Median Absolute Error Research Articles

Dose-Response Study of Norepinephrine Infusion for Maternal Hypotension in Preeclamptic Patients Undergoing Cesarean Delivery Under Spinal Anesthesia.

Spinal anesthesia remains the preferred mode of anesthesia for preeclamptic patients during cesarean delivery. We investigated the incidence of maternal hypotension under spinal anesthesia during cesarean delivery, by comparing different prophylactic infusion rates of norepinephrine with normal saline. We randomly allocated 180 preeclamptic patients (45 in each groups) aged 18-45 scheduled for cesarean delivery to receive one of four prophylactic norepinephrine infusions at doses of 0 (normal saline group), 0.025 (0.025 group), 0.05 (0.05 group), or 0.075 (0.075 group) µg/kg/min following spinal anesthesia. The primary endpoint was the incidence of maternal hypotension (systolic blood pressure < 80% of baseline). The incidence of maternal hypotension was reduced with different prophylactic infusion rates of norepinephrine (26.7%, 15.6%, and 6.7%) compared with normal saline (37.8%) with a significant decreasing trend (p = 0.002). As the infusion doses of norepinephrine increased, there is a significant decreasing trend in deviation of systolic blood pressure control (median performance error; median absolute performance error) from baseline (p < 0.001; p < 0.001) and need for rescue norepinephrine boluses (p = 0.020). The effective dose 50 and effective dose 90 of prophylactic norepinephrine infusion were - 0.018 (95% confidence interval - 0.074, 0.002) µg/kg/min and 0.065 (95% confidence interval 0.048, 0.108) µg/kg/min, respectively. Prophylactic infusion of norepinephrine, as compared to no preventive measures, can effectively reduce the incidence of maternal hypotension in preeclamptic patients under spinal anesthesia during cesarean delivery, without increasing other adverse events for either the mother or neonate. Clinical trials.gov identifier number NCT04556370.

Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency.

Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant R t . These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ~0.6% median absolute error and ~6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.

Comparison of Hill-RBF 3.0 with Barrett Universal II, SRK/T, Hoffer Q, Haigis, and Holladay 1 to predict the accuracy of post-cataract surgery refractive outcomes in Indian eyes.

To compare Hill-RBF 3.0 with Barrett Universal II (BU II), SRK/T, Hoffer Q, Haigis, and Holladay 1 in predicting the accuracy of post-cataract surgery refractive outcomes in Indian eyes. In this prospective, comparative, observational study, consecutive patients with uncomplicated age-related cataracts undergoing uneventful phacoemulsification with posterior chamber intraocular lens (IOL) implantation were included. The mean absolute errors (MAEs) and median absolute errors were used to determine the accuracy of predicted postoperative target refractions. A total of 219 eyes of 173 patients were enrolled. Based on the axial lengths (AL), the patients were classified into: AL <22 mm (short), 22-24.5 mm (normal), and >24.5 mm (long). BU II exhibited the lowest MAE for normal ALs (0.2683 ± 0.2790 D) as well as for the entire population (0.2764 ± 0.2764 D). For the short ALs, Hill RBF 3.0 exhibited the lowest MAE (0.3268 ± 0.3268 D), while for the long ALs, SRK/T showed the lowest MAE (0.2823 ± 0.2642 D). BU II exhibited the highest percentage of eyes of 57.5%, 95.4%, and 98.6% within ±0.25, ±0.75, and ±1.0 D of postoperative target refractions respectively, whereas Hill RBF 3.0 had the highest percentages of eyes (88.1%) within ±0.5 D of postoperative target refraction. Hill-RBF 3.0 exhibited the least MAE for patients with short ALs, while BU II showed the least MAE for normal ALs as well as for the entire population and SRK/T for long ALs. This study is likely to aid surgeons in selecting the most appropriate IOL power formula, which thereby improves the refractive outcomes with utmost accuracy.

Comparison of SRK/T with Kane formula for prediction of refractive outcome after phacoemulsification: Insights from a South Asian patient group

BackgroundPhacoemulsification stands as the most commonly conducted ocular procedure. Biometry represents an art with numerous generations of formulas introduced to enhance refractive prediction accuracy. The Sanders Retzlaff Kraff theoretical (SRK/T), a third-generation formula that merges linear regression and a theoretical eye model, and the Kane formula, a fourth-generation formula incorporating theoretical optics, regression, and artificial intelligence, embody our focal points. Our aim is to compare post-operative refractive outcomes utilizing SRK/T and Kane formulae for calculating intraocular lens (IOL) power in senile cataract patients undergoing phacoemulsification featuring a hydrophilic acrylic, monofocal IOL. MethodsThis prospective, interventional institutional study engaged 70 patients, totaling 74 eyes. They were categorized into two groups: Group A employed the SRK/T for IOL power calculation, while Group B used the Kane formula. Quantel Medical® A-scan biometer was employed. A comparison was drawn between both formulae in terms of mean absolute error (MAE) and median absolute error (MedAE), with the utilization of IBM SPSS 20 and a significance level of p < 0.05. ResultsThe MAE for the SRK/T and Kane formulae stood at 0.18 ± 0.82 D and 0.24 ± 0.94 D, respectively, while the MedAE recorded values of 0.16 D and 0.155 D, correspondingly (p < 0.05). ConclusionWithin this South Asian cohort of senile cataract patients, the SRK/T formula demonstrated a marginally improved refractive outcome, evidenced by a reduced mean absolute error.

Accuracy of 14 intraocular lens power calculation formulas in extremely long eyes.

To compare the accuracy of 14 formulas in calculating intraocular lens (IOL) power in extremely long eyes with axial length (AL) over 30.0mm. In this retrospective study, 211 eyes (211 patients) with ALs > 30.0mm were successfully treated with cataract surgery without complications. Ocular biometric parameters were obtained from IOLMaster 700. Fourteen formulas were evaluated using the optimized A constants: Barrett Universal II (BUII), Kane, Emmetropia Verifying Optical (EVO) 2.0, PEARL-DGS, T2, SRK/T, Holladay 1, Holladay 2, Haigis and Wang-Koch AL adjusted formulas (SRK/Tmodified-W/K, Holladay 1modified-W/K, Holladay 1NP-modified-W/K, Holladay 2modified-W/K, Holladay 2NP-modified-W/K). The mean prediction error (PE) and standard deviation (SD), mean absolute errors (MAE), median absolute errors (MedAE), and the percentage of prediction errors (PEs) within ± 0.25 D, ± 0.50 D, ± 1.00 D were analyzed. The Kane formula had the smallest MAE (0.43 D) and MedAE (0.34 D). The highest percentage of PE within ± 0.25 D was for EVO 2.0 (37.91%) and the Holladay 1NP-modified-W/K formulas (37.91%). The Kane formula had the highest percentage of PEs in the range of ± 0.50, ± 0.75, ± 1.00, and ± 2.00 D. There was no significant difference in PEs within ± 0.25, ± 0.50 ± 0.75 and ± 1.00 D between BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas (P > .05) by using Cochran's Q test. The Holladay 2modified-W/K formula has the lowest percentage of hyperopic outcomes (29.38%). The BUII, Kane, EVO 2.0 and Wang-Koch AL adjusted formulas have comparable accuracy for IOL power calculation in eyes with ALs > 30.0mm.

Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics.

A novel imageless accelerometer-based navigation system improves acetabular cup placement accuracy during total hip arthroplasty in the lateral decubitus position.

The accuracy of acetabular cup placement using conventional portable imageless navigation systems in total hip arthroplasty (THA) in the lateral decubitus position remains challenging. Several novel portable imageless navigation systems have been developed recently to improve cup placement accuracy in THA. This study compared the accuracy of acetabular cup placement using a conventional accelerometer-based portable navigation (c-APN) system and a novel accelerometer-based portable navigation (n-APN) system during THA in the lateral decubitus position. This retrospective cohort study compared 45 THAs using the c-APN and 45 THAs using the n-APN system. The primary outcomes were the absolute errors between the intraoperative and postoperative values of acetabular cup radiographic inclination and anteversion angles and the percentage of cases with absolute errors within 5°. Intraoperative values were shown on navigation systems, and postoperative measurements were conducted using computed tomography images. The median absolute errors of the cup inclination angles were significantly smaller in the n-APN group than in the c-APN group (3.9° [interquartile range 2.2°-6.0°] versus 2.2° [interquartile range 1.0°-3.3°]; P = 0.002). Additionally, the median absolute errors of the cup anteversion angles were significantly smaller in the n-APN group than in the c-APN group (4.4° [interquartile range 2.4°-6.5°] versus 1.9° [interquartile range 0.8°-2.7°]; P < 0.001). Significant differences were observed in the percentage of cases with absolute errors within 5° of inclination (c-APN group 67% versus n-APN group 84%; P = 0.049) and anteversion angles (c-APN group 62% versus n-APN group 91%; P = 0.001). The n-APN system improved the accuracy of the cup placement compared to the c-APN system for THA in the lateral decubitus position.

Accuracy of 20 Intraocular Lens Power Calculation Formulas in Medium-Long Eyes.

The aim of this work is to compare 20 intraocular lens (IOL) power calculation formulas in medium-long eyes (24.50-25.99mm) in terms of root mean square absolute error (RMSAE), median absolute error (MedAE), and percentage of eyes with prediction error (PE) within ± 0.50 D. The data of patients who underwent uneventful phacoemulsification between January 2017 and September 2023 were reviewed. Pre-surgery IOL power was calculated using Holladay1, SRK/T, Hoffer Q, Holladay 2, and Haigis. Three months after phacoemulsification, refraction was measured. Post-surgery IOL power calculations were performed utilizing the following formulas: Barrett Universal II, Kane, K6, Olsen (OLCR), Olsen (standalone), PEARL-DGS, Ladas Super Formula AI (LSF AI), T2, EVO, VRF, Hoffer QST, Castrop, VRF-G, Karmona, and Naeser 2. RMSAE, MedAE, and percentage of eyes with PE within ± 0.25 D, ± 0.50 D, ± 0.75 D and ± 1.00 were calculated. One hundred twenty-four eyes with axial length ranges between 24.52 and 25.97mm were studied. The SRK/T formula yielded the lowest RMSAE (0.206) just before Holladay 1 (0.260) and T2 (0.261). In terms of MedAE, the best outcome was obtained by SRK/T (0.12) followed by Barrett Universal II (0.15) and LSF AI (0.15). The highest percentage of eyes with prediction error within ± 0.50 D was achieved by SRK/T, T2, and Holladay 1 (97.58, 93.55, and 93.55%, respectively). Third-generation formulas (SRK/T, Holladay 1) provided highly accurate outcomes in medium-long eyes and still can be wildly used to calculate IOL power.

Особенности расчета оптической силы недифракционной интраокулярной линзы с расширенной глубиной фокуса

Purpose. Retrospective analysis of the accuracy of 7 formulas for calculating the optical power of a non-diffractive intraocular lens (IOL) with extended depth of focus. Material and methods. The retrospective study included 48 patients (81 eyes) with AcrySof IQ Vivity EDOF IOL implantation. The IOL optical power range was +15.0 to +30.0 D, and the target refraction was –0.25 to 0.25 D. The mean age of the patients was 59.1±7.7 (40–84) years.The SRK/T, Barrett Universal II, Haigis, Hoffer Q, Holladay 2, Olsen, and Kane formulas were retrospectively analyzed for effectiveness using appropriate constants. Each formula was optimized for the study group of patients to achieve an average refractive error as close to zero as possible. For each formula, the mean error (ME), mean absolute error (MAE), standard deviation (SD), median absolute error (MedAE), maximum absolute error (MaxAE), and percentage of eyes within the error range of 0.25, 0.5, 1.0, and 2.0 D were calculated. Results. The optimized constants were LF = 2.18, a0 = 1.39, a1 = 0.41, a2 = 0.13, personalized ACD = 5.74, ACD (Hoffer Q/Holladay 2) = 5.64/5.50, A-constant (SRK/T/Kane) = 119.2/119.3. The lowest MAE is determined for Kane, Barrett Universal II, and SRK/T formulas (p&lt;0.05). The use of Haigis and Olsen formulas was characterized by a lower MAE compared to Holladay 2 and Hoffer Q formulas (p&lt;0.05). With the exception of the Haigis and Hoffer Q formulas, all other formulas are shown to fall within the target refraction of ±2.00 D. With the Kane, Barrett Universal II and SRK/T formulas, 100% target refraction of ±1.00 D, 98‒99% target refraction of ±0.50 D, and greater than 80% target refraction of ±0.20 D were determined. Conclusion. In this study, we retrospectively evaluated the performance of seven formulas for calculating the optical power of the Acrysof IQ Vivity EDOF non-diffractive intraocular lens. The best results were shown for the Kane, Barrett Universal II and SRK/T formulas. Key words:cataract, phacoemulsification, EDOF, Symfony, IOL optical power calculation, constant optimization

Predictability of Existing IOL Formulas After Cataract Surgery in Patients with a Previous History of Radial Keratotomy: A Retrospective Cohort Study and Literature Review.

This study aims to evaluate the accuracy of 12 different intraocular lens (IOL) power calculation formulas for post-radial keratotomy (RK) eyes. The investigation utilizes recent advances in topography/tomography devices and artificial intelligence (AI)-based calculators, comparing the results to those reported in current literature to assess the efficacy and predictability of IOL calculations for this patient group. In this retrospective study, 37 eyes from 24 individuals with a history of RK who underwent cataract surgery at Hoopes Vision Center were analyzed. Biometry and corneal topography measurements were taken preoperatively. Subjective refraction was obtained 6 months postoperatively. Twelve different IOL power calculations were used, including the American Society of Cataract and Refractive Surgery (ASCRS) post-RK online formula, and the Barrett True K, Double K modified-Holladay 1, Haigis-L, Panacea, Camellin-Calossi, Emmetropia Verifying Optical (EVO) 2.0, Kane, and Prediction Enhanced by Artificial Intelligence and output Linearization-Debellemanière, Gatinel, and Saad (PEARL-DGS) formulas. Outcome measures included median absolute error (MedAE), mean absolute error (MAE), arithmetic mean error (AME), and percentage of eyes achieving refractive prediction errors (RPE) within ± 0.50 D, ± 0.75 D, and ± 1 D for each formula. A search of the literature was also performed by two independent reviewers based on relevant formulas. Overall, the best performing IOL power calculations were the Camellin-Calossi (MedAE = 0.515 D), the ASCRS average (MedAE = 0.535 D), and the EVO (MedAE = 0.545 D) and Kane (MedAE = 0.555 D) AI-based formulas. The EVO and Kane formulas along with the ASCRS calculation performed similarly, with 48.65% of eyes scoring within ± 0.50 D of the target range, while the Equivalent Keratometry Reading (EKR) 65 Holladay formula achieved the greatest percentage of eyes scoring within ± 0.25 D of the target range (35.14%). Additionally, the EVO 2.0 formula achieved 64.86% of eyes scoring within the ± 0.75 D RPE category, while the Kane formula achieved 75.68% of eyes scoring within the ± 1 D RPE category. There was no significant difference in MAE between the established and newer generation formulas (P > 0.05). The Panacea formula consistently underperformed when compared to the ASCRS average and other high-performing formulas (P < 0.05). This study demonstrates the potential of AI-based IOL calculation formulas, such as EVO 2.0 and Kane, for improving the accuracy of IOL power calculation in post-RK eyes undergoing cataract surgery. Established calculations, such as the ASCRS and Barrett True K formula, remain effective options, while under-utilized formulas, like the EKR65 and Camellin-Calossi formulas, show promise, emphasizing the need for further research and larger studies to validate and enhance IOL power calculation for this patient group.

Application of Artificial Neural Network Methods to Anatolian Plate Earthquake Magnitude and Location Prediction

Same-region earthquakes usually have a pattern that is difficult to identify clearly. Therefore, time series analysis methods have been proposed for earthquake prediction. Our work attempts to predict three earthquake parameters in the Anatolian Peninsula using pure artificial neural network methods. An optimized BP-NN model and optimally hyper-parameterized LSTM Model have been designed to predict earthquake magnitude, latitude, and longitude. The two models are compared among each other and with previous works for their prediction performances, using four well-accepted metrics: Mean Squared Error, Mean Absolute Error, Median Absolute Error, and Standard Deviation. The time, depth, sun, and moon distances to Earth were identified as the most contributing factors in earthquake occurrence through analysis by five different feature extraction algorithms. The date harmed the prediction accuracy. The LSTM model outperformed the BP-NN Model in magnitude prediction with 0.062 MSE. Latitude predictions of both methods were satisfactory and close. However, BP-NN had lower error rates in latitude prediction. However, longitude prediction errors were large in both models. Therefore, our designs were not successful in predicting the exact location of the earthquakes. However, multi-variate, stacked LSTM models are promising in predicting Anatolian Peninsula earthquake magnitudes, but future work is necessary for location and timing predictions.

Machine Learning-Based Prediction of Changes in the Clinical Condition of Patients With Complex Chronic Diseases: 2-Phase Pilot Prospective Single-Center Observational Study.

Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (20<BI≤60; severe dependence), and class C (BI>60; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters.

Multiomics Picture of Obesity in Young Adults.

Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC-MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC-MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions.

Carbon Monitor Power-Simulators (CMP-SIM v1.0) across countries: a data-driven approach to simulate daily power generation

Abstract. The impact of climate change on power demand and power generation has become increasingly significant. Changes in temperature, relative humidity, and other climate variables affect cooling and heating demand for households and industries and, therefore, power generation. Accurately predicting power generation is crucial for energy system planning and management. It is also crucial to understand the evolution of power generation to estimate the amount of CO2 emissions released into the atmosphere, allowing stakeholders to make informed plans to reduce emissions and to adapt to the impacts of climate change. Artificial intelligence techniques have been used to investigate energy-demand-side responses to external factors at various scales in recent years. However, few have explored the impact of climate and weather variability on power demand. This study proposes a data-driven approach to model daily power demand provided by the Carbon Monitor Power project by combining climate variables and human activity indices as predictive features. Our investigation spans the years 2020 to 2022 and focuses on eight countries or groups of countries selected to represent different climates and economies, accounting for over 70 % of global power consumption. These countries include Australia, Brazil, China, the European Union (EU), India, Russia, South Africa, and the United States. We assessed various machine-learning regressors to simulate daily power demand at the national scale. For countries within the EU, we extended the analysis to one group of countries. We evaluated the models based on key evaluating metrics: coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and median absolute error (MedAE). We also used the models to identify the most influential variables that impact power demand and determine their relationship with it. Our findings provide insight into variations in important predictive features among countries, along with the role played by distinct climate variables and indicators of the level of economic activity, such as weekends and working days, vacations and holidays, and the influence of COVID-19.

The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde®.

The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device.

Predictive Modeling of Groundwater Recharge under Climate Change Scenarios in the Northern Area of Saudi Arabia

Water scarcity is considered a major problem in dry regions, such as the northern areas of Saudi Arabia and especially the city of Hail. Water resources in this region come mainly from groundwater aquifers, which are currently suffering from high demand and severe climatic conditions. Forecasting water consumption as accurately as possible may contribute to a high level of sustainability of water resources. This study investigated different Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Linear Regression (LR), and Gradient Boosting (GB), to efficiently predict water consumption in such areas. These models were evaluated using a set of performance measures, including Mean Squared Error (MSE), R-squared (R2), Mean Absolute Error (MAE), Explained Variance Score (EVS), Mean Absolute Percentage Error (MAPE), and Median Absolute Error (MedAE). Two datasets, water consumption and weather data, were collected from different sources to examine the performance of the ML algorithms. The novelty of this study lies in the integration of both weather and water consumption data. After examining the most effective features, the two datasets were merged and the proposed algorithms were applied. The RF algorithm outperformed the other models, indicating its robustness in capturing water usage behavior in dry areas such as Hail City. The results of this study can be used by local authorities in decision-making, water consumption analysis, new project construction, and consumer behavior regarding water usage habits in the region.

Scan-Free and Fully Automatic Tracking of Native Knee Anatomy from Dynamic Stereo-Radiography with Statistical Shape and Intensity Models.

Kinematic tracking of native anatomy from stereo-radiography provides a quantitative basis for evaluating human movement. Conventional tracking procedures require significant manual effort and call for acquisition and annotation of subject-specific volumetric medical images. The current work introduces a framework for fully automatic tracking of native knee anatomy from dynamic stereo-radiography which forgoes reliance on volumetric scans. The method consists of three computational steps. First, captured radiographs are annotated with segmentation maps and anatomic landmarks using a convolutional neural network. Next, a non-convex polynomial optimization problem formulated from annotated landmarks is solved to acquire preliminary anatomy and pose estimates. Finally, a global optimization routine is performed for concurrent refinement of anatomy and pose. An objective function is maximized which quantifies similarities between masked radiographs and digitally reconstructed radiographs produced from statistical shape and intensity models. The proposed framework was evaluated against manually tracked trials comprising dynamic activities, and additional frames capturing a static knee phantom. Experiments revealed anatomic surface errors routinely below 1.0 mm in both evaluation cohorts. Median absolute errors of individual bone pose estimates were below 1.0 or mm for 15 out of 18 degrees of freedom in both evaluation cohorts. Results indicate that accurate pose estimation of native anatomy from stereo-radiography may be performed with significantly reduced manual effort, and without reliance on volumetric scans.

Epigenetic clocks, sex markers and age-class diagnostics in three harvested large mammals.

The development of epigenetic clocks, or the DNA methylation-based inference of age, is an emerging tool for ageing in free ranging populations. In this study, we developed epigenetic clocks for three species of large mammals that are the focus of extensive management throughout their range in North America: white-tailed deer, black bear and mountain goat. We quantified differential DNA methylation patterns at over 30,000 cytosine-guanine sites (CpGs) from tissue samples of all three species (black bear n = 49; white-tailed deer n = 47; mountain goat n = 45). We used a penalized regression model (elastic net) to build explanatory (black bear r = .95; white-tailed deer r = .99; mountain goat r = .97) and robust (black bear Median Absolute Error or MAE = 1.33; white-tailed deer MAE = 0.29; mountain goat MAE = 0.61) models of age or clocks. We also characterized individual CpG sites within each species that demonstrated clear differences in methylation levels between age classes and sex, which can be used to develop a suite of accessible diagnostic markers. This tool has the potential to contribute to wildlife monitoring by providing easily obtainable representations of age structure in managed populations.

Retrospective assessment of accuracy of nine intraocular lens power calculation formulae in eyes with axial myopia.

To compare the accuracy of nine conventional and newer-generation formulae in calculating intraocular lens power in eyes with axial myopia. Tertiary eye care center, Bengaluru, India. Retrospective cross-sectional, comparative study conducted in India. Patients undergoing uneventful phacoemulsification in eyes with axial length >26 mm were included. Preoperative biometry was done using Lenstar LS 900 (Haag-Streit AG, Switzerland). Single eye of patients undergoing bilateral implantation was randomly selected. Optimized lens constants were used to calculate the predicted postoperative refraction of each formula, which was then compared with the actual refractive outcomes to give the prediction errors, following which subgroup analysis was performed. The Kane formula, Barrett universal II, Emmetropia Verifying Optical (EVO) 2.0, Hill Radial Basis Function (Hill RBF) 3.0, Olsen formula, along with Wang Koch-adjusted four formulae, that is, Sanders Retzlaff Kraff/Theoretical (SRK/T), Holladay 1, Haigis, and Hoffer Q formula, were compared for intraocular lens power calculations. One hundred and sixty-five eyes that fulfilled all the inclusion criteria were studied. Hill RBF 3.0 had the lowest mean and median absolute prediction errors (0.355 and 0.275, respectively) compared to all formulas. In subgroup analysis (26-28, >28-30, and >30 mm), significant difference was seen only in extremely long eyes (>30 mm). The Hill RBF 3.0 formula generated the maximum percentage of eyes with refractive errors within ±0.25, ±0.5, ±0.75, and ±1 D (46%, 76.2%, 89.9%, and 95.8%, respectively). This is the first study evaluating all the formulas exclusively in the myopic eyes. Hill RBF 3 was found to be superior in accuracy to all other formulas.

Improving Accuracy of In-The-Bag Intraocular Lens Power Calculation in Adult Eyes with Unilateral Subluxated Lenses by Using the Anterior Chamber Depth of the Unaffected Eye

Introduction: This study aimed to determine the interchangeability of bilateral anterior chamber depth (ACD) in intraocular lens (IOL) power calculations for cataractous eyes and refractive outcomes using the unaffected fellow eye’s ACD in subluxated crystalline lenses. Methods: The predicted postoperative spherical equivalent (SE) calculated using the Kane formula with and without fellow eye’s ACD in 202 cataract patients was compared. Refractive outcomes of the newer formulas (the Kane, Barrett Universal II [BUII], and Pearl-DGS formulas) with affected eye’s ACD and with unaffected fellow eye’s ACD were compared in 33 eyes with lens subluxation (the affected eye) undergoing in-the-bag IOL implantation. The SD of the prediction error (PE) was assessed using the heteroscedastic method. Results: In 202 paired cataractous eyes, no marked ACD difference was found bilaterally; the predicted SE obtained without the fellow eye’s ACD was comparable with that calculated with the fellow eye one (p = 0.90), with a mean absolute difference of 0.03 ± 0.03 D. With the affected eye AL, keratometry, and ACD, the median absolute error (MedAE) was 0.38–0.64 D, and the percentage of PE within ±0.50 D was 30.30–57.58%. The unaffected eye’s ACD improved the results (MedAE, 0.35–0.49 D; the percentage of PE within ±0.50 D, 54.55–63.64%). The SDs of the BUII (0.82 D) and Pearl-DGS formulas (0.87 D) with the affected eye’s ACD were significantly larger than those of the Kane and Pearl-DGS formulas (both 0.69 D) with the unaffected eye’s ACD. Conclusion: Bilateral ACD was interchangeable in IOL power calculation for cataractous eyes when using the Kane formula. Unaffected eye’s ACD in lieu of affected eye’s ACD can enhance the accuracy of newer formulas in patients with unilateral subluxated lenses undergoing in-the-bag IOL implantation.