Characteristic Plots Research Articles

Accuracy of MRI in detecting seminal vesicle invasion in prostate cancer: a systematic review and meta-analysis.

To determine the diagnostic test accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting seminal vesicle invasion (SVI). The Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed, the Excerpta Medica dataBASE (EMBASE) and Cochrane databases were search up to May 2023. We included studies that investigated the accuracy of mpMRI in detecting SVI when compared to radical prostatectomy specimens as the reference standard. Data extraction was performed by two independent reviewers to construct 2 × 2 tables, as well as patient and study characteristics. The methodological quality of the included studies was assessed with the Quality of Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity were pooled and presented graphically with summary receiver operator characteristic (SROC) plots. A total of 27 articles with 4862 patients were included for analysis. The summary sensitivity and specificity were 0.57 (95% confidence interval [CI] 0.45-0.68) and 0.95 (95% CI 0.92-0.99), respectively. Meta-regression indicated that there was no evidence that coil strength (P = 0.079), coil type (P = 0.589), year of publication (P = 0.503) or use of the Prostate Imaging-Reporting and Data System (P = 0.873) significantly influenced these results. The summary diagnostic odds ratio was 28.3 (95% CI 15.0-48.8) and the area under the curve for the SROC curve was 0.87. The I2 statistic was a modest 11.9%. In general, methodological quality was good. The use of mpMRI in detecting SVI has excellent specificity but poor sensitivity. Both endorectal coils and magnetic field strength do not significantly impact the accuracy of MRI. These findings suggest that mpMRI cannot reliably rule out SVI in patients with prostate cancer.

A comparative performance assessment of artificial intelligence based classifiers and optimized feature reduction technique for breast cancer diagnosis

Breast cancer (BC) is a catastrophic global health concern that causes numerous fatalities worldwide. Early detection of breast cancer may mitigate death rates; however, the prevailing diagnostic procedure for the malignancy necessitates numerous multifaceted laboratory tests that must be performed by medical professionals. In this article machine learning, a branch of Artificial Intelligence (AI), has been employed to improve cancer diagnosis, prognoses and survival rates while reducing the vulnerability of humans. Support Vector Machine (SVM), K-nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Grey Wolf Optimizer (GWO) are implemented to prognosticate breast cancer. Comprehensive insights into the efficacy of these approaches for breast cancer prognosis are provided by the performance assessment that is accomplished using the confusion matrix, Receiver Operating Characteristic (ROC) curves and parallel coordinate plots. Both UCI (University of California Irvine) and SEER (Surveillance, Epidemiology and End Results) datasets have been utilized to confirm the investigation's findings and ensure their generalizability across diverse data sources. The results conclusively demonstrate that SVM is the cohort's most accurate classifier. With a stupendous accuracy rate of 99.1 %, the GWO-SVM compares favorably to all other algorithms. Furthermore, feature reduction approaches such as Minimum Redundancy Maximum Relevance (mRMR), ReliefF and Principal Component Analysis (PCA) are utilized. ReliefF has demonstrated exceptional effectiveness with a maximum accuracy of 98.2 %.

Predicting implementation of response to intervention in math using elastic net logistic regression.

The primary objective of this study was to identify variables that significantly influence the implementation of math Response to Intervention (RTI) at the school level, utilizing the ECLS-K: 2011 dataset. Due to missing values in the original dataset, a Random Forest algorithm was employed for data imputation, generating a total of 10 imputed datasets. Elastic net logistic regression, combined with nested cross-validation, was applied to each imputed dataset, potentially resulting in 10 models with different variables. Variables for the models derived from the imputed datasets were selected using four methods, leading to four candidate models for final selection. These models were assessed based on their performance of prediction accuracy, culminating in the selection of the final model that outperformed the others. Method50 and Methodcoef emerged as the most effective, achieving a balanced accuracy of 0.852. The ultimate model selected relevant variables that effectively predicted RTI. The predictive accuracy of the final model was also demonstrated by the receiver operating characteristic (ROC) plot and the corresponding area under the curve (AUC) value, indicating its ability to accurately forecast math RTI implementation in schools for the following year.

A log odds of positive lymph nodes-based predictive model effectively forecasts prognosis and guides postoperative adjuvant chemotherapy duration in stage III colon cancer: a multi-center retrospective cohort study

BackgroundThe log odds of positive lymph nodes (LODDS) was considered a superior staging system to N stage in colon cancer, yet its value in determining the optimal duration of adjuvant chemotherapy for stage III colon cancer patients has not been evaluated. This study aims to assess the prognostic value of a model that combines LODDS with clinicopathological information for stage III colon cancer patients and aims to stratify these patients using the model, identifying individuals who could benefit from varying durations of adjuvant chemotherapy.MethodA total of 663 consecutive patients diagnosed with stage III colon cancer, who underwent colon tumor resection between November 2007 and June 2020 at Sun Yat-sen University Cancer Center and Longyan First Affiliated Hospital of Fujian Medical University, were enrolled in this study. Survival outcomes were analyzed using Kaplan–Meier, Cox regression. Nomograms were developed to forecast patient DFS, with the Area Under the Curve (AUC) values of time-dependent Receiver Operating Characteristic (timeROC) and calibration plots utilized to assess the accuracy and reliability of the nomograms.ResultsMultivariate analysis revealed that perineural invasion (HR = 1.776, 95% CI: 1.052–3.003, P = 0.032), poor tumor differentiation (HR = 1.638, 95% CI: 1.084–2.475, P = 0.019), and LODDS groupings of 2 and 1 (HR = 1.920, 95% CI: 1.297–2.842, P = 0.001) were independent predictors of disease-free survival (DFS) in the training cohort. Nomograms constructed from LODDS, perineural invasion, and poor tumor differentiation demonstrated robust predictive performance for 3-year and 5-year DFS in both training (3-year AUC = 0.706, 5-year AUC = 0.678) and validation cohorts (3-year AUC = 0.744, 5-year AUC = 0.762). Stratification according to this model showed that patients in the high-risk group derived significant benefit from completing 8 cycles of chemotherapy (training cohort, 82.97% vs 67.17%, P = 0.013; validation cohort, 89.49% vs 63.97%, P = 0.030).ConclusionThe prognostic model, integrating LODDS, pathological differentiation, and neural invasion, demonstrates strong predictive accuracy for stage III colon cancer prognosis. Moreover, stratification via this model offers valuable insights into optimal durations of postoperative adjuvant chemotherapy.

Establishing the cutoff value of near visual acuity for assessment of early presbyopia.

There is limited evidence to evaluate the numerical cutoff point for detecting early presbyopia. Thus, we aimed to establish a clinically relevant optimal cutoff value of near visual acuity for detecting early presbyopia. Prospective diagnostic accuracy study. We included consecutive individuals aged ≥ 20 years with a binocular-corrected distance visual acuity of ≥ 20/25 who did not undergo ophthalmic surgery between December 17, 2020 and December 19, 2021, at two healthcare facilities in Japan. Binocular distance-corrected near visual acuity at 40cm, accommodative amplitude, awareness of presbyopia, and Near Activity Visual Questionnaire scores were examined. The optimal cutoff values of distance-corrected near visual acuity for diagnosing early presbyopia were evaluated using receiver operating characteristic plots. Among 115 participants, 74 (64.3%) had presbyopia. The proportion of participants with no difficulty performing near-vision tasks decreased markedly when near visual acuity decreased to 20/20 (> 0.00 logMAR). A cutoff value of 0.00 logMAR for distance-corrected near visual acuity was optimal, showing high sensitivity of 56.76% and specificity of 92.68%, as opposed to the commonly used cutoff value of 0.40 logMAR (20/50; sensitivity, 9.46% and specificity, 100%) for diagnosing early presbyopia. Near visual acuity of 0.00 logMAR (20/20) could be the optimal cutoff value for diagnosing early presbyopia.

Visualizing and diagnosing spillover within randomized concurrent controlled trials through the application of diagnostic test assessment methods

BackgroundSpillover of effect, whether positive or negative, from intervention to control group patients invalidates the Stable Unit Treatment Variable Assumption (SUTVA). SUTVA is critical to valid causal inference from randomized concurrent controlled trials (RCCT). Spillover of infection prevention is an important population level effect mediating herd immunity. This herd effect, being additional to any individual level effect, is subsumed within the overall effect size (ES) estimate derived by contrast-based techniques from RCCT’s. This herd effect would manifest only as increased dispersion among the control group infection incidence rates above background.Methods and resultsThe objective here is to explore aspects of spillover and how this might be visualized and diagnosed. I use, for illustration, data from 190 RCCT’s abstracted in 13 Cochrane reviews of various antimicrobial versus non-antimicrobial based interventions to prevent pneumonia in ICU patients. Spillover has long been postulated in this context. Arm-based techniques enable three approaches to identify increased dispersion, not available from contrast-based techniques, which enable the diagnosis of spillover within antimicrobial versus non-antimicrobial based infection prevention RCCT’s. These three approaches are benchmarking the pneumonia incidence rates versus a clinically relevant range, comparing the dispersion in pneumonia incidence among the control versus the intervention groups and thirdly, visualizing the incidence dispersion within summary receiver operator characteristic (SROC) plots. By these criteria there is harmful spillover effects to concurrent control group patients.ConclusionsArm-based versus contrast-based techniques lead to contrary inferences from the aggregated RCCT’s of antimicrobial based interventions despite similar summary ES estimates. Moreover, the inferred relationship between underlying control group risk and ES is ‘flipped’.

Serum Levels of Arachidonic Acid, Interleukin-6, and C-Reactive Protein as Potential Indicators of Pulmonary Viral Infections: Comparative Analysis of Influenza A, Respiratory Syncytial Virus Infection, and COVID-19.

Acute respiratory tract infections, including influenza A (FluA), respiratory syncytial virus (RSV) infection, and COVID-19, can aggravate to levels requiring hospitalization, increasing morbidity and mortality. Identifying biomarkers for an accurate diagnosis and prognosis of these infections is a clinical need. We performed a cross-sectional study aimed to investigate the changes in circulating levels of arachidonic acid, interleukin 6 (IL-6), and C-reactive protein (CRP) in patients with FluA, RSV, or COVID-19, and to analyze the potential of these parameters as diagnosis or prognosis biomarkers. We analyzed serum samples from 172 FluA, 80 RSV, and 217 COVID-19 patients, and 104 healthy volunteers. Individuals with lung viral diseases showed reduced arachidonic acid concentrations compared to healthy people, with these differences being most pronounced in the order COVID-19 > RSV > FluA. Conversely, IL-6 and CRP levels were elevated across diseases, with IL-6 emerging as the most promising diagnostic biomarker, with areas under the curve (AUC) of the receiver operating characteristics plot higher than 0.85 and surpassing arachidonic acid and CRP. Moreover, IL-6 displayed notable efficacy in distinguishing between FluA patients who survived and those who did not (AUC = 0.80). These findings may provide useful tools for diagnosing and monitoring the severity of acute viral respiratory tract infections, ultimately improving patient outcomes.

Predictive Modeling of Canadian Carbonatite-Hosted REE +/− Nb Deposits

Carbonatites are the primary geological sources for rare earth elements (REEs) and niobium (Nb). This study applies machine learning techniques to generate national-scale prospectivity models and support mineral exploration targeting of Canadian carbonatite-hosted REE +/− Nb deposits. Extreme target feature label imbalance, diverse geological settings hosting these deposits throughout Canada, selecting negative labels, and issues regarding the interpretability of some machine learning models are major challenges impeding data-driven prospectivity modeling of carbonatite-hosted REE +/− Nb deposits. A multi-stage framework, exploiting global hierarchical tessellation model systems, data-space similarity measures, ensemble modeling, and Shapley additive explanations was coupled with convolutional neural networks (CNN) and random forest to meet the objectives of this work. A risk–return analysis was further implemented to assist with model interpretation and visualization. Multiple models were compared in terms of their predictive ability and their capability of reducing the search space for mineral exploration. The best-performing model, derived using a CNN that incorporates public geoscience datasets, exhibits an area under the curve for receiver operating characteristics plot of 0.96 for the testing labels, reducing the search area by 80%, while predicting all known carbonatite-hosted REE +/− Nb occurrences. The framework used in our study allows for an explicit definition of input vectors and provides a clear interpretation of outcomes generated by prospectivity models.

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning

BackgroundPrior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia.MethodsTwo osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein–protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated.ResultsThe merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93).ConclusionWe identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.

External validation and updating of the Rossi nomogram for predicting cesarean delivery following induction: is the Bishop score valuable?

This study sought to validate the Rossi nomogram in a Chinese population and then to include the Bishop score to see if it has an effect on the accuracy of the nomogram. The Rossi predictive model was applied and externally validated in a retrospective cohort from August 2017 and July 2023 in a Chinese tertiary-level medical center. For the revision and updating of the models, the regression coefficients of all the predictors (except race) were re-estimated and then the cervical Bishop score at the time of induction was added. Each model's performance was measured using the receiver-operating characteristic and calibration plots. Decision curve analysis determined the range of the probability threshold for each prediction model that would be of clinical value. A total of 721 women met the inclusion criteria, of whom 183 (25.4%) underwent a cesarean delivery. The calibration demonstrated the underestimation of the original model, with an area under the curve (AUC) of 0.789 (95% confidence interval [CI] 0.753-0.825, p < 0.001). After recalibrating the original model, the discriminative performance was improved from 0.789 to 0.803. Moreover, the discriminatory power of the updated model was further improved when the Bishop score at the time of induction was added to the recalibrated multivariable model. Indeed, the updated model demonstrated good calibration and discriminatory power, with an AUC of 0.811. The decision curve analysis indicated that all the models (original, recalibrated, and updated) provided higher net benefits of between 0 and 60% of the probability threshold, which indicates the benefits of using the models to make decisions concerning patients who fall within the identified range of the probability threshold. The net benefits of the updated model were higher than those of the original model and the recalibrated model. The nomogram used to predict cesarean delivery following induction developed by Rossi et al. has been validated in a Chinese population in this study. More specifically, adaptation to a Chinese population by excluding ethnicity and including the Bishop score prior to induction gave rise to better performance. The three models (original, recalibrated, and updated) offer higher net benefits when the probability threshold is between 0 and 60%.

Evaluation of Transformer Protection System Coordination at ULTG Jeneponto

Power transformers are essential components in electrical power systems as they transform electrical quantities. In practical terms, electrical power systems may experience disturbances such as short-circuit faults, either between phases or to ground. One type of relay used to accommodate these faults is the Over Current Relay (OCR) for phase-to-phase faults and the Ground Fault Relay (GFR) for ground faults. This research evaluates the coordination settings of transformer protection systems at PT PLN (Persero) ULTG Jeneponto by analyzing the characteristics plot of each transformer unit. The evaluation results reveal intersecting characteristics on the high voltage (HV) and medium voltage (MV) sides of Transformers #3 Tallasa and #3 Bulukumba. By implementing recommended settings for these two transformer units, anomalies can be addressed, and the protection system can coordinate effectively.

Assessment of balance and fear of falling in individuals with acute ankle sprain: A cross-sectional pilot study

Objectives: The primary objective of this research was to assess balance, pain, and fear of falling (FOF) among individuals with an acute ankle sprain. The secondary objective was to correlate FOF with pain and balance. Methods: The research was conducted among individuals with acute ankle sprain —two to three days after removal of two weeks of casting at M S Ramaiah hospitals, Bengaluru. The primary outcome measures were the Short Falls Efficacy Scale-International (Short FES-I) and the star excursion balance test (SEBT), and the secondary outcome measure was the numerical pain rating scale (NPRS). Cutoff points for Short FES-I defined in prior research using receiver operator characteristic plots were used in this study. For Short FES-I, frequencies and percentages were employed. The NPRS and SEBT were measured using means and standard deviations. Results: The results showed that among 21 individuals, 76.2% had FOF. Among those with FOF, 56.25% had moderate FOF, and 43.75% had severe FOF. Mean SEBT scores were greater for the uninvolved limb than for the involved limb. Spearman’s Rho test showed that the Short FES-I and NPRS showed no correlation (P = 0.102 &gt; 0.05), and Short FES-I and SEBT showed a significant correlation (P = 0.003 &lt; 0.05). Conclusion: The FOF was present in two-thirds of those with acute ankle sprains, and balance impairment was present in individuals with Grade II ankle sprains. Among individuals with FOF, the degree of FOF was moderate to severe. There was a significant negative correlation between FOF and balance, but no correlation was found between FOF and pain.

Evaluation of nuclear morphometry in exfoliative cytology of buccal mucosa in patients with high risk of oral cancer

BackgroundOral cancer poses a significant global health burden, with India having the highest prevalence. Effective detection is crucial in effective prevention. This study aimed to evaluate nuclear morphometric parameters (NMPs) in buccal mucosa cells of smokers, correlate NMPs with dysplasia, establish cut off values for grading dysplasia, and investigate the relationship between NMPs and smoking. MethodsAfter obtaining ethical approval and informed consent, patients were recruited from the outpatient department of our institution. A target sample size of 250 was calculated. The data included smoking exposure quantified in pack-years, nuclear morphometric analysis (NMA) of buccal mucosa cells obtained through oral cytology using Image J, and the severity of dysplasia of the slides assessed by pathologists. Statistical analysis assessed the impact of dysplasia and the association between nuclear characteristics and smoking exposure. Receiver operating characteristic (ROC) plots determined the potential of these parameters to distinguish dysplasia levels. ResultsSignificant differences in NMPs were observed among different smoking groups. Dysplasia severity had a significant correlation with NMPs, and strong correlations were found between NMPs and lifetime smoking exposure. ROC analysis established cut off values for NMPs with good sensitivity and specificity for classifying dysplasia severity. ConclusionsThis study highlights the potential of NMA as a tool for oral cancer screening. NMPs can distinguish dysplasia severity and correlate with tobacco (smoking). The efficiency of NMA in a non-invasive oral cytology offers promise for patient-centered screening Additionally, the findings suggest future applications in telepathology and the potential for AI integration in automated screening after conducting multicentric large-scale studies.

Noninvasive and Multiplex Self-Test of Kidney Disease Biomarkers with Graphene-Based Lab-on-a-Chip (G-LOC): Toward Digital Diagnostics in the Hands of Patients.

Chronic kidney disease is one of the major health issues worldwide. However, diagnosis is now highly centralized in large laboratories, resulting in low access to patient monitoring and poor personalized treatments. This work reports the development of a graphene-based lab-on-a-chip (G-LOC) for the digital testing of renal function biomarkers in serum and saliva samples. G-LOC integrates multiple bioelectronic sensors with a microfluidic system that enables multiplex self-testing of urea, potassium, sodium, and chloride. The linearity, limit of detection (LOD), accuracy, and coefficient of variability (CV) were studied. Accuracy values higher than 95.5% and CV lower than 9% were obtained for all of the biomarkers. The analytical performance was compared against three reference lab benchtop analyzers by measuring healthy- and renal-failure-level samples of serum. From receiver operating characteristic (ROC) plots, sensitivities (%) of 99.7, 97.6, 99.1, and 89.0 were obtained for urea, potassium, sodium, and chloride, respectively. Then, the test was evaluated in noninvasive saliva samples and compared against reference methods. Correlation and Bland-Altman plots showed good correlation and agreement of the G-LOC with the reference methods. It is noteworthy that the precision of G-LOC was similar to better than benchtop lab analyzers, with the advantage of being highly portable. Finally, a user testing study was conducted. The analytical performance obtained with untrained volunteers was similar to that obtained with trained chemists. Additionally, based on a user experience survey, G-LOC was found to have very simple usability and would be suitable for at-home diagnostics.

An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study

BackgroundShortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices.MethodsFirst, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology.ResultsThe number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 − 96.26%]), whilst the specificity was 85.45% [77.45 − 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 − 95.13%) and 87.85% (80.12 − 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 − 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy.ConclusionsCombining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.

A new propulsion system GUI based control amenable model development for high‐power rockets

SummaryThis paper proposes a new algorithm to model and characterize an autonomous high‐power rocket using an indigenously developed graphical user interface (GUI) platform. This platform features a newly devised app, termed as THIEC Rocketry App which embeds the simulation based analysis to determine the design parameters of the rocket, required for a vertical flight. A solid propellant using potassium nitrate and sucrose, also known as rocket‐candy, is considered for the GUI development. The GUI facilitates the designer to specify the desired flight parameters for the rocket propulsion system. Various characteristic plots for visualization and analysis are made available in GUI. The obtained parameters from the GUI are then utilized in computer‐aided designing (CAD) for further identification of geometrical parameters like inertia tensor, center of gravity (CG) and center of pressure (CP). The mathematical control amenable model of the rocket is then developed using first principles so as to achieve an altitude up to 3 km. The overall system represents a complex nonlinear multi‐input multi‐output (MIMO) dynamics, having six degrees of freedom. The Newton‐Euler formulation is employed to develop the equations of motion. The attitude control using canards is analyzed via simulations for the complete flight path ‐ the boost and coast flights. Finally, the developed GUI based model is validated by practically manufacturing the components of the propulsion system for the small‐scale high‐power rocket. The proposed model will create the pathway for the development of some robust model‐based control schemes for such autonomous rockets in future.

Performance evaluation and verification of post-processing methods for TIGGE ensemble data using machine learning approaches

Abstract Ensemble modelling has become a significant technique in the field of machine learning, as it utilises the combined knowledge of multiple base models to improve the accuracy of predictions in different domains. Nevertheless, the effectiveness of ensemble predictions relies on the implementation of post-processing techniques that enhance and optimize the outputs of the ensemble. This study explores the domain of ensemble data post-processing, utilizing a machine learning-focused methodology to thoroughly assess and contrast a variety of post-processing methods. TIGGE Ensemble data from ECMWF and NCEP were used from 2010 to 2020. Research covers machine learning approaches post-processing methods such as BMA, cNLR, HXLR, OLR, logreg, hlogreg, QM were applied. The probabilistic forecasts were validated using the Brier Score (BS), Area Under Curve (AUC) of Receiver Operator Characteristics (ROC) plots and reliability plots. The cNLR and BMA strategies for post-processing performed exceptionally well with BS value of 0.10 and RPS value of 0.11 at all grid points for both methods. The ROC–AUC values for the cNLR and BMA methods were found to be 91.87 and 91.82%, respectively. The results show that improved post-processing techniques can be helpful to predict the flood in advance with accurate precision and warning.

Comparing 2-dimensional macular pigment optical density with objective and subjective perimetry and visual acuity in age-related macular degeneration

PurposeTo compare diagnostic power for different severities of age-related macular degeneration (AMD) of two-dimensional macular pigment optical densities (2D-MPOD) and spatially matched objective perimetry, with standard perimetry and best-corrected visual acuity (BCVA).MethodsThe ObjectiveField Analyser (OFA) provided objective perimetry, and a Heidelberg Spectralis optical coherence tomography (OCT) measured 2D-MPOD in AMD patients, both completed twice over 0.99 ± 0.16 years. From each 2D-MPOD image, we extracted 20 regions/macula, matched to the 20 OFA stimuli/macula. For each region, we calculated 7 measures from the 2D-MPOD pixel values and correlated those with OFA sensitivities and delays. We quantified 2D-MPOD changes, the ability of 2D-MPOD and OFA to discriminate AMD stages, and the discriminatory power of Matrix perimetry and BCVA using percentage area under receiver operator characteristic plots (%AUROC).ResultsIn 58 eyes of 29 subjects (71.6 ± 6.3 years, 22 females), we found significant correlations between 2D-MPOD and OFA sensitivities for Age-Related Eye Disease Studies (AREDS)-3 and AREDS-4 severities. Delays showed significant correlations with AREDS-2. For AREDS-4, correlations extended across all eccentricities. Regression associated with the Bland–Altman plots showed significant changes in 2D-MPOD over the study period, especially variability measures. MPOD per-region medians discriminated AREDS-1 from AREDS-3 eyes at a %AUROC of 80.0 ± 6.3%, outperforming OFA, Matrix perimetry, and BCVA.ConclusionsMPOD changes correlated with central functional changes and significant correlations extended peripherally in later-stage AMD. Good diagnostic power for earlier-stage AMD and significant change over the study suggest that 2D-MPOD and OFA may provide effective biomarkers.

The Graphic Legacy of Oleksandr Bogomazov: General Overview and Artist’s Notes in Some Thoughts on Graphics

The study presents a general overview of the graphic work and the publication of theoretical notes about the graphics of the Ukrainian artist O. K. Bohomazov. There are features of graphic works in different periods of creativity, which are noted, there are stylistic features of works of different periods,which are analyzed. Graphic works give an opportunity to trace all stylistic changes and identify reference points in the evolution of the artist’s work. Sketches allow to trace the stages of work on the creation of picturesque easel paintings. The graphic heritage of O. Bohomazov is really diverse. There are characteristic symbolist motifs and plots, which can be traced in the early graphics. Magazine graphics of 1908-1912 provide samples of the Art Nouveau style. In the works of this period, the line gives the picture maximum decorativeness. Fascination with color linocut gave the artist the opportunity to experiment with changing the psychological state of objects depending on the color change. At the turn of 1912-1913, a radical stylistic change takes place in O. Bohomazov’s work: the generalized decorativeness and linear rhythms of the modern style give way to the dynamics of futurism. While working at the Kyiv Art Institute in 1922–1930, Oleksandr Bohomazov taught painting and drawing as formal and technical disciplines. He taught in his educational programs the physical and psychological properties of colours and their interaction. The graphics of the late period of the artist’s work acquired a neo-realistic objectivity in the spirit of "New objectivity" (Die Neue Sachlichkeit).

An improved method for estimating low LDL-C based on the enhanced Sampson-NIH equation

BackgroundThe accurate measurement of Low-density lipoprotein cholesterol (LDL-C) is critical in the decision to utilize the new lipid-lowering therapies like PCSK9-inhibitors (PCSK9i) for high-risk cardiovascular disease patients that do not achieve sufficiently low LDL-C on statin therapy.ObjectiveTo improve the estimation of low LDL-C by developing a new equation that includes apolipoprotein B (apoB) as an independent variable, along with the standard lipid panel test results.MethodsUsing β-quantification (BQ) as the reference method, which was performed on a large dyslipidemic population (N = 24,406), the following enhanced Sampson-NIH equation (eS LDL-C) was developed by least-square regression analysis:eSLDL-C=TC1.15-HDL-C1.25-TG6.99-TG×NonHDL-C1120+TG28910+TG×ApoB1240+ApoB4.54-4.73\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$eS\\,LDL-C= \\frac{TC}{1.15}-\\frac{HDL-C}{1.25}-\\frac{TG}{6.99}-\\frac{\\left(TG\ imes NonHDL-C\\right)}{1120}+\\frac{{TG}^{2}}{8910}+\\frac{\\left(TG\ imes ApoB\\right)}{1240}+\\frac{ApoB}{4.54}-4.73$$\\end{document}ResultsThe eS LDL-C equation was the most accurate equation for a broad range of LDL-C values based on regression related parameters and the mean absolute difference (mg/dL) from the BQ reference method (eS LDL-C: 4.51, Sampson-NIH equation [S LDL-C]: 6.07; extended Martin equation [eM LDL-C]: 6.64; Friedewald equation [F LDL-C]: 8.3). It also had the best area-under-the-curve accuracy score by Regression Error Characteristic plots for LDL-C < 100 mg/dL (eS LDL-C: 0.953; S LDL-C: 0.920; eM LDL-C: 0.915; F LDL-C: 0.874) and was the best equation for categorizing patients as being below or above the 70 mg/dL LDL-C treatment threshold for adding new lipid-lowering drugs by kappa score analysis when compared to BQ LDL-C for TG < 800 mg/dL (eS LDL-C: 0.870 (0.853–0.887); S LDL-C:0.763 (0.749–0.776); eM LDL-C:0.706 (0.690–0.722); F LDL-C:0.687 (0.672–0.701). Approximately a third of patients with an F LDL-C < 70 mg/dL had falsely low test results, but about 80% were correctly reclassified as higher (≥ 70 mg/dL) by the eS LDL-C equation, making them potentially eligible for PCSK9i treatment. The M LDL-C and S LDL-C equations had less false low results below 70 mg/dL than the F LDL-C equation but reclassification by the eS LDL-C equation still also increased the net number of patients correctly classified.ConclusionsThe use of the eS LDL-C equation as a confirmatory test improves the identification of high-risk cardiovascular disease patients, who could benefit from new lipid-lowering therapies but have falsely low LDL-C, as determined by the standard LDL-C equations used in current practice.