UNASSIGNED: This study assesses the utility of jitter analysis with concentric needles to evaluate disease severity in myasthenia gravis (MG), correlate changes in jitter with clinical status as well as identify reasons for any discordance.
UNASSIGNED: We performed a retrospective chart review of 82 MG patients and extracted data on demographics, MG subtype, antibody status, clinical scales, electrophysiology, and interventions at baseline and follow-up.
UNASSIGNED: Baseline MGII scores correlated with jitter (r = 0.25, p = 0.024) and abnormal pairs (r = 0.24, p = 0.03). After 28 months, MGII scores correlated with jitter (r = 0.31, p = 0.006), abnormal pairs (r = 0.29, p = 0.009), and pairs with blocks (r = 0.35, p = 0.001). Changes in MGII scores correlated with changes in jitter (r = 0.35, p = 0.002), abnormal pairs (r = 0.27, p = 0.014), and pairs with blocks (r = 0.36, p = 0.001).
UNASSIGNED: Concentric needle jitter analysis may have the potential to evaluate baseline and sequential disease severity in MG.
UNASSIGNED: This study highlights the potential for improved MG patient care through precise assessment and management using concentric needle jitter analysis to improve the accuracy of MG diagnosis and monitoring of disease activity.

OBJECTIVE: There is a lack of studies comparing the accuracy of neuromuscular jitter analysis during voluntary activation (v-jitter study) versus axonal stimulation (s-jitter study). The study aimed to compare these two techniques in the same population of patients with suspected ocular myasthenia gravis (OMG).
METHODS: Fourteen control subjects (mean age: 55.5 ± 15.2 years) and 34 patients with suspected OMG (mean age: 59 ± 13.9 years) were prospectively evaluated. Twenty spike pairs and 30 individual spikes were analyzed during v-jitter and s-jitter study, respectively. Two different criteria for abnormal individual jitter values were evaluated: ≥ or > than 10% values exceeding the upper normal limit.
RESULTS: OMG was diagnosed in 19 patients based on clinical and laboratory findings, without considering jitter measurements. In most patients, v-jitter and s-jitter analyses provided comparable results. The maximum sensitivity (89%) was achieved with s-jitter study using the ≥10% criterion, while the maximum specificity (93%) was found with v-jitter study using the >10% criterion.
CONCLUSIONS: Both v-jitter and s-jitter studies showed good to very good accuracy for the diagnosis of OMG, in the absence of any statistically significant difference. Therefore, the patient\'s cooperation level and examiner\'s experience should guide the choice of performing v-jitter or s-jitter analysis in patients with suspected OMG.

Objective.Patients suffering from heavy paralysis or Locked-in-Syndrome can regain communication using a Brain-Computer Interface (BCI). Visual event-related potential (ERP) based BCI paradigms exploit visuospatial attention (VSA) to targets laid out on a screen. However, performance drops if the user does not direct their eye gaze at the intended target, harming the utility of this class of BCIs for patients suffering from eye motor deficits. We aim to create an ERP decoder that is less dependent on eye gaze.Approach.ERP component latency jitter plays a role in covert visuospatial attention (VSA) decoding. We introduce a novel decoder which compensates for these latency effects, termed Woody Classifier-based Latency Estimation (WCBLE). We carried out a BCI experiment recording ERP data in overt and covert visuospatial attention (VSA), and introduce a novel special case of covert VSA termed split VSA, simulating the experience of patients with severely impaired eye motor control. We evaluate WCBLE on this dataset and the BNCI2014-009 dataset, within and across VSA conditions to study the dependency on eye gaze and the variation thereof during the experiment.Main results.WCBLE outperforms state-of-the-art methods in the VSA conditions of interest in gaze-independent decoding, without reducing overt VSA performance. Results from across-condition evaluation show that WCBLE is more robust to varying VSA conditions throughout a BCI operation session.Significance. Together, these results point towards a pathway to achieving gaze independence through suited ERP decoding. Our proposed gaze-independent solution enhances decoding performance in those cases where performing overt VSA is not possible.

UNASSIGNED: Our aim was to examine the significance of single-fiber electromyography (SFEMG) in patients diagnosed with amyotrophic lateral sclerosis (ALS) and determine the best correlating parameter with SFEMG parameters and clinical scales across different muscles including facial muscles.
UNASSIGNED: SFEMG examinations were conducted on the extensor digitorum (ED), frontalis, and orbicularis oculi muscles. Mean jitter, percentage of increased jitter, fiber density (FD), and impulse blocking percentage were compared to reference values and functional scales.
UNASSIGNED: Significant differences (p < 0.001) were observed between the patients\' SFEMG results and reference values in all muscles. Significant correlations were found between SFEMG parameters and clinical scales, particularly when considering both FD and jitter. A notable value of the ALS Functional Rating Scale Revised (ALSFRS-R) was detected in all muscles: 31 points in the ED muscle, 30 in the orbicularis oculi muscle, and 31 in the frontalis muscle. Below this ALSFRS-R threshold, the percentage of increased jitter was higher, while FD remained relatively low.
UNASSIGNED: SFEMG examination emerges as a valuable tool for better understanding ALS and holds potential for assessing prognosis. Combined jitter and FD analysis showed the strongest correlation with clinical scales. In addition to the ED muscle, the orbicularis oculi muscle may be important in the assessment.

OBJECTIVE: This study aimed to compare trained and untrained Iranian traditional singers on the Dysphonia Severity Index (DSI) and the Singing Voice Handicap Index. The second objective was to examine the relationship between the DSI and Persian version of the Singing Voice Handicap Index (P-SVHI) scores in each group of trained and untrained Iranian traditional singers.
METHODS: This study is a comparative cross-sectional study.
METHODS: This study included 17 trained male Iranian traditional singers who were compared with 17 untrained ones who were matched in terms of age. The P-SVHI was completed by trained and untrained Iranian traditional singers. Measures of jitter, lowest intensity, highest phonational frequency, and maximum phonation time (MPT) were obtained from each participant. The DSI scores were calculated using these values.
RESULTS: Trained male traditional singers had an average age of 33.76 ± 7.45 years, 3.24 ± 1.85 hours of daily practice, 5.24 ± 3.78 years of training, 3.06 ± 2.65 no. of professional performances, and untrained singers had an average age of 32.76 ± 12.92 years, 1.53 ± 1.17 hours of daily practice, and 0.88 ± 1.65 no. of professional performances. Trained singers had lower P-SVHI scores and sub-scores, longer MPT, higher F0 high, lower jitter, and lower I low, resulting in higher DSI values (P < 0.05). There was no significant correlation between P-SVHI scores and DSI values in each group of trained and untrained Iranian traditional singers (P > 0.05).
CONCLUSIONS: The results of this study show that the vocal abilities of professional vocalists are enhanced through voice training (higher DSI scores in trained singers vs untrained ones). Voice training can also help to lessen the perception of a handicap related to the singing voice. Hence, it may be necessary to consider alternative norms for the DSI and P-SVHI when administering them to patients who have undergone guided vocal training, such as voice/singing lessons. The current research indicates that the perception of singing voice handicap and DSI values are two distinct characteristics that do not have a significant correlation.

OBJECTIVE: In this prospective study, we aimed to investigate the difference in voice acoustic parameters between girls with idiopathic central precocious puberty (ICPP) and those who developed normally during prepuberty.
METHODS: Our study recruited 54 girls diagnosed with ICPP and randomly sampled 51 healthy prepubertal girls as the control. Tanner stages, circulating hormone levels and bone ages of the girls with ICPP and the age and body mass index (BMI) of all participants were recorded. Acoustic analyses were performed using PRAAT computer-based voice analysis software and the mean pitch (F0), jitter, shimmer, noise-to harmonic-ratio (NHR) and harmonic-to-noise ratio (HNR) values were compared in the patient and control groups.
RESULTS: The two groups did not significantly differ in age or BMI. In the evaluation of the F0 and jitter values, we were found to be lower in the control group than in the patient group. However, we did not find a statistical significance. The mean shimmer values of the patient group were significantly higher than those of the control group. In addition, a statistically significant difference was noted for the mean HNR and NHR values (P < 0.001). A moderate negative correlation was found between shimmer and hormone levels in the patient group.
CONCLUSIONS: Voice acoustic parameters one of the defining features of girls with ICPP. Voice changes in acoustic parameters could reflect hormonal changes during puberty. Clinicians should suspect ICPP when there is a change in the voice.

Accurate state-of-charge (SOC) estimation is the core index of battery management system (BMS). When the battery equivalent circuit model (ECM) identifies the parameters under complex operating conditions, there is more jitter or even divergence, which will affect the estimation accuracy of battery SOC. To solve this problem, this paper proposes a new algorithm, namely the cross time scale fusion (CTSF) algorithm. Firstly, the cross-time scales Δt1 and Δt2 are determined, the number of cross-time cycles is calculated according to the total amount of complex operating condition data N. Then the ECM parameters are identified in Δt1 by using forgetting factor recursive least square (FFRLS), and the battery SOC is estimated in Δt2 based on the identified parameters, finally the battery parameters are identified and the SOC is estimated by cycling in the cross-time. The experimental results show that, no matter at the same temperature in different conditions or at different temperatures in the same condition, The proposed algorithm not only effectively solves the ECM parameter identification jitter problem, but also improves the accuracy of SOC estimation, the Mean Absolute Error (MAE) minimum of SOC result is 1.42% for different operating conditions at the same temperature and 0.25% for different temperatures at the same operating conditions, respectively.

Random Telegraph Noise (RTN) is one of the major reliability concerns in nanoscale complementary metal-oxide semiconductor (CMOS) technologies. In this paper, we discuss the characterization of RTN in 40 nm CMOS technology using Ring Oscillators (ROSCs). We used different types of ROSCs to study the temporal and spectral characteristics of the RTN. We conducted measurements on one of the arrays with 128 identical ROSC cells. These results enabled statistical characterization of the RTN amplitude strength and its frequency characteristics in different supply voltage variations from 0.5 V to 0.7 V. At power supply of 0.65 V, dominant and observable RTN amplitude above 0.37% Δf/fmean is found in 60% of cells in the array. Further, the capture and emission time constant τe//c can be extracted from the measurements, the values observed ranging from 0.2 μs to 10 ms.
Das zufällige Telegrafenrauschen (RTN) stellt die Zuverlässigkeit von komplementären Metall-Oxid-Halbleiter-Technologien (CMOS) vor große Herausforderungen. In diesem Beitrag diskutieren wir die Charakterisierung von RTN in 40 nm CMOS-Technologie unter Verwendung von Ringoszillatoren (ROSCs). Verschiedene Arten von ROSCs wurden verwendet, um die zeitlichen und spektralen Eigenschaften des RTN anhand eines Arrays mit 128 identischen ROSC-Zellen zu untersuchen. Diese Ergebnisse ermöglichten die statistische Charakterisierung der RTN-Amplitude und ihrer Frequenzcharakteristiken bei Versorgungsspannungsvariationen von 0,5 V bis 0,7 V. Bei einer Versorgungsspannung von 0,65 V wird in 60 % der Zellen des Arrays eine dominante und beobachtbare RTN-Amplitude über 0,37 % Δf/fmean gefunden. Darüber hinaus kann die Einfang- und Emissionszeitkonstante τe//c aus den Messungen extrahiert werden, wobei die beobachteten Werte zwischen 0,2 μs und 10 ms liegen.

Fiber optic gyroscope (FOG)-based north finding is extensively applied in navigation, positioning, and various fields. In dynamic north finding, an accelerated turntable speed shortens the time required for north finding, resulting in a rapid north-finding response. However, with an increase in turntable speed, the turntable\'s jitter contributes to signal contamination in the FOG, leading to a deterioration in north-finding accuracy. This paper introduces a divide-and-conquer algorithm, the segmented cross-correlation algorithm, designed to mitigate the impact of turntable speed jitter. A model for north-finding error is established and analyzed, incorporating FOG\'s self-noise and the turntable\'s speed jitter. To validate the feasibility of our method, we implemented the algorithm on a FOG. The simulation and experimental results exhibited a strong concordance, affirming the validity of our proposed north-finding error model. The experimental findings indicate that, at a turntable speed of 180°/s, the north-finding bias error within a 360 s duration is 0.052°, representing a 64% improvement over the traditional algorithm. These results indicate the effectiveness of the proposed algorithm in mitigating the impact of unstable turntable speeds, offering a solution for north finding with both prompt response and enhanced accuracy.

Multimodal recording using electroencephalogram (EEG) and other biological signals (e.g., muscle activities, eye movement, pupil diameters, or body kinematics data) is ubiquitous in human neuroscience research. However, the precise time alignment of multiple data from heterogeneous sources (i.e., devices) is often arduous due to variable recording parameters of commercially available research devices and complex experimental setups. In this review, we introduced the versatility of a Lab Streaming Layer (LSL)-based application that can overcome two common issues in measuring multimodal data: jitter and latency. We discussed the issues of jitter and latency in multimodal recordings and the benefits of time-synchronization when recording with multiple devices. In addition, a computer simulation was performed to highlight how the millisecond-order jitter readily affects the signal-to-noise ratio of the electrophysiological outcome. Together, we argue that the LSL-based system can be used for research requiring precise time-alignment of datasets. Studies that detect stimulus-induced transient neural responses or test hypotheses regarding temporal relationships of different functional aspects with multimodal data would benefit most from LSL-based systems.