High Accuracy Value Research Articles

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.

Convolutional Neural Network Activation Function Performance on Image Recognition of The Batak Script

Deep Learning is a sub-set of Machine learning, Deep Learning is widely used to solve problems in various fields. One of the popular deep learning architectures is The Convolutional Neural Network (CNN), CNN has a layer that transforms feature extraction automatically so it is widely used in image recognition. However, CNN's performance using the tanh function is still relatively low, therefore it is necessary to select the right activation function to improve accuracy performance. This study analyzes the use of the activation function in image recognition of the Batak script. The result of this study is that the CNN model using the ReLU and eLU functions produces the highest accuracy compared to the CNN model using the tanh function. The CNN model using eLU produces the best accuracy performance in the training process, which is 99.71% with an error value of 0.0108. Meanwhile, in the testing process, the highest accuracy value is generated by the CNN Model using the ReLU function with an accuracy of 94.11%, an error value of 0.3282, a precision value of 0.9411, a recall of 0.9411, and an f1-score of 0.9416.

Improving the quality of pulmonary nodules segmentation using the new proposed U-Net neural network

Diagnosing lung cancer is difficult due to the complexity of the nature of nodules. CT scan imaging is the most common imaging to diagnosis of lung cancer. Detection of nodules from these images is a challenge for radiologists and doctors. In recent years, neural networks have been developed for automatic, faster and more accurate diagnosis of diseases from medical images. In the present study, a new improved U-Net neural network is introduced for the automatic detection and segmentation of pulmonary nodules. The evaluation of this model has been done on LIDC-IDRI database. Our results have high values of recall, specificity and accuracy. The highest Recall value is 97.97 and is related to Juxtra-vascular. Specificity and accuracy for non-solid, partially solid and tiny has a value of 96.99.

UPLC METHOD DETERMINATION AND QUANTIFICATION OF BELUMOSUDIL AND ITS IMPURITIES IN THE HUMAN PLASMA SAMPLES

This current analysis demonstrates the development of the related substances (RS) method for separating and quantifying BSL and its related substances from plasma using the UPLC technique. The solid phase extraction (SPE) method was employed to extract the belumosudil (BSL) and related impurities from plasma. This SPE method has been shown to have high recovery values for BSL and its related impurities. The method employed a column from Waters, specifically the X-Bridge BEH C18 150x4.6mm, 3.5µm, with a mobile phase flow rate (0.8 mL/min) and a UV-wavelength (250 nm). In this method, BSL and its impurities show good peak shapes and high resolutions. This developed method shows high sensitivity, accuracy, and precision values. While performing the stability studies, the drug was stable and it showed less than 4.53% degradation. Six different concentration solutions (BSL (25-250µg/mL) and impurities (3-18µg/mL)) were prepared for constructing linearity plots. The obtained linearity plot shows the accepted correlation coefficient (R2) value (≥0.999). The Limit of detection (LOD) and Limit of quantification (LOQ) were determined using the signal-to-noise (S/N) ratio method. This UPLC method was validated according to ICH guidelines.

Risk Factors and Predictive Nomogram for Carbapenem-Resistant Klebsiella pneumoniae in Children in a Grade 3 First-Class General Hospital in Central China.

This study determined risk factors for Carbapenem-resistant Klebsiella pneumoniae (CRKP)in children admitted to a grade 3 first-class general hospital and developed an individualized line graph predictive model. The clinical data of 185 children infected with Klebsiella pneumoniae from January 2015 to December 2019 were analyzed retrospectively. Patients were grouped according to carbapenem resistance: CRKP group (50 cases) and CSKP (carbapenem-sensitive Klebsiella pneumoniae) group (135 cases). Risk factors for CRKP in children were screened by logistic regression analysis. The predictive model was established using R software and validated using the Bootstrap method. Age (odds ratio [OR]=0.104, 95% confidence interval [CI]: 0.026-0.408), intensive care unit admission (OR =2.829, 95% CI: 1.138-7.030), mechanical ventilation (OR =7.510, 95% CI: 3.140-17.961), surgery history (OR =5.005, 95% CI: 1.507-16.618) and glucocorticoid (OR =0.235, 95% CI: 0.099-0.557) were independent risk factors for CRKP in children (P < 0.05), The total risk score of each factor was 362.5, and the risk rate was 0.1-0.9. In receiver-operating characteristic curve analysis, the area under the curve of CRKP predicted by the total risk score was 0.872 (95% CI=0.844-0.901; P < 0.001). The correction curve indicated that the consistency between the observed value and the predicted value was good. This study successfully established a model based on the risk factors, with high accuracy and good predictive value for CRKP in children. Hospitals should take necessary preventive measures against the risk factors for drug-resistant bacteria, such as optimizing the configuration of ICU space, timely isolation of infected children, and adequate disinfection of ICU equipment. Which may reduce CRKP infection rate.

Predictive value of blood coagulation and routine blood indices for rebleeding after endoscopic treatment in hepatitis B-related cirrhotic patients with esophagogastric fundal varices: a logistic regression model analysis.

To evaluate the predictive value of blood coagulation and routine blood indices for rebleeding after endoscopic treatment of ruptured esophagogastric fundal varices (EGVB) in cirrhotic patients with hepatitis B infection. This retrospective analysis included 248 patients with hepatitis B-related cirrhosis and EGVB who received initial endoscopic treatment from October 2019 to March 2022 and were followed up for 12 months. Patients were divided into rebleeding and non-rebleeding groups. Laboratory indices were analyzed, and univariate and multivariate analyses identified predictors of rebleeding. The efficacy of a logistic regression model was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), and a risk factor nomogram was constructed for assessing the predictive efficiency of those risk factors. Univariate analysis showed significant differences in portal vein diameters and lower Child-Pugh scores in the rebleeding group in contrast to those in the non-rebleeding group. Key laboratory markers such as platelet count (PLT), albumin (ALB), alanine aminotransferase (ALT), lymphocytes (LYM), and prognostic nutritional index (PNI) were lower, while prothrombin time (PT) and lactate levels (LN) were higher in the rebleeding group than those in the non-rebleeding group. Multivariate analysis identified portal vein diameter, PLT, ALT, PT, LYM, and PNI as significant predictors of rebleeding. The logistic model demonstrated high accuracy (AUC=0.986) and clinical value, validated by ROC curves, calibration curves (C-index =0.986), and DCA results. A risk factor predictive nomogram was successfully constructed. This study developed a logistic regression model with a nomogram for predicting EGVB-related rebleeding in patients with hepatitis B-related cirrhosis, achieving an AUC of 0.986, indicating high accuracy and significant clinical relevance.

Nomogram Model for Cardiac Surgery-Associated Acute Kidney Injury Based on Clinical Characteristics Combined with Plasma suPAR.

Analyze risk factors for cardiac surgery-associated acute kidney injury (CSA-AKI) in adults and establish a nomogram model for CSA-AKI based on plasma soluble urokinase-type plasminogen activator receptor (suPAR) and clinical characteristics. In a study of 170 patients undergoing cardiac surgery with cardiopulmonary bypass, enzyme-linked immunosorbent assay (ELISA) measured plasma suPAR levels. Multivariable logistic regression analysis identified risk factors associated with CSA-AKI. Subsequently, the CSA-AKI nomogram model was developed using R software. Predictive performance was evaluated using a receiver operating characteristic (ROC) curve and the area under the curve (AUC). Internal validation was performed through the Bootstrap method with 1000 repeated samples. Additionally, decision curve analysis (DCA) assessed the clinical applicability of the model. Multivariable logistic regression analysis revealed that being male, age ≥ 50 years, operation time ≥ 290 minutes, postoperative plasma suPAR at 2 hours, and preoperative left ventricular ejection fraction (LVEF) were independent risk factors for CSA-AKI. Employing these variables as predictive factors, a nomogram model was constructed, an ROC curve was generated, and the AUC was computed as 0.817 (95% CI 0.726-0.907). The calibration curve indicated the accuracy of the model, and the results of DCA demonstrated that the model could benefit the majority of patients. Being male, age ≥ 50 years, operation time ≥ 290 minutes, low preoperative LVEF, and elevated plasma suPAR at 2 hours are independent risk factors for CSA-AKI. The nomogram model established based on these risk factors has high accuracy and clinical value, serving as a predictive tool for assessing the risk of CSA-AKI.

Research on the Innovation and Effectiveness of the Teaching Mode of Civics and Political Science Courses Supported by Mobile Computing Platforms

Abstract In today’s world, with the rapid development of information technology and scientific education, data technology has triggered important changes in ideological and political education in colleges and universities, effectively promoting the transformation of educational concepts, ways of thinking, educational methods, and practice modes. Based on the research background under big data, this study investigates the traditional knowledge-tracking model, introduces the attention mechanism and the characteristics of ideological and political learners on the basis of the traditional model, and proposes an improved knowledge-tracking model. At the same time, a mobile teaching platform was constructed, then the improved knowledge tracking model was tested, and the effect of the knowledge platform application on the relevant dataset was explored. The results show that using the optimized knowledge state tracking model, the teacher’s teaching time can be adjusted according to the students’ state, and there is a good effect on the prediction of students’ grades, and the average grade of the students is only 2-6 points of error from the predicted grades. In terms of model application accuracy, the average value of low accuracy is 6.92%, the average value of medium accuracy is 12.9%, and the average value of high accuracy is 79.79%, so the model assesses students’ knowledge status with high accuracy. Finally, the optimized model was used to analyze the learning effect of the students and to conduct a survey on the effect of using satisfaction, and the survey concluded that the students were more satisfied with the model.

Accuracy of Kampala Trauma Score as a Predictor in Assessing the Prognostic Value of Multiple Trauma

Determining a trauma scoring system appropriate for certain conditions will help in predicting mortality and morbidity, and can determine the need for treatment in patients. Kampala Trauma Score (KTS) is a trauma scoring system that uses a decent number of assessment variables and can be used in all general groups. This study was a retrospective prognostic test conducted in Dr. Hasan Sadikin Hospital Bandung, Indonesia, from December 2021 to July 2022. Data obtained was processed using Microsoft Excel and SPSS spreadsheets. The respondents were 66 people with multiple trauma. Results showed that this study was dominated by patients with blunt trauma (n=63, 95.45%). The mean KTS score in this study was 13.08±1.03. In 49 (74.24%) patients who survived, the mean KTS score was 13.41±0.89 and in 17 (25.76%) patients who died, the mean KTS score was 12.12±0.79. The greatest mortality accuracy was at the cutoff point of the KTS score &gt;12 (81.82% accuracy; 70.59% sensitivity; 85.71% specificity). The accuracy value of the KTS score is in line with the ROC where the optimal KTS score is &gt;12 (85.77%). In conclusion, the Kampala trauma score can be used as a predictor in assessing the prognostic value of multiple trauma patients with the obtained cut-point value as it has high accuracy, sensitivity, and specificity values.

Arabic Character Recognition Using CNN LeNet-5

The human handwriting pattern is one of the research areas of pattern recognition; it is very complex. Therefore, research in this field has become quite popular. Moreover, human handwriting pattern recognition is needed for several things, one of them being character recognition. Recognition of Arabic handwriting is complex because everyone has different characteristics in writing and Arabic characters have quite abstract shapes and patterns. From previous research, Convolutional Neural Network (CNN), a deep learning-based algorithm, has a fairly high accuracy value when used for public datasets such as AHDB and private datasets. In this study, private datasets are used with a fairly high level of complexity because the respondents appointed to write Arabic letters come from different age categories. The CNN architecture used in this research is the architecture developed by Yan LeCun known as LeNet-5. The local dataset used was 8400 images, with details of 6720 for training data (each letter has 240 images) and 1680 for testing data (each letter has 60 images). The total respondents who wrote Arabic script were 30 people, and each person wrote each letter ten times. The accuracy obtained is 81% higher than in previous studies. The following study will test a number of additional CNN architectures to increase the accuracy of the results. In addition to accuracy, this study will also calculate the misclassification rate, root mean square error, and mean absolute error.

Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.

Mapping Pinus spp. Forestry and Land Cover Classes Using High-resolution PlanetScope Satellite Data: Experimenting Images from Different Seasons and Machine Learning Methods

The Remote Sensing and machine learning techniques are cost-effective ways of mapping land use and cover, especially forestry areas. This is essential for the management and planning of such resources. The purpose of this study was to identify which classifier (Random Forest or Support Vector Machine) reach the best accuracy in land use and cover classification and determine which is the best season of year for Pinus spp. forest mapping. PlanetScope multispectral image was used with 3.7 m of spatial resolution, collected over the coastal region of Rio Grande do Sul state. The input variables for the classifiers were the four spectral bands: RGB and NIR, and the NDVI vegetation index. In both classifiers, high accuracy values were obtained, as well as for all seasons of the year. The Random Forest classifier obtained better results in the spring and summer seasons, while in the autumn and winter seasons there was no significant difference between the classifiers for the classification of Pinus spp. forests. The results reached an adequate precision to be used for the management and monitoring of the land use and cover in the municipality of São José do Norte, RS.

Testing the Reliability of Maximum Entropy Method for Mapping Gully Erosion Susceptibility in a Stream Catchment of Calabria Region (South Italy)

Gully erosion poses severe problems for land degradation in several areas worldwide. This study aims to evaluate the accuracy and robustness of the maximum entropy (MaxEnt) method for assessing gully erosion susceptibility. We selected the catchment of the Mesima stream as the test site, which is situated in the southwest sector of the Calabria region (South Italy). An inventory map of gully erosion was realised and 12 predisposing factors, such as lithology, soil texture, soil bulk density, land use, drainage network, slope gradient, aspect, length–slope (LS), plan curvature, stream power index (SPI), topographic position index (TPI), and topographic wetness index (TWI), were selected to implement the dataset in the MaxEnt method. The accuracy and uncertainty of the method were tested by 10-fold cross-validation based on accuracy, kappa coefficient, and receiver operating characteristic curve (ROC) and related area under curve (AUC). The dataset was randomly divided into 10 equal-sized groups (folds). Nine folds (90% of the selected dataset) were used to train the model. Instead, the remaining fold (10% of the dataset) was used for testing the model. This process was repeated 10 times (equal to the number of the folds) and each fold was used only once as the validation data. The average of 10 repeated processes was performed to generate the susceptibility map. In addition, this procedure allowed the reliability of the susceptibility map to be assessed, in terms of variables, importance and role of predisposing factors selected, prediction ability, and accuracy in the assessed probabilities for each pixel of the map. In addition to exploiting the 10-fold cross-validation, the mean value and standard deviation for the probability estimates of each pixel were computed and reported in the susceptibility and uncertainty map. The results showed that the MaxEnt method has high values of accuracy (&gt;0.90), of the kappa coefficient (&gt;0.80), and AUC (&gt;0.92). Furthermore, the achieved findings showed that the capacity of the method used for mapping gully erosion susceptibility is quite robust when the training and testing sets are changed through the 10-fold cross-validation technique.

TDCAU-Net: retinal vessel segmentation using transformer dilated convolutional attention-based U-Net method

Retinal vessel segmentation plays a vital role in the medical field, facilitating the identification of numerous chronic conditions based on retinal vessel images. These conditions include diabetic retinopathy, hypertensive retinopathy, glaucoma, and others. Although the U-Net model has shown promising results in retinal vessel segmentation, it tends to struggle with fine branching and dense vessel segmentation. To further enhance the precision of retinal vessel segmentation, we propose a novel approach called transformer dilated convolution attention U-Net (TDCAU-Net), which builds upon the U-Net architecture with improved Transformer-based dilated convolution attention mechanisms. The proposed model retains the three-layer architecture of the U-Net network. The Transformer component enables the learning of contextual information for each pixel in the image, while the dilated convolution attention prevents information loss. The algorithm efficiently addresses several challenges to optimize blood vessel detection. The process starts with five-step preprocessing of the images, followed by chunking them into segments. Subsequently, the retinal images are fed into the modified U-Net network introduced in this paper for segmentation. The study employs eye fundus images from the DRIVE and CHASEDB1 databases for both training and testing purposes. Evaluation metrics are utilized to compare the algorithm’s results with state-of-the-art methods. The experimental analysis on both databases demonstrates that the algorithm achieves high values of sensitivity, specificity, accuracy, and AUC. Specifically, for the first database, the achieved values are 0.8187, 0.9756, 0.9556, and 0.9795, respectively. For the second database, the corresponding values are 0.8243, 0.9836, 0.9738, and 0.9878, respectively. These results demonstrate that the proposed approach outperforms state-of-the-art methods, achieving higher performance on both datasets. The TDCAU-Net model presented in this study exhibits substantial capabilities in accurately segmenting fine branching and dense vessels. The segmentation performance of the network surpasses that of the U-Net algorithm and several mainstream methods.

Automatic simultaneous ciliary muscle segmentation and biomarker extraction in AS-OCT images using deep learning-based approaches

Recent clinical studies have emphasized the importance of understanding the morphology and mechanics of the ciliary muscle. The ciliary muscle plays a vital role in various functions related to the anterior segment of the eye, including the regulation of intraocular pressure and the maintenance of the shape of the crystalline lens. To advance research in this area, we propose a fully automated methodology for the segmentation and biomarker measurement of the ciliary muscle in two different scan depths (6mm and 16mm), which are commonly used by clinicians to analyze biomarkers. Our methodology aims to provide repeatable, and immediate results through an exhaustive analysis of different network architectures, encoders, and transfer learning strategies. We also extracted a comprehensive set of relevant biomarkers, including parameters that provide essential information about its behavior during the accommodation process, overall dimensions, and biomechanical properties. These biomarkers can help clinicians and researchers in the diagnoses and monitor of different ocular diseases such as glaucoma, myopia, and presbyopia and develop new therapeutic strategies, potentially leading to more effective treatments and improved patient outcomes. Our methodology achieved accurate qualitative and quantitative results, with high accuracy values of 0.9665±0.1280 and 0.9772±0.0873 for the best combinations for 6mm and 16mm, respectively. Our proposed system provides a valuable and automatic tool for clinicians and researchers in the segmentation and analysis of the ciliary muscle in AS-OCT images.

Calculation method of external fault short-circuit current for variable-speed pumped storage units based on time-domain dynamic circuit

The calculation method of the external fault short-circuit current of large units with clear mechanisms and practicality is of great value in engineering practice. Due to the special AC excitation structure and control mode, the external fault short-circuit characteristics of variable-speed pumped storage units (VSPSUs) are very different from those of conventional units. Therefore, this paper proposes a calculation method for external fault short-circuit current based on a time-domain dynamic circuit. According to the excitation and control characteristics of VSPSUs under external fault, the VSPSU under external fault is divided into two cases: continuous excitation and jumper action. Based on space vector analysis, the time-domain dynamic circuit models are established, respectively. On this basis, the analytical formula of the external fault short-circuit current of VSPSUs is derived by combining the transition boundary conditions of the two cases and the dynamic characteristics of the stator flux linkage. Finally, the comparison with PSCAD/EMTDC simulation results shows that the proposed VSPSUs’ external fault short-circuit current calculation method has high accuracy and practical value, which can meet the needs of primary equipment selection and relay protection of variable-speed pumped storage power plants.

Quantile regression for genomic selection of growth curves

This study evaluated the efficiency of genome-wide selection (GWS) based on regularized quantile regression (RQR) to obtain genomic growth curves based on genomic estimated breeding values (GEBV) of individuals with different probability distributions. The data were simulated and composed of 2,025 individuals from two generations and 435 markers randomly distributed across five chromosomes. The simulated phenotypes presented symmetrical, skewed, positive, and negative distributions. Data were analyzed using RQR considering nine quantiles (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9) and traditional methods of genomic selection (specifically, RR-BLUP, BLASSO, BayesA, and BayesB). In general, RQR-based estimation of the GEBV was efficient—at least for a quantile model, the results obtained were more accurate than those obtained by the other evaluated methodologies. Specifically, in the symmetrical-distribution scenario, the highest accuracy values were obtained for the parameters with the models RQR0.4, RQR0.3, and RQR0.4. For positive skewness, the models RQR0.2, RQR0.3, and RQR0.1 presented higher accuracy values, whereas for negative skewness, the best model was RQR0.9. Finally, the GEBV vectors obtained by RQR facilitated the construction of genomic growth curves at different levels of interest (quantiles), illustrating the weight–age relationship.

Application of Data Mining to Classify Receiving Social Assistance Using the Naïve Bayes Method

The application of Naïve Bayes in classifying potential recipients of Non-Cash Food Assistance (BPNT) in Grujugan Kidul Village was made to solve problems in the process of distributing non-cash food assistance programs that had not run optimally and had not been on target. The purpose of this study is to find out how the Naïve Bayes Algorithm is applied in classifying Non-Cash Food Assistance Recipients in Grujugan Kidul Village so as to obtain optimal results. The method used in this study is the Naïve Bayes classification method. The Naïve Bayes algorithm is proven to have good performance in predictions, and produces high accuracy values. The stages of data analysis were carried out based on the CRISP-DM method while the algorithm testing was carried out on the RapidMiner 9.10.001 software as a comparison between manual calculations and software calculations. The results of the accuracy in this study were 89.19% from 8:2 Comparison of 80% test data, 20% test data.

Optimum timing for assessing phenotypic resistance against gastrointestinal nematodes in Pelibuey ewes.

The objective was to identify the optimal stage of production to evaluate the resistance of Pelibuey ewes against gastrointestinal nematodes (GIN). Faecal egg count (FEC) was used to classify the ewes as resistant, sensible or intermediate against GIN. Forty-seven ewes were mating during 30 d. The gestation was verified by ultrasonography, and the breeding date was used to calculate the productive stages. Faeces were taken weekly to determine the FEC. Blood samples were taken to determine the packed cell volume (PCV), the peripheral eosinophils count (PEC), plasma protein concentration (PP), and Immunoglobulin A (IgA) against Haemonchus contortus. The body condition score (BCS) was recorded at each visit. Six moments during the study (early, mid and late gestation; early, mid and late lactation) were considered. The ewes were classified according to FEC (mean FEC ± three standard errors). The higher FEC occurred during all lactation stages than during early and mid-gestation stages (P<0.05). PCV, PP, and BCS during early gestation stage were higher than shown during the lactation stages (P<0.01). The PEC and IgA were higher during all lactation stages than early and mid-gestation stages (P<0.05). Concerning the type of birth, double births showed higher FEC than single birth (P<0.01). The highest values of accuracy (100 %) and concordance (Youden's J = 1.0) were found during early lactation. Therefore, it is concluded that the optimal stage of production to evaluate phenotypic resistance against GIN infections in Pelibuey ewes was during the early lactation.

Predicting clinical decline over 36 months with a memory‐based digital biomarker

AbstractBackgroundWord‐list recall tests are routinely employed in clinical practice for dementia screening. Process scoring and latent modeling of these tests, where underlying neurocognitive mechanisms are exploited, have shown potential to enhance test accuracy without requiring test redesign. We examined Embic’s digital biomarker M, a measure of recall derived from multinomial processing trees and hierarchical Bayesian computational methods, as a predictor of conversion to mild cognitive impairment (MCI) and Alzheimer’s disease (AD), and of increase in Clinical Dementia Rating (CDR) score.MethodsSecondary data analyses were carried out from ADNI data. M was computed from Rey’s AVLT data, and compared to standard AVLT clinical metrics (total and delayed recall). We conducted logistic regression analyses with diagnosis at 36 months (normal vs. MCI + AD) as outcome; all 169 participants were classified as normal at baseline, and 13 of these progressed to a clinical diagnosis (see Table 1). We controlled for baseline age, gender and years of education; and ran separate analyses with other metrics to avoid multicollinearity. We then repeated the same analytical framework with CDR scores (stable at 0 vs. increased to 0.5 or 1) at 36 months as outcome: stable participants were 141, and 24 increased (see Table 2). Covariates and predictors were the same as in the previous analysis.Results M predicted conversion to MCI or AD after three years (FDR‐corrected p value = 0.048), while neither AVLT total nor delayed recall did. Performance metrics showed that M generated 92% accuracy, or probability of yielding a correct classification, and 100% specificity, or no false positives (Figure 1). Analogously, M, but not standard AVLT metrics, also predicted a CDR increase after three years (FDR‐corrected p value = 0.003), with high accuracy (84%), specificity (96%), and precision (80%), or positive predictive value (Figure 2). Sensitivity analyses with ordinal regression broadly confirmed these findings.ConclusionsThese results indicate Embic’s digital biomarker M outperforms some of AVLT’s traditional metrics in identifying individuals who will progress to MCI and AD after three years, from a healthy baseline.