A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades

Modeling the cortical response elicited by wrist manipulation via a nonlinear delay differential embedding

Regarding motor processes, modeling healthy people's brains is essential to understand the brain activity in people with motor impairments. However, little research has been undertaken when external forces disturb limbs, having limited information on physiological pathways. Therefore, in this paper, a nonlinear delay differential embedding model is used to estimate the brain response elicited by externally controlled wrist movement in healthy individuals. The aim is to improve the understanding of the relationship between a controlled wrist movement and the generated cortical activity of healthy people, helping to disclose the underlying mechanisms and physiological relationships involved in the motor event. To evaluate the model, a public database from the Delft University of Technology is used, which contains electroencephalographic recordings of ten healthy subjects while wrist movement was externally provoked by a robotic system. In this work, the cortical response related to movement is identified via Independent Component Analysis and estimated based on a nonlinear delay differential embedding model. After a cross-validation analysis, the model performance reaches 90.21% ± 4.46% Variance Accounted For, and Correlation 95.14% ± 2.31%. The proposed methodology allows to select the model degree, to estimate a general predominant operation mode of the cortical response elicited by wrist movement. The obtained results revealed two facts that had not previously been reported: the movement's acceleration affects the cortical response, and a common delayed activity is shared among subjects. Going forward, identifying biomarkers related to motor tasks could aid in the evaluation of rehabilitation treatments for patients with upper limbs motor impairments.

Non-destructive Raman Method Development for Quantifying Active Pharmaceutical Ingredient in an Oral Suspension Through Plastic Dosing Syringes

For liquid drug products, e.g., solutions or suspensions for oral or parenteral dosing, stability needs to be demonstrated in primary packaging during storage and in dosing devices during in-use periods per quality guidelines from the International Conference on Harmonisation (ICH) and the European Agency for the Evaluation of Medicinal Products (EMEA). One aspect of stability testing for liquid drug products is in-use stability, which typically includes transferring the liquid samples into another container for further sample preparation with extraction diluent and necessary agitation. Samples are then analyzed with traditional chromatography methods, which are laborious, prone to human errors, and time-consuming, especially when this process needs to be repeated multiple times during storage and in-use periods. Being able to analyze the liquid samples non-destructively would significantly improve testing efficiency. We investigated different Raman techniques, including transmission Raman (TRS) and back scatter Raman with a non-contact optic (NCO) probe, as alternative non-destructive tools to the UHPLC method for API quantitation in in-use liquid samples pulled into plastic dosing syringes. The linearity of the chemometric methods for these two techniques was demonstrated by cross-validation sample sets at three levels over an API concentration range of 60 to 80 mg/mL. The accuracy of the chemometric models was demonstrated by the accurate prediction of the API concentrations in independent samples from four different pilot plant batches manufactured at different sites. Both techniques were successful in measuring a signal through a plastic oral dosing syringe, and predicting the suspension API concentration to within 4% of the UHPLC-measured value. For future work, there are opportunities to improve the methodology by exploring additional probes or to expand the range of applications by using different sample presentations (such as prefilled syringes) or formulation matrices for solutions and suspensions.

Fluorescence spectroscopic profiling of urine samples for predicting kidney transplant rejection

Rejection is the primary factor affecting the functionality of a kidney post-transplant, where its prompt prediction of risk significantly influences therapeutic strategies and clinical outcomes. Current graft health assessment methods, including serum creatinine measurements and transplant kidney puncture biopsies, possess considerable limitations. In contrast, urine serves as a direct indicator of the graft's degenerative stage and provides a more accurate measure than peripheral blood analysis, given its non-invasive collection of kidney-specific metabolite. This research entailed collecting fluorescent fingerprint data from 120 urine samples of post-renal transplant patients using hyperspectral imaging, followed by the development of a learning model to detect various forms of immunological rejection. The model successfully identified multiple rejection types with an average diagnostic accuracy of 95.56 %.Beyond proposing an innovative approach for predicting the risk of complications post-kidney transplantation, this study heralds the potential introduction of a non-invasive, rapid, and accurate supplementary method for risk assessment in clinical practice.

Application of Rapid Identification and Determination of Moisture Content of Coptidis Rhizoma From Different Species Based on Data Fusion

BACKGROUND

For thousands of years, traditional Chinese medicine (TCM) has been clinically proven, and doctors have highly valued the differences in utility between different species.

OBJECTIVE

This study aims to replace the complex methods traditionally used for empirical identification by compensating for the information loss of a single sensor through data fusion. The research object of the study is Coptidis rhizoma (CR).

METHOD

Using spectral optimization and data fusion technology, near infrared (NIR) and mid-infrared (MIR) spectra were collected for CR. PLS-DA (n = 134) and PLSR (n = 63) models were established to identify the medicinal materials and to determine the moisture content in the medicinal materials.

RESULTS

For the identification of the three species of CR, the mid-level fusion model performed better than the single-spectrum model. The sensitivity and specificity of the prediction set coefficients for NIR, MIR, and data fusion qualitative models were all higher than 0.95, with an AUC value of 1. The NIR data model was superior to the MIR data model. The results of low-level fusion were similar to those of the NIR optimization model. The RPD of the test set of NIR and low-level fusion model was 3.6420 and 3.4216, respectively, indicating good prediction ability of the model.

CONCLUSIONS

Data fusion technology using NIR and MIR can be applied to identify CR species and to determine the moisture content of CR. It provides technical support for the rapid determination of moisture content, with a fast analysis speed and without the need for complex pretreatment methods.

HIGHLIGHTS

This study is the first to introduce spectral data fusion technology to identify CR species. Data fusion technology is feasible for multivariable calibration model performance and reduces the cost of manual identification. The moisture content of CR can be quickly evaluated, reducing the difficulty of traditional methods.

Accuracy of clinical versus oculographic detection of pathological saccadic slowing

Saccadic slowing as a component of supranuclear saccadic gaze palsy is an important diagnostic sign in multiple neurologic conditions, including degenerative, inflammatory, genetic, or ischemic lesions affecting brainstem structures responsible for saccadic generation. Little attention has been given to the accuracy with which clinicians correctly identify saccadic slowing. We compared clinician (n = 19) judgements of horizontal and vertical saccade speed on video recordings of saccades (from 9 patients with slow saccades, 3 healthy controls) to objective saccade peak velocity measurements from infrared oculographic recordings. Clinician groups included neurology residents, general neurologists, and fellowship-trained neuro-ophthalmologists. Saccades with normal peak velocities on infrared recordings were correctly identified as normal in 57% (91/171; 171 = 9 videos × 19 clinicians) of clinician decisions; saccades determined to be slow on infrared recordings were correctly identified as slow in 84% (224/266; 266 = 14 videos × 19 clinicians) of clinician decisions. Vertical saccades were correctly identified as slow more often than horizontal saccades (94% versus 74% of decisions). No significant differences were identified between clinician training levels. Reliable differentiation between normal and slow saccades is clinically challenging; clinical performance is most accurate for detection of vertical saccade slowing. Quantitative analysis of saccade peak velocities enhances accurate detection and is likely to be especially useful for detection of mild saccadic slowing.

A comparative study of single-channel signal processing methods in fetal phonocardiography

Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.

Estimation of amino acid contents in maize leaves based on hyperspectral imaging

Estimation of the amino acid content in maize leaves is helpful for improving maize yield estimation and nitrogen use efficiency. Hyperspectral imaging can be used to obtain the physiological and biochemical parameters of maize leaves with the advantages of being rapid, non-destructive, and high throughput. This study aims to estimate the multiple amino acid contents in maize leaves using hyperspectral imaging data. Two nitrogen (N) fertilizer experiments were carried out to obtain the hyperspectral images of fresh maize leaves. The partial least squares regression (PLSR) method was used to build the estimation models of various amino acid contents by using the reflectance of all bands, sensitive band range, and sensitive bands. The models were then validated with the independent dataset. The results showed that (1) the spectral reflectance of most amino acids was more sensitive in the range of 400-717.08 nm than other bands. The estimation accuracy was better by using the reflectance of the sensitive band range than that of all bands; (2) the sensitive bands of most amino acids were in the ranges of 505.39-605 nm and 651-714 nm; and (3) among the 24 amino acids, the estimation models of the β-aminobutyric acid, ornithine, citrulline, methionine, and histidine achieved higher accuracy than those of other amino acids, with the R ², relative root mean square error (RE), and relative percent deviation (RPD) of the measured and estimated value of testing samples in the range of 0.84-0.96, 8.79%-19.77%, and 2.58-5.18, respectively. This study can provide a non-destructive and rapid diagnostic method for genetic sensitive analysis and variety improvement of maize.

Rapid Automatized Picture Naming in an Outpatient Concussion Center: Quantitative Eye Movements during the Mobile Universal Lexicon Evaluation System (MULES) Test

Number and picture rapid automatized naming (RAN) tests are useful sideline diagnostic tools. The main outcome measure of these RAN tests is the completion time, which is prolonged with a concussion, yet yields no information about eye movement behavior. We investigated eye movements during a digitized Mobile Universal Lexicon Evaluation System (MULES) test of rapid picture naming. A total of 23 participants with a history of concussion and 50 control participants performed MULES testing with simultaneous eye tracking. The test times were longer in participants with a concussion (32.4 s [95% CI 30.4, 35.8] vs. 26.9 s [95% CI 25.9, 28.0], t=6.1). The participants with a concussion made more saccades per picture than the controls (3.6 [95% CI 3.3, 4.1] vs. 2.7 [95% CI 2.5, 3.0]), and this increase was correlated with longer MULES times (r = 0.46, p = 0.026). The inter-saccadic intervals (ISI) did not differ between the groups, nor did they correlate with the test times. Following a concussion, eye movement behavior differs during number versus picture RAN performance. Prior studies have shown that ISI prolongation is the key finding for a number-based RAN test, whereas this study shows a primary finding of an increased saccade number per picture with a picture-based RAN test. Number-based and picture-based RAN tests may be complimentary in concussion detection, as they may detect different injury effects or compensatory strategies.

Smart-Median: A New Real-Time Algorithm for Smoothing Singing Pitch Contours

Pitch detection is usually one of the fundamental steps in audio signal processing. However, it is common for pitch detectors to estimate a portion of the fundamental frequencies incorrectly, especially in real-time environments and when applied to singing. Therefore, the estimated pitch contour usually has errors. To remove these errors, a contour smoother algorithm should be employed. However, because none of the current contour-smoother algorithms has been explicitly designed to be applied to contours generated from singing, they are often unsuitable for this purpose. Therefore, this article aims to introduce a new smoother algorithm that rectifies this. The proposed smoother algorithm is compared with 15 other smoother algorithms over approximately 2700 pitch contours. Four metrics were used for the comparison. According to all the metrics, the proposed algorithm could smooth the contours more accurately than other algorithms. A distinct conclusion is that smoother algorithms should be designed according to the contour type and the result’s final applications.

King-Devick Test Performance and Cognitive Dysfunction after Concussion: A Pilot Eye Movement Study

(1) Background: The King-Devick (KD) rapid number naming test is sensitive for concussion diagnosis, with increased test time from baseline as the outcome measure. Eye tracking during KD performance in concussed individuals shows an association between inter-saccadic interval (ISI) (the time between saccades) prolongation and prolonged testing time. This pilot study retrospectively assesses the relation between ISI prolongation during KD testing and cognitive performance in persistently-symptomatic individuals post-concussion. (2) Results: Fourteen participants (median age 34 years; 6 women) with prior neuropsychological assessment and KD testing with eye tracking were included. KD test times (72.6 ± 20.7 s) and median ISI (379.1 ± 199.1 msec) were prolonged compared to published normative values. Greater ISI prolongation was associated with lower scores for processing speed (WAIS-IV Coding, r = 0.72, p = 0.0017), attention/working memory (Trails Making A, r = −0.65, p = 0.006) (Digit Span Forward, r = 0.57, p = −0.017) (Digit Span Backward, r= −0.55, p = 0.021) (Digit Span Total, r = −0.74, p = 0.001), and executive function (Stroop Color Word Interference, r = −0.8, p = 0.0003). (3) Conclusions: This pilot study provides preliminary evidence suggesting that cognitive dysfunction may be associated with prolonged ISI and KD test times in concussion.

Neural correlates of intra-saccadic motion perception

Retinal motion of the visual scene is not consciously perceived during ocular saccades in normal everyday conditions. It has been suggested that extra-retinal signals actively suppress intra-saccadic motion perception to preserve stable perception of the visual world. However, using stimuli optimized to preferentially activate the M-pathway, Castet and Masson (2000) demonstrated that motion can be perceived during a saccade. Based on this psychophysical paradigm, we used electroencephalography and eye-tracking recordings to investigate the neural correlates related to the conscious perception of intra-saccadic motion. We demonstrated the effective involvement during saccades of the cortical areas V1-V2 and MT-V5, which convey motion information along the M-pathway. We also showed that individual motion perception was related to retinal temporal frequency.

Optimal Sizing of Hybrid Wind-Solar Power Systems to Suppress Output Fluctuation

Harnessing wind energy is one of the fastest-growing areas in the energy industry. However, wind power still faces challenges, such as output intermittency due to its nature and output reduction as a result of the wake effect. Moreover, the current practice uses the available renewable energy resources as a fuel-saver simply to reduce fossil-fuel consumption. This is related mainly to the inherently variable and non-dispatchable nature of renewable energy resources, which poses a threat to power system reliability and requires utilities to maintain power-balancing reserves to match the supply from renewable energy resources with the real-time demand levels. Thus, further efforts are needed to mitigate the risk that comes with integrating renewable resources into the electricity grid. Hence, an integrated strategy is being created to determine the optimal size of the hybrid wind-solar photovoltaic power systems (HWSPS) using heuristic optimization with a numerical iterative algorithm such that the output fluctuation is minimized. The research focuses on sizing the HWSPS to reduce the impact of renewable energy resource intermittency and generate the maximum output power to the grid at a constant level periodically based on the availability of the renewable energy resources. The process of determining HWSPS capacity is divided into two major steps. A genetic algorithm is used in the initial stage to identify the optimum wind farm. A numerical iterative algorithm is used in the second stage to determine the optimal combination of photovoltaic plant and battery sizes in the search space, based on the reference wind power generated by the moving average, Savitzky–Golay, Gaussian and locally weighted linear regression techniques. The proposed approach has been tested on an existing wind power project site in the southern part of the Sultanate of Oman using a real weather data. The considered land area dimensions are 2 × 2 km. The integrated tool resulted in 39 MW of wind farm, 5.305 MW of PV system, and 0.5219 MWh of BESS. Accordingly, the estimated cost of energy based on the HWSPS is 0.0165 EUR/kWh.

How sandbag-able are concussion sideline assessments? A close look at eye movements to uncover strategies

Background: Sideline diagnostic tests for concussion are vulnerable to volitional poor performance ("sandbagging") on baseline assessments, motivated by desire to subvert concussion detection and potential removal from play. We investigated eye movements during sandbagging versus best effort on the King-Devick (KD) test, a rapid automatized naming (RAN) task.Methods: Participants performed KD testing during oculography following instructions to sandbag or give best effort.Results: Twenty healthy participants without concussion history were included (mean age 27 ± 8 years). Sandbagging resulted in longer test times (89.6 ± 39.2 s vs 48.2 ± 8.5 s, p < .001), longer inter-saccadic intervals (459.5 ± 125.4 ms vs 311.2 ± 79.1 ms, p < .001) and greater numbers of saccades (171.4 ± 47 vs 138 ± 24.2, p < .001) and reverse saccades (wrong direction for reading) (21.2% vs 11.3%, p < .001). Sandbagging was detectable using a logistic model with KD times as the only predictor, though more robustly detectable using eye movement metrics.Conclusions: KD sandbagging results in eye movement differences that are detectable by eye movement recordings and suggest an invalid test score. Objective eye movement recording during the KD test shows promise for distinguishing between best effort and post-injury performance, as well as for identifying sandbagging red flags.

Neural modeling of antisaccade performance of healthy controls and early Huntington's disease patients

Huntington's disease (HD), a genetically determined neurodegenerative disease, is positively correlated with eye movement abnormalities in decision making. The antisaccade conflict paradigm has been widely used to study response inhibition in eye movements, and reliable performance deficits in HD subjects have been observed, including a greater number and timing of direction errors. We recorded the error rates and response latencies of early HD patients and healthy age-matched controls performing the mirror antisaccade task. HD participants displayed slower and more variable antisaccade latencies and increased error rates relative to healthy controls. A competitive accumulator-to-threshold neural model was then employed to quantitatively simulate the controls' and patients' reaction latencies and error rates and uncover the mechanisms giving rise to the observed HD antisaccade deficits. Our simulations showed that (1) a more gradual and noisy rate of accumulation of evidence by HD patients is responsible for the observed prolonged and more variable antisaccade latencies in early HD; (2) the confidence level of early HD patients making a decision is unaffected by the disease; and (3) the antisaccade performance of healthy controls and early HD patients is the end product of a neural lateral competition (inhibition) between a correct and an erroneous decision process, and not the end product of a third top-down stop signal suppressing the erroneous decision process as many have speculated.

An automated segmentation approach to calibrating infantile nystagmus waveforms

Infantile nystagmus (IN) describes a regular, repetitive movement of the eyes. A characteristic feature of each cycle of the IN eye movement waveform is a period in which the eyes are moving at minimal velocity. This so-called “foveation” period has long been considered the basis for the best vision in individuals with IN. In recent years, the technology for measuring eye movements has improved considerably, but there remains the challenge of calibrating the direction of gaze in tracking systems when the eyes are continuously moving. Identifying portions of the nystagmus waveform suitable for calibration typically involves time-consuming manual selection of the foveation periods from the eye trace. Without an accurate calibration, the exact parameters of the waveform cannot be determined. In this study, we present an automated method for segmenting IN waveforms with the purpose of determining the foveation positions to be used for calibration of an eye tracker. On average, the “point of regard” was found to be within 0.21° of that determined by hand-marking by an expert observer. This method enables rapid clinical quantification of waveforms and the possibility of gaze-contingent research paradigms being performed with this patient group.