Standard Deviation Error Research Articles

Optimizing the input feature sets and machine learning algorithms for reliable and accurate estimation of continuous, cuffless blood pressure

The advent of mobile devices, wearables and digital healthcare has unleashed a demand for accurate, reliable, and non-interventional ways to measure continuous blood pressure (BP). Many consumer products claim to measure BP with a cuffless device, but their lack of accuracy and reliability limit clinical adoption. Here, we demonstrate how multimodal feature datasets, comprising: (i) pulse arrival time (PAT); (ii) pulse wave morphology (PWM), and (iii) demographic data, can be combined with optimized Machine Learning (ML) algorithms to estimate Systolic BP (SBP), Diastolic BP (DBP) and Mean Arterial Pressure (MAP) within a 5 mmHg bias of the gold standard Intra-Arterial BP, well within the acceptable limits of the IEC/ANSI 80601-2-30 (2018) standard. Furthermore, DBP’s calculated using 126 datasets collected from 31 hemodynamically compromised patients had a standard deviation within 8 mmHg, while SBP’s and MAP’s exceeded these limits. Using ANOVA and Levene’s test for error means and standard deviations, we found significant differences in the various ML algorithms but found no significant differences amongst the multimodal feature datasets. Optimized ML algorithms and key multimodal features obtained from larger real-world data (RWD) sets could enable more reliable and accurate estimation of continuous BP in cuffless devices, accelerating wider clinical adoption.

Microbiological Profile of Primary Teeth with Irreversible Pulpitis and Pulp Necrosis with/without Abscess and their Susceptibility to Three Antibiotics as Intracanal Medication.

The ineffective disinfection potential of conventional intracanal medicaments to eliminate enteropathogens from root canal systems leads to their persistence contributing to endodontic treatment failures. Hence, the use of appropriate intracanal medicament becomes the essential phase to accomplishing comprehensive decontamination of the root canal system. When applied topically as an intracanal medicament, antibiotics eradicate residual microorganisms from tortuous endodontic spaces, minimizing the risk of systemic toxicity. To evaluate the prevalence of various bacterial species associated with signs of irreversible pulpitis and pulp necrosis with/without abscess in primary teeth root canals and their susceptibility against three antimicrobial agents. The pulp tissue and organic debris were retrieved from deciduous teeth (n = 50) from children between the age of 3-10 years and cultured. The bacterial identification and antibacterial profiling of isolated bacteria were done against clindamycin, metronidazole, and doxycycline through minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assay. The MIC and MBC of each antibiotic were expressed as mean ± standard deviation (SD), range, and standard error (SE of the mean). The intergroup comparisons were done by the Kruskal-Wallis test, while intragroup pair-wise comparisons were done using the Wilcoxon signed-rank test. The confidence level will be set at 95%. Aerobic bacteria were found in 54%, microaerophilic bacteria in 76%, facultative anaerobes in 26%, and obligatory anaerobes were isolated from 30% of teeth. The intragroup and intergroup comparisons of test agent MIC revealed a nonsignificant difference (p > 0.05). The intragroup MBC comparisons of all the test agents revealed statistically nonsignificant (p > 0.05), while intergroup comparisons demonstrated nonsignificant (p > 0.05) to highly significant difference (p < 0.001). Clindamycin demonstrated promising antibacterial activity against most of the isolated bacteria, while against metronidazole and doxycycline, most of the bacteria were moderate to highly resistant. Determining the antibacterial agents' efficacy along with modifications can help to target maximum pathogenic microbes and reduce catastrophic endodontic therapy failures. Dahake PT, Kothari S. Microbiological Profile of Primary Teeth with Irreversible Pulpitis and Pulp Necrosis with/without Abscess and their Susceptibility to Three Antibiotics as Intracanal Medication. Int J Clin Pediatr Dent 2023;16(2):312-320.

Depletion of 13C in CO in the Atmosphere of Mars Suggested by ExoMars-TGO/NOMAD Observations

The atmosphere of Mars is mainly composed by carbon dioxide (CO2). It has been predicted that photodissociation of CO2 depletes 13C in carbon monoxide (CO). We present the carbon 13C/12C isotopic ratio in CO at 30–50 km altitude from the analysis of the solar occultation measurements taken by the instrument Nadir and Occultation for Mars Discovery on board the ExoMars Trace Gas Orbiter (ExoMars-TGO). We retrieve 12C16O, 13C16O, and 12C18O volume mixing ratios from the spectra taken at 4112–4213 cm−1, where multiple CO isotope lines with similar intensities are available. The intensities of the 12C16O lines in this spectral range are particularly sensitive to temperature, thus we derive the atmospheric temperature by retrieving CO2 density with simultaneously measured spectra at 2966–2990 cm−1. The mean δ 13C value obtained from the 13C16O/12C16O ratios is −263‰, and the standard deviation and standard error of the mean are 132‰ and 4‰, respectively. The relatively large standard deviation is due to the strong temperature dependences in the 12C16O lines. We also examine the 13C16O/12C18O ratio, whose lines are less sensitive to temperature. The mean δ value obtained with 12C18O instead of 12C16O is −82‰ with smaller standard deviation, 60‰. These results suggest that CO is depleted in 13C when compared to CO2 in the Martian atmosphere as measured by the Curiosity rover. This depletion of 13C in CO is consistent with the CO2 photolysis-induced fractionation, which might support a CO-based photochemical origin of organics in Martian sediments.

Implementation of variance reduction techniques applied to the pricing of investment certificates

Certificates are structured financial instruments that aim to provide investors with investment solutions tailored to their needs. Certificates can be modeled using a bond component and a derivative component, typically an options strategy. The pricing of certificates is typically performed using the Monte Carlo numerical methodology. Such method allows for projections of the underlying using series of random numbers. The results obtained display an error (standard deviation) that depends on the number of simulations used and on the specific characteristics of the structured product. This work has the objective of minimizing the experimental error, and, consequently, of accelerating the speed of convergence using statistical techniques known in the literature as variance reduction methods. The most popular stochastic dynamics have been analyzed, like the classical Black and Scholes model, the Local Volatility model and the Heston model. Three certificates are analyzed in the paper and they are characterized by different payoffs. The variance reduction techniques, implemented in different programming languages (Python, Matlab and R), are: Latin Hypercube, Stratified Sampling, Antithetic Variables, Importance Sampling, Moment Matching and Control Variates

Determining minimal clinically important differences in the North Star Ambulatory Assessment (NSAA) for patients with Duchenne muscular dystrophy.

The North Star ambulatory assessment (NSAA) is a functional motor outcome measure in Duchenne muscular dystrophy (DMD), widely used in clinical trials and natural history studies, as well as in clinical practice. However, little has been reported on the minimal clinically important difference (MCID) of the NSAA. The lack of established MCID estimates for NSAA presents challenges in interpreting the significance of the results of this outcome measure in clinical trials, natural history studies and clinical practice. Combining statistical approaches and patient perspectives, this study estimated MCID for NSAA using distribution-based estimates of 1/3 standard deviation (SD) and standard error of measurement (SEM), an anchor-based approach, with six-minute walk distance (6MWD) as the anchor, and evaluation of patient and parent perception using participant-tailored questionnaires. The MCID for NSAA in boys with DMD aged 7 to 10 years based on 1/3 SD ranged from 2.3-2.9 points, and that on SEM ranged from 2.9-3.5 points. Anchored on the 6MWD, the MCID for NSAA was estimated as 3.5 points. When the impact on functional abilities was considered using participant response questionnaires, patients and parent perceived a complete loss of function in a single item or deterioration of function in one to two items of the assessment as an important change. Our study examines MCID estimates for total NSAA scores using multiple approaches, including the impact of patient and parent perspective on within scale changes in items based on complete loss of function and deterioration of function, and provides new insight on evaluation of differences in these widely used outcome measure in DMD.

Accuracy of measuring scapular position and motion with a novel motion capture system

Scapula kinematics is recognized to be a crucial variable in shoulder dysfunction. Nevertheless, quantitative scapula tracking and measurement are not part of the current clinical evaluation. The main concern is measurement accuracy. To assess the accuracy of the wearable sensor technology Showmotion a cadaver experiment was designed, allowing a direct comparison between sensors directly pinned to the scapula and superficial sensors. A measurement protocol was adopted to evaluate errors in measurement, mimicking the suggested invivo evaluation. Sensors were simultaneously placed above (supraspinal) and below (infraspinal) the scapular spine to determine if one placement resulted in fewer errors compared to the other. Mean and standard deviations of the supraspinal sensor root mean square error (RMSE) in flexion-extension movements resulted in 3.59° ± 2.36°, 4.73° ± 2.98°, and 6.26° ± 3.62° for upward-downward rotation (up-down), anterior-posterior tilt and internal-external (intra-extra) rotation, respectively, while 2.16° ± 1.21°, 2.20° ± 1.02°, and 4.46° ± 2.16° for the infraspinal sensor. In abduction-adduction movements, mean and standard deviations of the supraspinal sensor RMSE resulted in 4.26° ± 2.98°, 5.68° ± 4.22°, and 7.04° ± 4.36° for up-down rotation, anterior-posterior tilt, and intra-extra rotation, respectively, while 2.38° ± 1.63°, 2.47° ± 1.77°, and 4.92° ± 3.14° for the infraspinal sensor. The same behavior was confirmed in shrug movements, where 4.35° ± 3.24°, 4.63° ± 3.09°, and 5.34° ± 6.67° are mean and standard deviations of the supraspinal sensor RMSE for up-down rotation, anterior-posterior tilt, and intra-extra rotation, respectively, while 2.76° ± 1.87°, 2.83° ± 2.53°, and 4.68° ± 5.22° for the infraspinal sensor. This method of quantitative assessment of scapular motion is shown to have good accuracy and low error between the sensor measurements and actual bone movement in multiple planes of scapular motion, both over the entire range of motion and in its individual segment intervals. The decreased amount of error with the infraspinal sensor placement suggests that placement is ideal for clinical quantitative assessment of scapular motion.

A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics.

Traditional cuff-based sphygmomanometers for measuring blood pressure can be uncomfortable and particularly unsuitable to use during sleep. A proposed alternative method uses dynamic changes in the pulse waveform over short intervals and replaces calibration with information from photoplethysmogram (PPG) morphology to provide a calibration-free approach using a single sensor. Results from 30 patients show a high correlation of 73.64% for systolic blood pressure (SBP) and 77.72% for diastolic blood pressure (DBP) between blood pressure estimated with the PPG morphology features and the calibration method. This suggests that the PPG morphology features could replace the calibration stage for a calibration-free method with similar accuracy. Applying the proposed methodology on 200 patients and testing on 25 new patients resulted in a mean error (ME) of -0.31 mmHg, a standard deviation of error (SDE) of 4.89 mmHg, a mean absolute error (MAE) of 3.32 mmHg for DBP and an ME of -4.02 mmHg, an SDE of 10.40 mmHg, and an MAE of 7.41 mmHg for SBP. These results support the potential for using a PPG signal for calibration-free cuffless blood pressure estimation and improving accuracy by adding information from cardiovascular dynamics to different methods in the cuffless blood pressure monitoring field.

Development of a method for determining the position of an object using a typical form of its image

Violating the observation conditions for the investigated objects leads to the formation of diverse typical forms of objects throughout the frame in the series. As a consequence, determining the exact position of the object on the frame becomes difficult. To this end, a method was devised to determine the position of an object using the typical form of its image on a series of frames. This method is based on the formation of a typical form of a digital image of an object based on data from all frames of the series. This makes it possible to take into account the peculiarities of the very formation of the digital image of an object on each frame of the original series. Based on this, a more accurate assessment of the initial approximation of the parameters of all Gaussians of the object's image is performed. Adapting the method specifically for the typical form allows for a more accurate assessment of the positional parameters (coordinates) of the object in comparison with the analytically set profile. The estimation of the position of an object was obtained using the method of least squares. After that, minimization was performed using the Levenberg-Marquardt algorithm. Also, the use of the method makes it possible to improve identification with reference objects and reduce the number of false detections. The study showed a reduction in the standard deviation of frame identification errors by 7–10 times when using a typical digital image shape. The method devised for determining the position of an object using the typical form of its image was tested in practice within the framework of the CoLiTec project. It was implemented in the intraframe processing unit of the Lemur software to automatically detect new objects and track known ones. Owing to the use of Lemur software and the proposed computational method implemented in it, more than 700,000 measurements of various objects under study were successfully processed and identified

Further validation of the estimates of the downwelling solar radiation at ground level in cloud-free conditions provided by the McClear service: the case of Sub-Saharan Africa and the Maldives Archipelago

Abstract. Being part of the Copernicus Atmosphere Monitoring Service (CAMS), the McClear service provides estimates of the downwelling shortwave irradiance and its direct and diffuse components received at ground level in cloud-free conditions, with inputs on ozone, water vapor and aerosol properties from CAMS. McClear estimates have been validated over several parts of the world by various authors. This article makes a step forward by comparing McClear estimates to measurements performed at 44 ground-based stations located in Sub-Saharan Africa and the Maldives Archipelago in the Indian Ocean. The global irradiance received on a horizontal surface (G) and its direct component received at normal incidence (BN) provided by the McClear-v3 service were compared to 1 min measurements made in cloud-free conditions at the stations. The correlation coefficient is greater than 0.96 for G, whereas it is greater than 0.70 at all stations but five for BN. The mean of G is accurately estimated at stations located in arid climates (BSh, BWh, BSk, BWk) and temperate climates without a dry season and a hot or warm summer (Cfa, Cfb) or with a dry and hot summer (Csa) with a relative bias in the range [−1.5, 1.5] % with respect to the means of the measurements at each station. It is underestimated in tropical climates of monsoon type (Am) and overestimated in tropical climates of savannah type (Aw) and temperate climates with a dry winter and hot (Cwa) or warm (Cwb) summer. The McClear service tends to overestimate the mean of BN. The standard deviation of errors for G ranges between 13 W m−2 (1.3 %) and 31 W m−2 (3.7 %) and that for BN ranges between 31 W m−2 (3.0 %), and 70 W m−2 (7.9 %). Both offer small variations in time and space. A review of previous works reveals no significant difference between their results and ours. This work establishes a general overview of the performances of the McClear service.

Digital Technologies for Assessing and Predicting the Impact of Spatiotemporal Distribution of Greenhouse Gases on the Photosynthetic Activity of Crops

Purpose of research: formation of a bank of models for the implementation of simulation experiments to assess and predict the values of greenhouse gas concentrations in the surface layer of the territory's atmosphere based on artificial neural networks and GIS technologies. The influence of the increased concentration of carbon dioxide in the surface layer of the atmosphere on the growth and development of agricultural plants, namely on the change in photosynthetic activity, the level of mineralization of the humus layer of the soil, which affects crop yields, is considered. The peculiarities of agricultural production determine the relevance of the creation and implementation of a new intelligent technology that will provide the opportunity to identify optimal parameters of crop production.Methods. The formation of a training sample for a neural network was carried out through numerical experiments and mathematical modeling methods. To select the best neural network topology for predicting the concentration of greenhouse gases in the territory under consideration, experiments were conducted that revealed the standard deviation and relative error. To assess the predictive abilities of the models, field experiments were conducted to measure CO2 concentrations in the surface layer of the atmosphere in the agricultural territories of the Belgorod region.Results. A software toolkit has been developed that makes it possible to visualize the dispersion and accumulation of greenhouse gases in the surface layer of the atmosphere. This makes it possible to conduct simulation experiments necessary to determine the territories that are under the influence of man-made sources. An assessment of the photosynthetic activity of plants in the selected territory was carried out, which allows us to form further recommendations for the effective use of agricultural territory aimed at increasing crop yields.Conclusion. The paradigms of neural networks were considered, experiments were conducted to identify the best topology. A software toolkit has been developed to visualize the dispersion and accumulation of greenhouse gases in the surface layer of the atmosphere for decision makers. The effects of technogenic factors on the photosynthetic apparatus of agricultural plants are analyzed, on the basis of which conclusions and practical recommendations for the cultivation of agricultural crops are formulated.

Reliability Analysis of Vertebral Landmark Labelling on Lumbar Spine X-ray Images.

Vertebral landmark labelling on X-ray images is important for objective and quantitative diagnosis. Most studies related to the reliability of labelling focus on the Cobb angle, and it is difficult to find studies describing landmark point locations. Since points are the most fundamental geometric feature that can generate lines and angles, the assessment of landmark point locations is essential. The aim of this study is to provide a reliability analysis of landmark points and vertebral endplate lines with a large number of lumbar spine X-ray images. A total of 1000 pairs of anteroposterior and lateral view lumbar spine images were prepared, and 12 manual medicine experts participated in the labelling process as raters. A standard operating procedure (SOP) was proposed by consensus of the raters based on manual medicine and provided guidelines for reducing sources of error in landmark labelling. High intraclass correlation coefficients ranging from 0.934 to 0.991 verified the reliability of the labelling process using the proposed SOP. We also presented means and standard deviations of measurement errors, which could be a valuable reference for evaluating both automated landmark detection algorithms and manual labelling by experts.

Comparative Analysis of Selected Geostatistical Methods for Bottom Surface Modeling.

Digital bottom models are commonly used in many fields of human activity, such as navigation, harbor and offshore technologies, or environmental studies. In many cases, they are the basis for further analysis. They are prepared based on bathymetric measurements, which in many cases have the form of large datasets. Therefore, various interpolation methods are used for calculating these models. In this paper, we present the analysis in which we compared selected methods for bottom surface modeling with a particular focus on geostatistical methods. The aim was to compare five variants of Kriging and three deterministic methods. The research was performed with real data acquired with the use of an autonomous surface vehicle. The collected bathymetric data were reduced (from about 5 million points to about 500 points) and analyzed. A ranking approach was proposed to perform a complex and comprehensive analysis integrating typically used error statistics-mean absolute error, standard deviation and root mean square error. This approach allowed the inclusion of various views on methods of assessment while integrating various metrics and factors. The results show that geostatistical methods perform very well. The best results were achieved with the modifications of classical Kriging methods, which are disjunctive Kriging and empirical Bayesian Kriging. For these two methods, good statistics were calculated compared to other methods (for example, the mean absolute error for disjunctive Kriging was 0.23 m, while for universal Kriging and simple Kriging, it was 0.26 m and 0.25 m, respectively). However, it is worth mentioning that interpolation based on radial basis function in some cases is comparable to Kriging in its performance. The proposed ranking approach was proven to be useful and can be utilized in the future for choosing and comparing DBMs, mostly in mapping and analyzing seabed changes, for example in dredging operations. The research will be used during the implementation of the new multidimensional and multitemporal coastal zone monitoring system using autonomous, unmanned floating platforms. The prototype of this system is at the design stage and is expected to be implemented.

Photospectral Data Obtaining with the Unmanned Aerial Spectrometry Vehicle

Study of the Earth’s surface objects reflectance characteristics with unmanned aerial vehicles is one of the most actual remote sensing trends. Aim of this work was to develop a method for obtaining of photospectral data using unmanned aerial spectrometry vehicle.An adaptation of the cameras spatial resolution evaluating technique based on a specialized target photographic fixation was proposed. A method for synchronizing of the camera and spectrometer of the videospectral device was also proposed. It was based on an experiment with spectra and screen images recording. Different colors were sequentially displayed on the screen. The percentage contribution of each of colors to the “mixed” spectra was calculated. So the out-of-sync time estimation became possible. In addition the work proposed the method for combining images and spectra with their merging into photospectral images. The method allows to consider the aircraft displacement when linking the spectrometer field of view to the RGB image. The way for photospectral images combining based on the images key points detectors was also proposed.Spatial resolutions for 3 aerial vehicle cameras were obtained. The study showed that the spatial resolution decrease of Zenmuse H20T caused by the device carrier movement with a speed of up to 5 m/s can be ignored. The videospectral device camera and spectrometer out-of-sync time was evaluated. An automatic merging of a set of images using key points detection was made. The spectrometry areas were linked to the panoramic image. The reflectance coefficients were obtained for each of the areas in the range of 350–900 nm. The areas to image linking accuracy was 84.9 ± 11.6 %.A discrepancy between the angular spatial resolution values got experimentally and theoretically was revealed as a result of the cameras spatial resolution evaluating. This indicates the importance of the imaging equipment spatial resolution experimental evaluation. The videospectral device spectrometer and observation camera out-of-sync time evaluation made it possible to correct the data recording time. This led to the timing error standard deviation reduction from 142 ms to 15 ms. The way for the unmanned aerial spectrometry vehicle data obtaining in a photospectral representation was developed. The proposed methods and techniques can be used in similar unmanned systems.

Model-informed deep learning strategy with vision measurement for damage identification of truss structures

Structural damage identification approaches can be divided into two categories, i.e. data-driven approaches via statistical pattern recognition and model-based approaches via finite element (FE) model updating. These two approaches have their own merits, and their main shortcomings can be remedied by each other’s merits. Therefore, this study proposed a deep learning-based damage identification strategy involving both data-driven and model-based approaches, termed as model-informed deep learning (MIDL)-based strategy. This strategy first proposes a vision-based displacement estimation approach to extract structural displacement responses from video data. This approach reduces the displacement drift induced by conventional optical flow approaches and improves the tracking accuracy of feature points. Then, a calibrated FE model is built to construct data sets with different damage levels via FE model updating and time-history analysis. Following this, a one-dimensional convolutional neural network (1D CNN) is established to detect and localize structural damage by using direct displacement responses. Finally, FE model updating is performed again to quantify structural damage level with constrained targets. A truss structure is further used to evaluate the accuracy of the proposed strategy experimentally. Results illustrate that the proposed MIDL strategy, which uses time series to localize the structural damage, achieves a global location accuracy of 86.09% and avoids the feature extraction process. Meanwhile, with the known damage location, the efficiency and accuracy of structural damage quantification via rerunning model updating can also be significantly improved. In addition, the displacements estimated by the proposed approach have a good match with ground truth values with error standard deviations of less than 0.3 mm.

Comparison of the accuracy of nine intraocular lens power calculation formulas using partial coherence interferometry.

Cataract surgery is the most performed procedure in the world. To achieve the target refraction, several intraocular lens (IOL) power calculation formulas have been developed to improve the accuracy of IOL power predictions. We compared the accuracy of 9 IOL power calculation formulas (SRK/T, Hoffer Q, Holladay 1, Haigis, Barrett Universal II, Kane, EVO 2.0, Ladas Super formula and Hill-RBF 3.0) using partial coherence interferometry (PCI). We collected data from patients who underwent uncomplicated cataract surgery with implantation of 1 of 3 IOL types currently used in our center. All preoperative biometric measurements were performed using PCI. Prediction errors (PE) were deduced from refractive outcomes evaluated 3 months after surgery. The mean prediction error (ME), mean absolute prediction error (MAE), median absolute prediction error (MedAE), and standard deviation of prediction error (SD) were calculated, as well as the percentage of eyes with a PE within ±0.25, ±0.50, ±0.75 and ±1.00D for each formula. We included 126 eyes of 126 patients. Kane achieved the lowest MAE and SD across the entire sample as well as the highest percentage of PE within ±0.50D and was shown to be more accurate than Haigis and Hoffer Q (P<001). For an axial length of more than 26.0mm, EVO 2.0 and Barrett obtained the lowest MAEs, with EVO 2.0 and Kane showing a higher percentage of prediction at ±0.50D compared to old generation formulas except for SRK/T (P=04). All investigated formulas achieved good results; there was a tendency toward better outcomes with new generation formulas, especially in atypical eyes.

Comparison of Keratoconus Specific to Standard IOL Formulas in Patients With Keratoconus Undergoing Cataract Surgery.

To assess the performance of multiple intraocular lens (IOL) formulas in eyes with keratoconus. Eyes with stable keratoconus scheduled for cataract surgery with biometry measurements on the Lenstar LS900 (Haag-Streit) were included. Prediction errors were calculated using 11 different formulas, including two with keratoconus modifiers. Primary outcomes compared standard deviations, mean and median numerical errors, and percentage of eyes within diopter (D) ranges across all eyes with subgroup analysis according to anterior keratometric values. Sixty-eight eyes from 44 patients were identified. In eyes with keratometric values less than 50.00 D, prediction error standard deviations ranged from 0.680 to 0.857 D. Percentages of eyes within ±0.50 D of target ranged from 57.89% to 73.68% with no statistical differences among formulas. In eyes with a keratometric value of more than 50.00 D, prediction error standard deviations ranged from 1.849 to 2.349 D and were not statistically different with heteroscedastic analysis; percentages of eyes within ±0.50 D of target ranged from 0% to 18.18% with no statistical differences among formulas. Only keratoconus-specific formulas (Barrett-KC and Kane-KC) and the Wang-Koch axial length adjustment version of SRK/T resulted in median numerical errors not significantly different than 0, regardless of keratometric values. In keratoconic eyes, IOL formulas are less accurate than in normal eyes and result in hyperopic refractive outcomes that increase with steeper keratometric values. Using keratoconus-specific formulas and the Wang-Koch axial length adjustment version of SRK/T for axial lengths of 25.2 mm or greater improved IOL power prediction accuracy compared to other formulas. [J Refract Surg. 2023;39(4):242-248.].

Toward a better understanding of uncertainty for satellite precipitation products over complex terrain with sparse rain gauge data

Satellite precipitation product quality is critical for various meteorological and hydrological applications. Products are subjected to more uncertainties in sparsely-gauged regions than in densely-gauged regions due to insufficient error correction. However, ground-truth-based validation cannot accurately quantify the spatially distributed uncertainty of satellite precipitation products over complex terrain with sparse rain gauge data. In this study, we explored a gauge-independent method, called the Three-Cornered Hat (TCH), to overcome the quantification issue and better understand the uncertainty of satellite precipitation products in such situations. Firstly, the reliability of the TCH method was explored over a topographically complex area. Then, the performance of the Soil Moisture TO RAIN-Advanced SCATterometer (SM2RAIN-ASCAT) and Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) in the Chuan-Yu region was evaluated using TCH, with the help of the China Merged Precipitation Analysis (CMPA) precipitation product as the third input. We found that: (1) The high consistency between error standard deviation (ESD) estimated by TCH and root mean square error (RMSE) estimated by ground-truth-based validation demonstrated that TCH is reliable for evaluating precipitation datasets over complex terrain; (2) The ESDs of SM2RAIN-ASCAT and IMERG had different spatial distributions, and the quality of IMERG was overall higher than that of SM2RAIN-ASCAT over a mountainous area; (3) IMERG performed better than SM2RAIN-ASCAT both at low (under 3000 m) and high elevations (over 3000 m) in most years, as well as for most main land cover types (woody savannas, mixed forests, and grasslands) in the study area. This method and these findings provide a new insight for better use of satellite precipitation products over complex terrain.

Evaluation of RETRO-Mapping for electrophysiological features including direction of plane activity during atrial fibrillation using multipolar catheters in humans.

A quantifiable, automated standard of analyzing heart rhythm has long eluded cardiologists due, in part, to the limitations in technology and the ability to analyze large electrogram datasets. In this proof-of-concept study, we propose new measures to quantify plane activity in atrial fibrillation (AF) using our Representation of Electrical Tracking of Origin (RETRO)-Mapping software. We recorded 30 s segments of electrograms at the lower posterior wall of the left atrium using a 20-pole double loop catheter (AFocusII). The data were analyzed with the custom RETRO-Mapping algorithm in MATLAB. Thirty secondsegments were analyzed for number of activation edges, conduction velocity (CV), cycle length (CL), activation edge direction, and wavefront direction. These features were compared across 34 613 plane edges in three types of AF: persistent AF treated with amiodarone (11 906 wavefronts), persistent AF without amiodarone (14 959 wavefronts), and paroxysmal AF (7748 wavefronts). Change in activation edge direction between subsequent frames and change in overall wavefront direction between subsequent wavefronts were analyzed. All activation edge directions were represented across the lower posterior wall. The median change in activation edge direction followed a linear pattern for all three types of AF with R2 = 0.932 for persistent AF treated without amiodarone, R2 = 0.942 for paroxysmal AF, and R2 = 0.958 for persistent AF treated with amiodarone. All medians and the standard deviation error bars remained below 45° (suggesting all activation edges were traveling within a 90° sector, a criterion for plane activity). The directions of approximately half of all wavefronts (56.1% for persistent without amiodarone, 51.8% for paroxysmal, 48.8% for persistent with amiodarone) were predictive of the directions of the subsequent wavefront. RETRO-Mapping can measure electrophysiological features of activation activity and this proof-of-concept study suggests that this can be extended to the detection of plane activity in three types of AF. Wavefront direction may have a role in future work for predicting plane activity. For this study, we focused more on the ability of the algorithm to detect plane activity and less the differences between the types of AF. Future work should be in validating these results with a larger data set and comparing with other types of activation such as rotational, collision, and focal. Ultimately, this work can be implemented in real-time for prediction of wavefronts during ablation procedures.

Deep Learning-Based Univariate Prediction of Daily Rainfall: Application to a Flood-Prone, Data-Deficient Country

There are several attempts to model rainfall time series which have been explored by members of the hydrological research communities. Rainfall, being one of the defining factors for a flooding event, is rarely modeled singularly in deep learning, as it is usually performed in multivariate analysis. This study will attempt to explore a time series modeling method in four subcatchments located in Samar, Philippines. In this study, the rainfall time series was treated as a signal and was reconstructed into a combination of a ‘smoothened’ or ‘denoised’ signal, and a ‘detailed’ or noise signal. The discrete wavelet transform (DWT) method was used as a reconstruction technique, in combination with the univariate long short-term memory (LSTM) network method. The combination of the two methods showed consistently high values of performance indicators, such as Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), Kling–Gupta efficiency (KGE), index of agreement (IA), and Legates–McCabe index (LMI), with mean average percentage error (MAPE) values at almost zero, and consistently low values for both residual mean square error (RMSE) and RMSE-observations standard deviation ratio (RSR). The authors believe that the proposed method can give efficient, time-bound results to flood-prone countries such as the Philippines, where hydrological data are deficient.

Quantitative Determination of Zileuton from Spiked Human Plasma Using Liquid-Liquid Extraction Followed by RP-HPLC and UV Analysis

A rapid, cost-eff ective and simple “RP-HPLC method with UV detection” was developed for the determination of zileuton from human plasma. The method involved spiking human plasma and validation. Phenacetin (internal standard) and zileuton samples were prepared using LLE in diethyl ether which was used as solvent for extraction. “HiQsil C18 column (250 mm*4.6 mm* 5 m) was used for separation with tetrahydrofuran: water (45:55, v/v) as mobile phase. The fl ow rate was set at 1-mL/min and UV detection was done at 230 nm. Zileuton showed excellent separation from the internal standard and no interference was observed in plasma samples. A linear calibration curve was obtained in the 500 to 10,000 ng/mL range. The relative error (RE) and relative standard deviation (RSD) were found to be less than 15% for both within the run and between the runs. At lower zileuton concentrations, “weighted least square regression with a weighting factor of 1/X was used to reduce the heteroscedastic eff ect”. Extraction effi ciency of LLE method was confi rmed by the recovery of zileuton from samples. The stability data showed that “zileuton was stable in human plasma for 6 hours at room temperature for 30 days at -20°C after freeze thaw cycles”.