Quadriceps rate of torque development (RTD) and torque steadiness are valuable metrics for assessing explosive strength and the ability to control force over a sustained period of time, which can inform clinical assessments of knee function. Despite their widespread use, there is a significant gap in standardized methodology for measuring these metrics, which limits their utility in comparing outcomes across different studies and populations. To address these gaps, we evaluated the influence of sampling rates, signal filtering, and torque onset detection on RTD and torque steadiness. Twenty-seven participants with a history of a primary anterior cruciate ligament reconstruction (N = 27 (11 male/16 female), age = 23 ± 8 years, body mass index = 26 ± 4 kg/m2) and thirty-two control participants (N = 32 (13 male/19 female), age = 23 ± 7 years, body mass index = 23 ± 3 kg/m2) underwent isometric quadriceps strength testing, with data collected at 2222 Hz on an isokinetic dynamometer. The torque-time signal was downsampled to approximately 100 and 1000 Hz and processed using a low-pass, zero-lag Butterworth filter with a range of cutoff frequencies spanning 10-200 Hz. The thresholds used to detect torque onset were defined as 0.1 Nm, 1 Nm, and 5 Nm. RTD between 0 and 100 ms, 0 and 200 ms, and 40-160 ms was computed, as well as absolute and relative torque steadiness. Relative differences were computed by comparing all outcomes to the \"gold standard\" values computed, with a sampling rate of 2222 Hz, a cutoff frequency in the low-pass filter of 150 Hz, and torque onset of 1 Nm, and compared utilizing linear mixed models. While all combinations of signal collection and processing parameters reached statistical significance (p < 0.05), these differences were consistent between injured and control limbs. Additionally, clinically relevant differences (+/-10%) were primarily observed through torque onset detection methods and primarily affected RTD between 0 and 100 ms. Although measurements of RTD and torque steadiness were generally robust against diverse signal collection and processing parameters, the selection of torque onset should be carefully considered, especially in early RTD assessments that have shorter time epochs.

OBJECTIVE: Preterm delivery is an important factor in the disease burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a promising technique for predicting this condition, thanks to its high degree of sensitivity. Despite the technological progress made in predicting preterm labor, its use in clinical practice is still limited, one of the main barriers being the lack of tools for automatic signal processing without expert supervision, i.e. automatic screening of motion and respiratory artifacts in EHG records. Our main objective was thus to design and validate an automatic system of segmenting and screening the physiological segments of uterine origin in EHG records for robust characterization of uterine myoelectric activity, predicting preterm labor and help to promote the transferability of the EHG technique to clinical practice.
METHODS: For this, we combined 300 EHG recordings from the TPEHG DS database and 69 EHG recordings from our own database (Ci2B-La Fe) of women with singleton gestations. This dataset was used to train and evaluate U-Net, U-Net++, and U-Net 3+ for semantic segmentation of the physiological and artifacted segments of EHG signals. The model\'s predictions were then fine-tuned by post-processing.
RESULTS: U-Net 3+ outperformed the other models, achieving an area under the ROC curve of 91.4 % and an average precision of 96.4 % in detecting physiological activity. Thresholds from 0.6 to 0.8 achieved precision from 93.7 % to 97.4 % and specificity from 81.7 % to 94.5 %, detecting high-quality physiological segments while maintaining a trade-off between recall and specificity. Post-processing improved the model\'s adaptability by fine-tuning both the physiological and corrupted segments, ensuring accurate artifact detection while maintaining physiological segment integrity in EHG signals.
CONCLUSIONS: As automatic segmentation proved to be as effective as double-blind manual segmentation in predicting preterm labor, this automatic segmentation tool fills a crucial gap in the existing preterm delivery prediction system workflow by eliminating the need for double-blind segmentation by experts and facilitates the practical clinical use of EHG. This work potentially contributes to the early detection of authentic preterm labor women and will allow clinicians to design individual patient strategies for maternal health surveillance systems and predict adverse pregnancy outcomes.

In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal\'s ambient concentration increases. However, among competitive ligands, the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.

BACKGROUND: Sleep apnea syndrome, characterized by recurrent cessation (apnea) or reduction (hypopnea) of breathing during sleep, is a major risk factor for postoperative respiratory depression. Challenges in sleep apnea assessment have led to the proposal of alternative metrics derived from oxyhemoglobin saturation (SpO2), such as oxygen desaturation index (ODI) and percentage of cumulative sleep time spent with SpO2 below 90% (CT90), as predictors of postoperative respiratory depression. However, their performance has been limited with area under the curve of 0.60 for ODI and 0.59 for CT90. Our objective was to propose novel features from preoperative overnight SpO2 which are correlated with sleep apnea severity and predictive of postoperative respiratory depression.
METHODS: Preoperative SpO2 signals from 235 surgical patients were retrospectively analyzed to derive seven features to characterize the sleep apnea severity. The features included entropy and standard deviation of SpO2 signal; below average burden characterizing the area under the average SpO2; average, standard deviation, and entropy of desaturation burdens; and overall nocturnal desaturation burden. The association between the extracted features and sleep apnea severity was assessed using Pearson correlation analysis. Logistic regression was employed to evaluate the predictive performance of the features in identifying postoperative respiratory depression.
RESULTS: Our findings indicated a similar performance of the proposed features to the conventional apnea-hypopnea index (AHI) for assessing sleep apnea severity, with average area under the curve ranging from 0.77 to 0.81. Notably, entropy and standard deviation of overnight SpO2 signal and below average burden showed comparable predictive capability to AHI but with minimal computational requirements and individuals\' burden, making them promising for screening purposes. Our sex-based analysis revealed that compared to entropy and standard deviation, below average burden exhibited higher sensitivity in detecting respiratory depression in women than men.
CONCLUSIONS: This study underscores the potential of preoperative SpO2 features as alternative metrics to AHI in predicting postoperative respiratory.

UNASSIGNED: Current guidelines recommend quantitative neuromuscular block monitoring during neuromuscular blocking agent administration. Monitors using surface electromyography (EMG) determine compound motor action potential (cMAP) amplitude or area under the curve (AUC). Rigorous evaluation of the interchangeability of these methods is lacking but necessary for clinical and research assurance that EMG interpretations of the depth of neuromuscular block are not affected by the methodology.
UNASSIGNED: Digitised EMG waveforms were studied from 48 patients given rocuronium during two published studies. The EMG amplitudes and AUCs were calculated pairwise from all cMAPs classified as valid by visual inspection. Ratios of the first twitch (T1) to the control T1 before administration of rocuronium (T1c) and train-of-four ratios (TOFRs) were compared using repeated measures Bland-Altman analysis.
UNASSIGNED: Among the 2419 paired T1/T1c differences where the average T1/T1c was ≤0.2, eight (0.33%) were outside prespecified clinical limits of agreement (-0.148 to 0.164). Among the 1781 paired TOFR differences where the average TOFR was ≥0.8, 70 (3.93%) were outside the prespecified clinical limits of agreement ((-0.109 to 0.134). Among all 7286 T1/T1c paired differences, the mean bias was 0.32 (95% confidence interval 0.202-0.043), and among all 5559 paired TOFR differences, the mean bias was 0.011 (95% confidence interval 0.0050-0.017). Among paired T1/T1c and TOFR differences, Lin\'s concordance correlation coefficients were 0.98 and 0.995, respectively. Repeatability coefficients for T1/T1c and TOFR were <0.08, with no differences between methods.
UNASSIGNED: Quantitative assessment neuromuscular block depth is clinically interchangeable when calculated using cMAP amplitude or the AUC.

BACKGROUND: Electrocardiograms (ECGs) are vital for diagnosing cardiac conditions but obtaining clean signals in Left Ventricular Assist Device (LVAD) patients is hindered by electromagnetic interference (EMI). Traditional filters have limited efficacy. There is a current need for an easy and effective method.
METHODS: Raw ECG data obtained from 5 patients with LVADs. LVAD types included HeartMate II, III at multiple impeller speeds, and a case with HeartMate III and a ProtekDuo. ECG spectral profiles were examined ensuring the presence of diverse types of EMI in the study. ECGs were then processed with four denoising techniques: Moving Average Filter, Finite Impulse Response Filter, Fast Fourier Transform, and Discrete Wavelet Transform.
RESULTS: Discrete Wavelet Transform proved as the most promising method. It offered a one solution fits all, enabling automatic processing with minimal user input while preserving crucial high-frequency components and reducing LVAD EMI artifacts.
CONCLUSIONS: Our study demonstrates the practicality and efficiency of Discrete Wavelet Transform in obtaining high-fidelity ECGs in LVAD patients. This method could enhance clinical diagnosis and monitoring.

The ultrasonic pulse-echo technique is widely employed to measure the wall thickness reduction due to corrosion in pipelines. Ultrasonic monitoring is noninvasive and can be performed online to evaluate the structural health of pipelines. Although ultrasound is a robust technique, it presents two main difficulties arising from the temperature variation in the medium being monitored: the mechanical assembly must have high stability and the ultrasonic propagation velocity must take into account the temperature variation. In this paper, a detailed strategy is presented to compensate for changes in the propagation velocity whenever the temperature changes. The method is considered self-compensated because the calibration data is obtained from the ultrasonic signals captured using the pipe under evaluation. The analysis of systematic errors in the temperature compensation is presented, first considering that a reference initial pipe thickness is given, and second when a reference sound velocity is given. The technique was evaluated under laboratory conditions using a closed loop with accelerated corrosion through the use of continuous flow saline water containing sand. In this test, the ultrasonic results were compared with the traditional coupon method used to determine corrosion loss. The results show that the self-compensated method was able to compensate for temperature fluctuations, and the total thickness loss measured by the ultrasound technique was close to the value measured by the coupons. Finally, the measurement system was tested in a production pipeline exposed to sunlight. The results show that the self-compensated method can reduce the oscillations in the thickness loss readings, caused by temperature swings, but large temperature variations cannot be completely compensated for. This experiment also shows the effects of low mechanical stability, which caused completely invalid results.

GNSS signals are vulnerable to spoofing and interference, which poses a threat to the security of critical national infrastructure. GNSS data sets with spoofing and jamming are lacking, which hinders the research of GNSS anti-spoofing and anti-interference techniques. This data article presents a dataset recorded by a low-cost sensor deployed on the balcony at the 5th floor of the Science Hall of Yunnan University (25°3\'26\'\' N, 102°41\'55\'\' E). The sensor suite includes a GNSS antenna, a u-blox GNSS receiver and an embedded computer. In the experiment, interferences including spoofing and jamming were irregularly emitted using a SDR HackRF One and a commercial jammer, respectively. The dataset collected by the receiver consists of two parts: (1) raw data; (2) processed data. The types of the raw data include hardware information, satellite information and receiver parameters of GPS, Campass, Galileo, GLONASS and QZSS systems. The processed data are extracted from the raw data, including the signals, Doppler shift, pseudorange observations, carrier phase, position (latitude, longitude, and altitude), satellite azimuth and elevation angles, etc. The provided datasets are interesting for the GNSS security, anti-jamming and anti-spoofing mechanisms based scientific communities.

OBJECTIVE: Multiple sclerosis (MS) is a neuro-inflammatory disease affecting the central nervous system (CNS), where the immune system targets and damages the protective myelin sheath surrounding nerve fibers, inhibiting axonal signal transmission. Demyelinating optic neuritis (ON), a common MS symptom, involves optic nerve damage. We\'ve developed NeuroVEP, a portable, wireless diagnostic system that delivers visual stimuli through a smartphone in a headset and measures evoked potentials at the visual cortex from the scalp using custom electroencephalography electrodes.
METHODS: Subject vision is evaluated using a short 2.5-min full-field visual evoked potentials (ffVEP) test, followed by a 12.5-min multifocal VEP (mfVEP) test. The ffVEP evaluates the integrity of the visual pathway by analyzing the P100 component from each eye, while the mfVEP evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEPs. Key metrics from patients\' ffVEP results were statistically evaluated against data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification.
RESULTS: 20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging and Optical Coherence Tomography findings.
CONCLUSIONS: This pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.

With the advent of the Internet of Everything (IoE), the concept of fully interconnected systems has become a reality, and the need for seamless communication and interoperability among different industrial systems has become more pressing than ever before. To address the challenges posed by massive data traffic, we demonstrate the potentials of semantic information processing in industrial manufacturing processes and then propose a brief framework of semantic processing and communication system for industrial network. In particular, the scheme is featured with task-orientation and collaborative processing. To illustrate its applicability, we provide examples of time series and images, as typical industrial data sources, for practical tasks, such as lifecycle estimation and surface defect detection. Simulation results show that semantic information processing achieves a more efficient way of information processing and exchanging, compared to conventional methods, which is crucial for handling the demands of future interconnected industrial networks.