trajectories pass through the transient chaos areas, leading to different patterns of bursting oscillations. It can be found that not only the bifurcation structures but also the bifurcation delay behavior can influence the bursting patterns. In addition, in the transient chaos areas, the bifurcation delay behavior can result in the disappearance of the periodic signals in the small periodic windows.

Analytical estimation of the signal to noise ratio efficiency in axion dark matter searches using a Savitzky-Golay filter

The signal to noise ratio efficiency ϵSNR in axion dark matter searches has been estimated using large-statistic simulation data reflecting the background information and the expected axion signal power obtained from a real experiment. This usually requires a lot of computing time even with the assistance of powerful computing resources. Employing a Savitzky-Golay filter for background subtraction, in this work, we estimated a fully analytical ϵSNR without relying on large-statistic simulation data, but only with an arbitrary axion mass and the relevant signal shape information. Hence, our work can provide ϵSNR using minimal computing time and resources prior to the acquisition of experimental data, without the detailed information that has to be obtained from real experiments. Axion haloscope searches have been observing the coincidence that the frequency independent scale factor ξ is approximately consistent with the ϵSNR. This was confirmed analytically in this work, when the window length of the Savitzky-Golay filter is reasonably wide enough, i.e., at least 5 times the signal window.

Anthropogenic intensification of the eco-hydrothermal regime transition in regulated rivers: The cumulative effect of cascade reservoirs

Although more reservoirs are under construction, research on the cumulative effects of cascade reservoirs remains insufficient. In addition, there is a lack of detailed metrics for the extreme hydrothermal events. We develop a comprehensive framework that focuses on changes in the eco-hydrothermal regime of rivers and the cumulative effects of cascade reservoirs. The framework integrates 29 eco-hydrothermal indicators (existing and newly proposed), including Magnitude, Thermal suitability, Timing, Rate of change, and Extreme hydrothermal conditions. A coupled method of the long short-term memory (LSTM) and air2stream models is used to reconstruct multi-scenario water temperature under non-cascade reservoir regulation. Results show that the eco-hydrothermal regime in the upper Yangtze River has undergone significant changes, with the most pronounced change observed in extreme hydrothermal events, where the duration, severity, and kurtosis have increased, with an overall RVA value of 0.87. Cascade reservoir regulation is the main cause of eco-hydrothermal changes, promoting the homogenization of hydrothermal processes, reducing thermal suitability for the Four Famous Major Carp and Chinese Sturgeon during spawning, and delaying the window of thermal signals. The study found that while climate change has enhanced extreme thermal conditions in the upper Yangtze River, the aggravation of extreme high and low hydrothermal conditions is primarily controlled by cascade reservoirs. In terms of the impacts calculated, the duration corresponds to 104–117 days and 32–36 days, severity corresponds to 277.85–287.21 °C2 and −84.53∼−90.2 °C2, and kurtosis corresponds to 1.94–2.42 °C2 and −0.85∼−1.15 °C2.

Detection of aortic stenosis using built-in microphones of commercially available smartphones

Abstract Background According to recent data, asymptomatic patients with severe aortic stenosis benefit from early treatment. Hence, widely applicable methods to identify patients with severe aortic stenosis in an asymptomatic state may be desirable in the future. While the diagnosis of aortic stenosis is currently based on echocardiography, population-wide use is not feasible due to resource constraints. With recent technological advances in built-in microphones of commercially available smartphones, digital auscultation may provide a viable alternative. Methods As part of this study, a smartphone application to perform digital auscultation was developed. To ensure high-quality recordings real-time signal analysis is implemented. The 8 kHz sampling rate of the signal encompasses all typical frequencies of cardiac auscultation while keeping resource usage minimal. To prevent frequency leakage the signal is attenuated using a Blackman-Harris window. After applying Fast Fourier Transformation to the windowed signal, frequencies around 100 Hz and 250 Hz are amplified to match the frequency response of traditional stethoscopes. The resulting signal amplitudes are then visualized. The application provides functionality for storing metadata about each patient in addition to the raw audio recordings. To verify the feasibility of using the described application to detect severe aortic stenosis, this study aims to enroll 50 persons with no valvular heart disease and 50 persons with severe aortic stenosis. Initial audio recordings by the smartphone application and of a digital stethoscope are compared by visualizing the frequency spectrum using the Mel Frequency Cepstral Coefficients. Results The peaks of the first and second heart sound at around 50 Hz are both clearly identifiable, as is the crescendo-decrescendo murmur typically associated with aortic stenosis in the frequency range of 100-600 Hz (Figure 1). This is true for both smartphone and stethoscope recordings, even though the built-in microphone of smartphones results in a lower signal-to-noise ratio. Nevertheless, the quality of the signal is robust enough to clearly indicate the presence of aortic stenoses. Conclusions The initial findings of this study indicate that a smartphone application using built-in microphones is a promising approach for identifying severe aortic stenoses. This approach has the potential to be a reliable and cost-effective screening tool for early detection. Further development of automated algorithms may enhance the tool's effectiveness and accessibility.Figure 1

Landscape of the Radiosensitizing Properties of Targeted Agents from the NCI CTEP Portfolio across Genomically Diverse 3D Tumor Models

Clinical translation of molecular targeted radiosensitizers remains challenging. To improve the robustness of preclinical data, we sought to broadly examine across a wide spectrum of genomic cancer features the radiosensitizing properties of targeted drugs from the NCI Cancer Therapy Evaluation Program (CTEP). As part of a U01 Consortium, we adapted a robotic high-throughput platform for testing ionizing radiation (IR) plus drug combinations in 3D cultures derived from non-small cell lung carcinoma (NSCLC), head/neck (HNSCC), and pancreatic cancers. All cell lines were annotated, mycoplasma free, and re-authenticated. Signal windows were established for all 3D growth formats. CTEP drugs were added in four concentrations 24 hours prior to IR exposure of 384-well plates. Five days after IR, cell viability was assessed, an endpoint previously benchmarked using colony formation. Each data point was based on 4-6 technical replicates with 2 biological repeats. A multistep statistical method combining Wilcoxon signed-rank and Spearman rank-order correlation tests together with adjusted Benjamini-Hochberg corrections for multiple comparisons was used to identify radiosensitization hits. Select validation experiments of hits were carried out. We screened 73 3D tumor models with 34-42 CTEP drugs each plus IR (n = 10,744 cell line/drug concentration combinations). IR/drug patterns were comparable for the three cancer types. DNA damage response (DDR) inhibitors constituted the most active class of radiosensitizers with DNA-PKcs, PARP, and ATR being the top targets. In contrast, the majority of the other agents radiosensitized only a minority of models. Highlighting NSCLC, out of 1,120 cell line/drug combinations, 328 (29.3%) were associated with statistically significant radiosensitization. PARP inhibitors, of particular interest for clinical translation in this space, demonstrated relatively broad radiosensitization for talazoparib (62%) and olaparib (44%) but less so for veliparib (28%), which correlated with differential PARP trapping potentials. Radiosensitization was evident even for tumor genotypes associated with radioresistance such as mutant KRAS, KEAP1, and STK11. Interestingly, single-agent activity of olaparib was more pronounced in 11/30 3D cultures compared to 2D growth conditions but with only 2/11 having known homologous recombination defects. Lastly, for HPV negative HNSCC (735 combinations), there was radiosensitization in subsets of models for IAP, ATR, and AKT targets, which are under clinical investigation. We present a unique resource for examining radiosensitizing drug properties in 3D cultures. The observed radiosensitization patterns strongly argue for the use of larger panels of clinically relevant tumor models rather than a few non-representative cell lines. Our data highlight the urgent need for predictive biomarkers to guide IR/drug combinations, even for some DDR inhibitors.

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems.

Local and global measures of information storage for the assessment of heartbeat-evoked cortical responses

Objective:Brain–heart interactions involve bidirectional effects produced by bottom-up input at each heartbeat, and top-down neural regulatory responses of the brain. While the cortical processing of the heartbeat is usually investigated through the analysis of the Heartbeat Evoked Potential, in this study we propose an alternative approach based on the variability in the predictability of the brain dynamics induced by the heartbeat. Methods:In a group of eighteen subjects in whom simultaneous recording of the electroencephalogram (EEG) and electrocardiogram was performed in a resting-state, we analyzed the temporal profile of the local Information Storage (IS) to detect changes in the regularity of EEG signals in time windows associated with different phases of the cardiac cycle at rest. Results:The average values of the local IS were significantly higher in the parieto-occipital areas of the scalp, suggesting an activation of the Default Mode Network, regardless of the cardiac cycle phase. In contrast, the variability of the local IS showed marked differences across the cardiac cycle phases. Conclusion:Our results suggest that cardiac activity influences the predictive information of EEG dynamics differently in the various phases of the cardiac cycle. Significance:The variability of local IS measures can represent a useful index to identify spatio-temporal dynamics within the neurocardiac system, which generally remain overlooked by the more widely employed global measures.

Biorthogonal wavelet energy entropy method with different time windows for strong interference processing of GPR signals

In the light of the shielding effect of the strong reflection signal generated by the steel bars and the inhomogeneous of concrete materials on the weak signal in the tunnel lining detection, a high-resolution GPR image processing method of biorthogonal wavelet energy entropy in different time windows is proposed. Wavelet analysis has the characteristics of multiresolution, and wavelet entropy can represent the energy distribution characteristics of signals in different time windows with different scales. Firstly, the discrete biorthogonal wavelet transform is applied to decompose the original GPR image into subimages with different frequencies and different spatial local changes. Secondly, appropriate time windows are selected for wavelet entropy adaptive threshold processing of each subimage in the light of the different amplitude ratio of effective signal to strong interference in each subimage, and finally the reconstructed image after removing the strong reflection is spectrally whitened. Both the forward simulations and actual measurement results show that the method can effectively remove strong reflection interference, clear weak signals, and maintain the continuity of the reflection events, and compared to the conventional pulse deconvolution, predictive deconvolution and wavelet entropy methods with the same time window, this method has a more thorough interference removal effect, and the effective signal performance is more obvious, which proves the effectiveness and accuracy of the proposed method in improving the resolution of GPR image and highlighting weak signal.

Discovery of DTX3L inhibitors through a homogeneous FRET-based assay that monitors formation and removal of poly-ubiquitin chains

Ubiquitination is a reversible protein post-translational modification in which consequent enzymatic activity results in the covalent linking of ubiquitin to a target protein. Once ubiquitinated, a protein can undergo multiple rounds of ubiquitination on multiple sites or form poly-ubiquitin chains. Ubiquitination regulates various cellular processes, and dysregulation of ubiquitination has been associated with more than one type of cancer. Therefore, efforts have been carried out to identify modulators of the ubiquitination cascade. Herein, we present the development of a FRET-based assay that allows us to monitor ubiquitination activity of DTX3L, a RING-type E3 ubiquitin ligase. Our method shows a good signal window with a robust average Z’ factor of 0.76 on 384-well microplates, indicating a good assay for screening inhibitors in a high-throughput setting. From a validatory screening experiment, we have identified the first molecules that inhibit DTX3L with potencies in the low micromolar range. We also demonstrate that the method can be expanded to study deubiquitinases, such as USP28, that reduce FRET due to hydrolysis of fluorescent poly-ubiquitin chains.

Speech Enhancement Using Sliding Window Empirical Mode Decomposition and Hurst-based Technique

The most challenging in speech enhancement technique is tracking non-stationary noises for long speech segments and low Signal-to-Noise Ratio (SNR). Different speech enhancement techniques have been proposed but, those techniques were inaccurate in tracking highly non-stationary noises. As a result, Empirical Mode Decomposition and Hurst-based (EMDH) approach is proposed to enhance the signals corrupted by non-stationary acoustic noises. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by considering the least corrupted IMF. Though it increases SNR, the time and resource consumption were high. Also, it requires a significant improvement under nonstationary noise scenario. Hence, in this article, EMDH approach is enhanced by using Sliding Window (SW) technique. In this SWEMDH approach, the computation of EMD is performed based on the small and sliding window along with the time axis. The sliding window depends on the signal frequency band. The possible discontinuities in IMF between windows are prevented by the total number of modes and the number of sifting iterations that should be set a priori. For each module, the number of sifting iterations is determined by decomposition of many signal windows by standard algorithm and calculating the average number of sifting steps for each module. Based on this approach, the time complexity is reduced significantly with suitable quality of decomposition. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.

Measurement of vehicle speed based on the GCC algorithm and its application in anti-slip control

Measuring the speed of locomotives is important for train operation, especially in anti-slip control. A speed measurement method is proposed based on the correlation of vibration signals and the core process is to estimate the time shift of the windowed signals using the generalized cross-correlation (GCC) algorithm. A sensitivity analysis of the algorithm and suggested reasonable ranges for critical parameters are conducted. The effectiveness of the proposed method is demonstrated using both simulated and tested vibration signals, showing that it performs well under both constant and variable speed conditions. The proposed method had a maximum improvement in root-mean-square error (RMSE) of 2.72% compared to the measurement of the existing cross-correlation (CC) algorithm. The proposed method is integrated into a heavy-haul train model with an anti-slip controller. Simulation results indicate that the method can accurately measure the locomotive speed when partial or all wheels slip.

TESLA, A Tool for Automatic Earthquake Low-Frequency Spectral Level Estimation: The Study of 2013 St. Gallen Earthquake Fault-Plane Solutions

Abstract One of the challenges of seismicity monitoring is to achieve multiparametric catalogs complete down to small magnitude using automatic procedures. This can be obtained using seismic networks with high performance and robust, automatic algorithms able to process large data sets, limiting the manual operations of the analysts. The characterization of microseismicity is fundamental to study its spatial and temporal evolution and to define the seismic activity of fault systems. Among the source parameters of microseismic events, focal mechanisms are not generally calculated and, when available in the seismic catalog, their reliability may be dubious. We propose a new tool, named Tool for automatic Earthquake low-frequency Spectral Level estimAtion (TESLA), to automatically calculate the P- and S-wave low-frequency spectral levels. Indeed, it has been shown that these levels can be inverted together with P-phase polarities to better constrain the focal mechanism or to estimate the seismic moment. TESLA is designed to invert the P- and S-displacement spectra searching the optimal signal window to use for the spectral analysis. Using a signal window of fixed duration, although variable according to the earthquake magnitude, is not always the appropriate choice, especially when microseismicity is analyzed. TESLA performs three main tasks for both P and S phases: (1) a systematic exploration of several signal windows to use for the computation of displacement spectra, (2) the spectral analysis for all the selected signal windows, and (3) the evaluation of the best-displacement spectra through quantitative criteria and the estimation of the low-frequency spectral levels. The tool is first validated and then applied to the 2013 St. Gallen, Switzerland, induced seismic sequence to calculate the P and S low-frequency spectral level ratios, which are inverted to estimate focal mechanisms. Our results show the robustness of the tool to process microseismicity and the benefit of using it to automatically analyze large waveform data sets.

Noise Reduction Method of Pipeline Infrasonic Leakage Signal Based on Improved Prony Algorithm and Difference Energy Model

To solve the difficult problem of low-frequency noise processing in pipeline infrasonic leakage detection signals, a pipeline infrasonic leakage signal denoising method based on improved Prony algorithm and differential energy model was proposed to reduce the low-frequency interference noise in the signal and to improve the signal denoising effect. First, the frequency window of the effective signal is obtained according to the spectrogram of the infrasound signal. Aiming at the problem that the Prony algorithm is affected by noise, a difference energy model is proposed. The difference energy model is used to filter out the part of the frequency domain signal with large energy fluctuation, and a relatively stable preprocessing signal is obtained. In view of the instability of the traditional Prony algorithm, a Hankel matrix is established in the operation process. The stability is improved by extracting the extremum and residue of the signal instead of directly solving the sampling data points, and the extremum and residue of the effective signal are selected by combining the frequency window of the active ingredient. Finally, the effective signal is reconstructed to obtain a relatively stable infrasound leakage noise reduction signal. Experimental results show that the noise reduction technology based on the improved Prony algorithm and differential energy model can effectively reduce the noise of pipeline leakage signals. Compared with the traditional Prony algorithm, the noise reduction effect of the proposed method is up to 38.01% higher. Compared with the empirical mode decomposition method, the noise reduction effect of this method is improved by 9.25% at least, which opens up a new idea for pipeline leakage signal noise reduction.

EDNA metabarcoding from aquatic biofilms allows studying spatial and temporal fluctuations of fish communities from Lake Geneva

AbstractFish communities are now studied non‐invasively using environmental DNA (eDNA) recovered from water samples. The objective of this study is to evaluate the possibility of surveying these communities using fish eDNA passively “captured” by aquatic biofilms. To this end, biofilm samples developing on natural and artificial substrates were collected every 2 weeks for a year and a half in a large lake (Lake Geneva). DNA was extracted from biofilms and fish communities were targeted using a standard 12S barcode with a metabarcoding approach. The fish eDNA signal recovered from biofilms revealed temporal and spatial changes in fish communities. These changes were linked to fish habitat preferences and spawning season. Peaks in the eDNA signal of some taxa fitted with their spawning period reported in the literature. We evidenced that the nature of the biofilm's substrate (natural or artificial) does not affect the image obtained of the fish community composition. Furthermore, by using biofilms grown on artificial substrates, the studied temporal window of the eDNA signal can be controlled. With biofilms acting as environmental passive samplers, our results open up the possibility to accurately monitor fish communities and their temporal and spatial changes with eDNA in a faster and less expensive way than with the classical water filtration approach.

Dynamics of corticocortical brain functional connectivity relevant to therapeutic response to biologics in inflammatory arthritis.

Aberrant functional connectivity (FC) of the brain regions, evaluated by functional magnetic resonance imaging (fMRI), affects clinical courses in inflammatory arthritis (IA). The static analysis methods would be simplistic to estimate the whole picture of resting-state brain function because blood oxygen level-dependent (BOLD) signals fluctuate over time. The effects of FC dynamics on clinical course are unknown in IA. Therefore, we aimed to evaluate dynamic FC for therapeutic responsiveness to biologics in IA patients. We analyzed resting-state fMRI data of 64 IA patients in 2 cohorts. Dynamic FC was derived as a correlation coefficient of the windowed BOLD signal time series. We determined representative whole-brain dynamic FC patterns by k-means++ cluster analysis, leading to 4 distinct clusters. In the first cohort, occurrence probability of the distinct cluster was associated with favorable therapeutic response in disease activity and patients' global assessment, which was validated by the second cohort. The whole-brain FC of the distinct cluster indicated significantly increased corticocortical connectivity, and probabilistically decreased after therapy in treatment-effective patients compared with -ineffective patients. Taken together, frequent emergence of corticocortical connections was associated with clinical outcomes in IA. The coherence of corticocortical interactions might affect pain modulation, possibly relevant to therapeutic satisfaction.