Thermally magnetized Darcy–Forchheimer Eyringen micropolar material subject to chemical kinetics: A machine learning analysis

The Kem Kem Group of Southeastern Morocco, North Africa, is well known for theropod remains, especially isolated teeth. Here, a collection of isolated theropod teeth is assessed for diversity using a combination of linear discriminant, phylogenetic, and machine learning analyses for the first time. The results confirm earlier studies on Kem Kem theropod diversity, with teeth referred to Abelisauridae, Spinosaurinae, and Carcharodontosauridae. A single tooth is ascribed to a non-abelisauroid ceratosaur or a megaraptoran and may represent the enigmatic averostran Deltadromeus. Spinosaurine teeth are clearly differentiated by all three methodologies, whereas abelisaurid and carcharodontosaurid teeth could only be distinguished by the machine learning and phylogenetic analyses. This study shows that a combination of independent methods is most effective at providing strong evidence on theropod dental diversity in a particular assemblage, and that cladistic and machine learning analyses are the most reliable approaches to identify isolated dinosaur teeth. The methodology used here is likely to yield results in other dinosaur assemblages where isolated teeth are more abundant than body fossils.

Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors. Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland-Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort. Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai's trace: p < 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set. Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors.

BackgroundConsiderable number of people still use opium worldwide and many believe in opium’s health benefits. However, several studies proved the detrimental effects of opium on the body, especially the cardiovascular system. Herein, we aimed to provide the first evidence regarding the effects of opium use on one-year major adverse cardiovascular events (MACE) in the patients with ST-elevation MI (STEMI) who underwent primary PCI.MethodsWe performed a propensity score matching of 2:1 (controls: opium users) that yielded 518 opium users and 1036 controls. Then, we performed conventional statistical and machine learning analyses on these matched cohorts. Regarding the conventional analysis, we performed multivariate analysis for hazard ratio (HR) of different variables and MACE and plotted Kaplan Meier curves. In the machine learning section, we used two tree-based ensemble algorithms, Survival Random Forest and XGboost for survival analysis. Variable importance (VIMP), tree minimal depth, and variable hunting were used to identify the importance of opium among other variables.ResultsOpium users experienced more one-year MACE than their counterparts, although it did not reach statistical significance (Opium: 72/518 (13.9%), Control: 112/1036 (10.8%), HR: 1.27 (95% CI: 0.94–1.71), adjusted p-value = 0.136). Survival random forest algorithm ranked opium use as 13th, 13th, and 12th among 26 variables, in variable importance, minimal depth, and variable hunting, respectively. XGboost revealed opium use as the 12th important variable. Partial dependence plot demonstrated that opium users had more one-year MACE compared to non-opium-users.ConclusionsOpium had no protective effects on one-year MACE after primary PCI on patients with STEMI. Machine learning and one-year MACE analysis revealed some evidence of its possible detrimental effects, although the evidence was not strong and significant. As we observed no strong evidence on protective or detrimental effects of opium, future STEMI guidelines may provide similar strategies for opium and non-opium users, pending the results of forthcoming studies. Governments should increase the public awareness regarding the evidence for non-beneficial or detrimental effects of opium on various diseases, including the outcomes of primary PCI, to dissuade many users from relying on false beliefs about opium’s benefits to continue its consumption.

Effects of waste-based pyrolysis as heating source: Meta-analyze of char yield and machine learning analysis

This study aims to identify the key factors influencing the attrition rate of international students in South Korea by utilizing big data analysis, machine learning, and social network analysis. By analyzing 643 academic papers related to international students, this research seeks to propose measures to improve their retention rates. Furthermore, machine learning techniques are employed to pinpoint the most significant variables affecting student attrition. The analysis covers 221 four-year universities in Korea, focusing on variables such as the type of institution, student capacity, and geographical location. Results show that these universities can be categorized into seven nodes, with the admission competition rate of new students being the most influential factor; higher competition correlates with lower attrition rates. This study enhances objectivity and validity by employing advanced statistical techniques not used in previous research, providing scientific evidence to support policy improvements.

Does Corruption Control Enhance ESG-Induced Firm Value? Insights from Machine Learning Analysis

Research shows that effective teaching behavior is important for students' learning and outcomes, and scholars have developed various instruments for measuring effective teaching behavior domains. Although student assessments are frequently used for evaluating teaching behavior, they are mainly in Likert-scale or categorical forms, which precludes students from freely expressing their perceptions of teaching. Drawing on an open-ended questionnaire from large-scale student surveys, this study uses a machine learning tool aiming to extract teaching behavior topics from large-scale students’ open-ended answers and to test the convergent validity of the outcomes by comparing them with theory-driven manual coding outcomes based on expert judgments. We applied a latent Dirichlet allocation (LDA) topic modeling analysis, together with a visualization tool (LDAvis), to qualitative data collected from 173,858 secondary education students in the Netherlands. This data-driven machine learning analysis yielded eight topics of teaching behavior domains: Clear explanation, Student-centered supportive learning climate, Lesson variety, Likable characteristics of the teacher, Evoking interest, Monitoring understanding, Inclusiveness and equity, Lesson objectives and formative assessment. In addition, we subjected 864 randomly selected student responses from the same dataset to manual coding, and performed theory-driven content analysis, which resulted in nine teaching behavior domains and 19 sub-domains. Results suggest that the relation between machine learning and human analysis is complementary. By comparing the bottom-up (machine learning analysis) and top-down (content analysis), we found that the proposed topic modeling approach reveals unique domains of teaching behavior, and confirmed the validity of the topic modeling outcomes evident from the overlapping topics.

Conservative treatment remains the standard approach for first-time patellar dislocations. While risk factors for patellofemoral instability, a common paediatric injury, are well-established in adults, data concerning the progression of paediatric recurrent patellar dislocation remain scarce. A reproducible method was developed to quantitatively assess the patellofemoral morphology and anatomic risk factors in paediatric patients using magnetic resonance imaging (MRI) and machine learning analysis. Data were analyzed from a retrospective review (2005-2022) of paediatric patients diagnosed with acute lateral patellar dislocation (54 patients) who underwent MRI and were compared with an age-based control group (54 patients). Patellofemoral, tibial, tibiofemoral and patellar height parameters were measured. Differences between groups were analyzed with respect to MRI parameters. The potential diagnostic utility of the parameters was assessed via machine learning and genetic algorithm analyses. Significant differences were observed between the two groups in six patellofemoral morphological parameters. Regarding patellar height morphological parameters, all methods exhibited significant between-group differences. Among the tibia and tibiofemoral morphological parameters, only the tibial tubercle-trochlear groove distance exhibited significant differences between the two groups. No sex-related differences were present. Significant variations were observed in patellar height parameters, particularly in the Koshino-Sugimoto (KS) index, which had the highest area under the curve (AUC: 0.87). Using genetic algorithms and logistic regression, our model excelled with seven key independent variables. KS index and Wiberg index had the strongest association with lateral patellar dislocation. An optimized logistic regression model achieved an AUC of 0.934. Such performance is considered clinically relevant, indicating the model's effectiveness for the intended application. Level Ⅲ.

Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis

Label-free detection and discrimination of respiratory pathogens based on electrochemical synthesis of biomaterials-mediated plasmonic composites and machine learning analysis

The rising use of soft tissue fillers for aesthetic procedures has seen an increase in complications, including vascular occlusions and neurological symptoms that resemble stroke. This study synthesizes information on central nervous system (CNS) complications post-filler injections and evaluates the effectiveness of hyaluronidase (HYAL) treatment. A thorough search of multiple databases, including PubMed, EMBASE, Scopus, Web of Science, Google Scholar, and Cochrane, focused on publications from January 2014 to January 2024. Criteria for inclusion covered reviews and case reports that documented CNS complications related to soft tissue fillers. Advanced statistical and computational techniques, including logistic regression, machine learning, and Bayesian analysis, were utilized to dissect the factors influencing therapeutic outcomes. The analysis integrated findings from 20 reviews and systematic analyses, with 379 cases reported since 2018. Hyaluronic acid (HA) was the most commonly used filler, particularly in nasal region injections. The average age of patients was 38, with a notable increase in case reports in 2020. Initial presentation data revealed that 60.9% of patients experienced no light perception, while ptosis and ophthalmoplegia were present in 54.3 and 42.7% of cases, respectively. The statistical and machine learning analyses did not establish a significant linkage between the HYAL dosage and patient recovery; however, the injection site emerged as a critical determinant. The study concludes that HYAL treatment, while vital for managing complications, varies in effectiveness based on the injection site and the timing of administration. The non-Newtonian characteristics of HA fillers may also affect the incidence of complications. The findings advocate for tailored treatment strategies incorporating individual patient variables, emphasizing prompt and precise intervention to mitigate the adverse effects of soft tissue fillers. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Oral anticoagulant therapy (OAT) for managing atrial fibrillation (AF) encompasses vitamin K antagonists (VKAs, such as warfarin), which was the mainstay of anticoagulation therapy before 2010, and direct-acting oral anticoagulants (DOACs, namely dabigatran etexilate, rivaroxaban, apixaban, edoxaban), approved for the prevention of AF stroke over the last thirteen years. Due to the lower risk of major bleeding associated with DOACs, anticoagulant switching is a common practice in AF patients. Nevertheless, there are issues related to OAT switching that still need to be fully understood, especially for patients in whom AF and heart failure (HF) coexist. Herein, the effective impact of the therapeutic switching from warfarin to DOACs in HF patients with AF, in terms of cardiac remodeling, clinical status, endothelial function and inflammatory biomarkers, was assessed by a machine learning (ML) analysis of a clinical database, which ultimately shed light on the real positive and pleiotropic effects mediated by DOACs in addition to their anticoagulant activity.

Background: Acute Type A Aortic Dissection (ATAD) is a surgical emergency with 18% mortality. Anatomic segment (root, ascending, arch, descending) impacts aneurysm natural history but mechanisms remain unclear. Aim: To compare proteomic profiles of human thoracic aortic segments that could account for distinct phenotypes and clinical outcomes; to analyze the largest cohort to date with enhanced depth of coverage. Methods: Aortic tissues were collected (N=148) from 82 unique individuals and analyzed using our customized proteomics protocol ( Figure 1A ). Conventional statistics, machine learning (t-distributed Stochastic Neighbour Embedding, t-SNE), and functional enrichment analyses were used to characterize significant phenotypic differences by segment. Differential protein expression was validated using immunofluorescence. Results: From all samples, 7251 proteins were identified (5660 quantified), exceeding literature (by 100s-1000s). Significant differences were greatest in comparisons of root, ascending, and descending aorta ( Figure 1B-C ). Root vs descending and ascending vs descending comparisons had clear separation in t-SNE analysis; root and ascending samples also clustered modestly ( Figure 1D ). MFAP4, a cellular-binding protein previously associated with descending thoracic dissection in Marfan patients, was significantly elevated in ascending aortic segments compared to root and descending. This was validated by immunofluorescence ( Figure 1E ). Conclusions: We extensively profiled thoracic aneurysm tissue using enhanced coverage, customized proteomics from the largest known cohort of human samples. Thoracic aneurysm phenotype differs by aortic segment as a function of intrinsic biochemical (proteomic) processes. Potential thoracic aneurysm biomarkers likely must account for aortic segment. Multiomic integrative analysis (DNA, RNA, post-translational data) are underway.

Sarcopenia is defined as decreased skeletal muscle mass and function, and is an important cause of frailty in the elderly, also being associated with vascular lesions and poor microcirculation. The present study aimed to combine noninvasive pulse measurements, frequency-domain analysis, and machine learning (ML) analysis (1) to determine the effects on the pulse waveform induced by sarcopenia and (2) to develop discriminating models for patients with possible sarcopenia. Radial blood pressure waveform (BPW) signals were measured noninvasively for 1 min in 133 subjects who visited Tri-Service General Hospital for geriatric health checkups. They were assigned to a robust group and a possible-sarcopenia group that combined dynapenia, presarcopenia, and sarcopenia. Two classification methods were used: ML analysis and a self-developed scoring system that used 40 harmonic pulse indices as features: amplitude proportions and their coefficients of variation, and phase angles and their standard deviations. Significant differences were found in several spectral indices of the BPW between possible-sarcopenia and robust subjects. Threefold cross-validation results indicated excellent discrimination performance, with AUC equaling 0.77 when using LDA and 0.83 when using our scoring system. The present noninvasive and easy-to-use measurement and analysis method for detecting sarcopenia-induced changes in the arterial pulse transmission condition could aid the discrimination of possible sarcopenia.

A large international conference on Advances in Machine Learning and Data Analysis was held in UC Berkeley, California, USA, October 22-24, 2008, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2008). This volume contains sixteen revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Advances in Machine Learning and Data Analysis offers the state of the art of tremendous advances in machine learning and data analysis and also serves as an excellent reference text for researchers and graduate students, working on machine learning and data analysis.