Supervised Machine Learning Models Research Articles

Prediction of movies popularity in supervised learning techniques

When movies industry gradually become heavy capital, the prediction of movies popularity as well as their commercial potentiality based on historical data has become a popular research topic in the field of data analysis using machine learning models. In this paper, researchers trained three supervised machine learning models (Random Forest, Naive Bayesian Model and Support Vector Regression) using IMBD dataset to predict a movies popularity. This research has two outcomes: (1) the Random Forest Model has the highest accuracy rate; (2) the number of Oscar-winner included in both cast and crew is most positively related to a movies popularity.

ASSESSMENT OF DATA COLLECTION TECHNIQUES IN MANUFACTURING AND MECHANICAL ENGINEERING THROUGH MACHINE LEARNING MODELS

This research conducts a systematic literature review and case study analysis to compare the Design of Experiments (DOE) and Active Learning (AL) methods in the context of machine learning within manufacturing and mechanical engineering. The objective is to evaluate the efficacy of these data-gathering methods in informing Supervised Machine Learning models. The review began with a search yielding 1280 documents, culminating in a final selection of 35 articles after meticulous screening and eligibility checks. The findings illustrate that DOE methods provide comprehensive insights into process variables, while AL methods offer significant efficiency gains by requiring less data to achieve similar or improved model performance. Case studies demonstrate the practical applications and highlight the potential of a hybrid approach that integrates the thoroughness of DOE with the efficiency of AL. The research identifies gaps in the current literature, particularly in the real-world application of AL and its integration with emerging technologies. The conclusion suggests that future research should focus on developing sophisticated AL models to navigate the increasing complexity of manufacturing environments and that a nuanced approach to selecting data-gathering methods is crucial.

Machine-learning prediction of treatment response to stereotactic body radiation therapy in oligometastatic gynecological cancer: A multi-institutional study

Background and purposeWe aimed to develop and validate different machine-learning (ML) prediction models for the complete response of oligometastatic gynecological cancer after SBRT. Material and methodsOne hundred fifty-seven patients with 272 lesions from 14 different institutions and treated with SBRT with radical intent were included. Thirteen datasets including 222 lesions were combined for model training and internal validation purposes, with an 80:20 ratio. The external testing dataset was selected as the fourteenth Institution with 50 lesions. Lesions that achieved complete response (CR) were defined as responders. Prognostic clinical and dosimetric variables were selected using the LASSO algorithm. Six supervised ML models, including logistic regression (LR), classification and regression tree analysis (CART) and support vector machine (SVM) using four different kernels, were trained and tested to predict the complete response of uterine lesions after SBRT. The performance of models was assessed by receiver operating characteristic curves (ROC), area under the curve (AUC) and calibration curves. An explainable approach based on SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanations of the model's decisions. Results63.6% of lesions had a complete response and were used as ground truth for the supervised models. LASSO strongly associated complete response with three variables, namely the lesion volume (PTV), the type of lesions (lymph-nodal versus parenchymal), and the biological effective dose (BED10), that were used as input for ML modeling. In the training set, the AUCs for complete response were 0.751 (95% CI: 0.716–0.786), 0.766 (95% CI: 0.729–0.802) and 0.800 (95% CI: 0.742–0.857) for the LR, CART and SVM with a radial basis function kernel, respectively. These models achieve AUC values of 0.727 (95% CI: 0.669–0.795), 0.734 (95% CI: 0.649–0.815) and 0.771 (95% CI: 0.717–0.824) in the external testing set, demonstrating excellent generalizability. ConclusionML models enable a reliable prediction of the treatment response of oligometastatic lesions receiving SBRT. This approach may assist radiation oncologists to tailor more individualized treatment plans for oligometastatic patients.

Multilayer Perceptron Artificial Neural Networks and Tree Models as Multifactorial Binary Predictors of Heart Disease and Failure

BACKGROUND: Cardiovascular diseases (CVDs) are a significant global health concern, causing an estimated 17.9 million deaths annually, which represents 31% of worldwide deaths. A significant proportion of CVD deaths are due to heart attacks and strokes, with one-third of these deaths occurring prematurely in individuals under 70 years old. Heart failure is a notable event within CVDs and emerges when the heart cannot efficiently pump blood to fulfill the body's requirements. This complex syndrome's origins are multifactorial and often arise from conditions such as hypertension, diabetes, and hyperlipidemia. Large datasets with multiple features offer an opportunity for machine learning to aid in the early detection and prediction of heart failure. METHODS: The study employed an unmatched case-control retrospective design. Supervised machine learning models were utilized, notably Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) and decision tree-based models, to predict heart failure disease using data from 918 patients. The open licensed dataset, a combination from five independent heart datasets, comprises 11 demographic and clinical features related to patient status. The MLP-ANN, equipped with a hidden layer and a hyperbolic tangent activation function, was trained on 70% of the data and tested on the remaining 30%. Additionally, the study evaluated the decision tree model's performance through split-sample validation and 10-fold cross-validation. RESULTS: The ANN model demonstrated an accuracy of 88.10% in predicting heart disease (AUROC = 0.942) based on six factors and five covariates. The cross-validated tree model achieved an overall predictive accuracy of 84.3%. In contrast, the split-sample validated tree model, which used a balanced 50-50 data split for training and testing, attained an accuracy of 82.0%. OldPeak (ST depression induced by exercise relative to rest) had the highest normalized importance calculated from the MLP ANN model. CONCLUSION: Machine learning predictions have gained importance in healthcare, presenting potential benefits in early detection and intervention, leading to improved patient outcomes and reduced healthcare expenses. The study revealed that ANNs outperform decision tree models in accuracy for the dataset in use. Furthermore, the research emphasized the significance of the clinical feature "Oldpeak" in predicting heart failure through ANNs. ANNs can discern intricate relationships between variables and recognize non-linear interactions, a capability sometimes missed by decision tree models. However, the efficacy of machine learning models remains dependent on the quality and volume of the available data.

OptimizedFeature Selection and Transformations for Early Stage Prediction of Autism Using Supervised Machine Learning Models

OptimizedFeature Selection and Transformations for Early Stage Prediction of Autism Using Supervised Machine Learning Models

Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection

In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.

Classifying High Risk Diabetic Patients using Supervised Machine Learning Models

Classifying High Risk Diabetic Patients using Supervised Machine Learning Models

CreCENT: Creatinine and Chloride based Electrochemical Non-faradaic renal health mapping Technology

In this work, we propose a novel biosensing scheme to enable the stratification of kidney diseases based on severity and progression for timely triage and efficient management. Here, using Creatinine and Chloride as target analytes for the biosensor, we have discussed an arbitrary three-class stratification mapping renal health for Chronic Kidney Disease (CKD) management. Our method is fully quantitative, fast (<5 min turn-around time), and can work with any combination of disease biomarkers to categorize diseases by subtypes and severity. At its core, the biosensor relies on electrochemical impedance spectroscopy to transduce subtle changes at the input Creatinine and Chloride levels in a drop of neat, unprocessed urine. It can operate over a wide dynamic range of 0.15-5 mg/mL for Creatinine and 15-105 mM for Chloride. Further, as proof of concept, the biosensing scheme utilizes a simple Support Vector Machine-based supervised machine learning model for 3-state output disease state classification (corresponding to low, medium, and high disease severity) with a 97.96% accuracy. This scheme is versatile and can be extended to more complex scenarios with more biomarker input stimuli for improved diagnostics and precision therapy for other chronic urological diseases.

M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks.

Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize the need for large annotated samples by actively sampling the most informative examples for annotation. These examples contribute significantly to improving the performance of supervised machine learning models, and thus, active learning can play an essential role in selecting the most appropriate information in deep learning-based diagnosis, clinical assessments, and treatment planning. Although some existing works have proposed methods for sampling the best examples for annotation in medical image analysis, they are not task-agnostic and do not use multimodal auxiliary information in the sampler, which has the potential to increase robustness. Therefore, in this work, we propose a Multimodal Variational Adversarial Active Learning (M-VAAL) method that uses auxiliary information from additional modalities to enhance the active sampling. We applied our method to two datasets: i) brain tumor segmentation and multi-label classification using the BraTS2018 dataset, and ii) chest X-ray image classification using the COVID-QU-Ex dataset. Our results show a promising direction toward data-efficient learning under limited annotations.

Can we use machine learning to predict cognitive performance from actigraphy data? Preliminary results from the UK Biobank Study

AbstractBackgroundCircadian rhythms (i.e., the ∼24‐hour biological clock) are critical to the maintenance of the sleep‐wake cycle, and sleep‐wake disturbances are common in people at risk for cognitive decline and dementia. Several studies have identified circadian factors associated with cognitive decline using actigraphy (a common field measure for indexing the sleep‐wake cycle). However, there are currently untapped opportunities to use the power of artificial intelligence, specifically machine learning (ML), to improve our ability to identify signs of cognitive decline from actigraphy data. As a first step towards this goal, we examined the utility of two supervised ML models for predicting cognitive performance using data from the UK Biobank study.MethodA cross‐sectional analysis of participants in the UK Biobank study (40‐69 years at entry) with valid actigraphy data and complete cognitive data (N = 49,469). Participants completed computerized versions of Trail Making Test B‐A (TMT) and Digit Symbol Substitution Test (DSST). Actigraphy data were collected over 7 days, with average hourly movement being indexed. Along with 24‐hour actigraphy data, we included the following features in each model: age, biological sex, household income, educational attainment, smoking and alcohol intake, ethnicity, body mass index, and Townsend Deprivation Index. Seventy percent of participants were randomized to the training set, with the remaining 30% held out as a test set. We developed two separate ML models to predict cognitive performance: 1) a linear regression approach; and 2) a 3‐hidden layer (40 hidden units per layer) neural network. Model accuracy was compared using the coefficient of determination (R2).ResultMean age was 55 years (SD = 8 years) and 56% of participants were female. Average TMT time was 27.22 seconds (SD = 20.10 seconds), and mean DSST score was 20.06 (SD = 5.04). Our supervised linear regression had modest predictive ability of TMT (R2 = 8%) and DSST performance (R2 = 21%); the 3‐hidden layer neural network had similar predictive capability for both TMT (R2 = 8%) and DSST (R2 = 21%).ConclusionML approaches for predicting cognitive performance using actigraphy data show modest capability. Further work is needed to identify how ML can be used to predict cognitive function from biometric data.

Fracture risk assessment from 2D-DXA scan by supervised machine learning modeling

Fracture risk assessment from 2D-DXA scan by supervised machine learning modeling

Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease.

Ventricular tachycardia (VT) recurrence after catheter ablation remains a concern, emphasizing the need for precise risk assessment. We aimed to use machine learning (ML) to predict 1-month and 1-year VT recurrence following VT ablation. For 337 patients undergoing VT ablation, we collected 31 parameters including medical history, echocardiography, and procedural data. 17 relevant features were included in the ML-based feature selection, which yielded six and five optimal features for 1-month and 1-year recurrence, respectively. We trained several supervised machine learning models using 10-fold cross-validation for each endpoint. We observed 1-month VT recurrence was observed in 60 (18%) cases and accurately predicted using our model with an area under the receiver operating curve (AUC) of 0.73. Input features used were hemodynamic instability, incessant VT, ICD shock, left ventricular ejection fraction, TAPSE, and non-inducibility of the clinical VT at the end of the procedure. A separate model was trained for 1-year VT recurrence (observed in 117 (35%) cases) with a mean AUC of 0.71. Selected features were hemodynamic instability, the number of inducible VT morphologies, left ventricular systolic diameter, mitral regurgitation, and ICD shock. For both endpoints, a random forest model displayed the highest performance. Our ML models effectively predict VT recurrence post-ablation, aiding in identifying high-risk patients and tailoring follow-up strategies.

Supervised machine learning model to predict mortality in patients undergoing venovenous extracorporeal membrane oxygenation from a nationwide multicentre registry

BackgroundExisting models have performed poorly when predicting mortality for patients undergoing venovenous extracorporeal membrane oxygenation (VV-ECMO). This study aimed to develop and validate a machine learning (ML)-based prediction model to...

Automated Peripheral Smear Recognition and Classification of LGL Leukemia Using Machine Learning

Background: Machine learning can augment clinical decision support, especially in cases where complex diagnostic criteria are necessary. In making a diagnosis of LGL leukemia, a pathologist must review the peripheral smear thoroughly, which is a tedious process. Supervised machine learning has been successfully implemented in such cases by training on very large image-based datasets, followed by a corrective phase of updating the model weights and finally demonstrating a high degree of successful predictions in an independent dataset. We sought to assess whether a supervised machine learning model can automate histopathological image analysis and provide a degree of confidence in supporting a pathologist's manual review. Methods: We searched the published literature from 2016-2022 for high-definition peripheral smear images with confirmed cases. The samples were downloaded, pre-processed with the Segment Anything Model (SAM), and labeled as training, testing, or validation data. Non-pathological controls, defined as peripheral smears confirmed negative for LGLL, were also obtained from published literature and labeled manually. The neural network was written and trained on TensorFlow using the open-source Keras library with parallel threading and on a multicore GPU machine. The resulting predictive model had a binary classification task: Does the presented image have cells that resemble LGL cells, returning true or false. Every “true” label returned from the classifier also creates a stack of probable positives that need pathology review. Feedback from each pathologist review further updates the model weights to increase the probability of successful prediction in the future. Results: We screened 57 publications to obtain 163 high-definition peripheral smear images that had confirmed LGLL and 100 non-pathological smears. Two cohorts were created: 200 smears in a training cohort comprised of 100 LGLL smears and 100 non-pathological smears, and 63 smears in the validation cohort. To remain agnostic towards image orientation, we created 7 views of each smear, and 1400 total images underwent feature extraction using SAM. The resulting data was labeled and used for model training and our process is depicted visually below in Figure 1. During validation, our model accurately predicted LGL-cells in 56/63 cases with an 8% false positive rate, 2% false negatives and 2% unclassifiable. After updating model weights and re-training with Adaptive Boosting, the accuracy increased to 60/63 smears with a false-positive rate of 3%. Our trained classification model achieved an AUC of 0.8144. Conclusion: Supervised machine learning model with Adaptive Boosting can be used to label a peripheral smear image with a high likelihood of containing LGL cells and, in turn, help a pathologist review smears with higher priority in a timely fashion. Our initial training data was limited, which hindered the performance of our model; however, with a sufficiently large training dataset, we can drastically improve our accuracy. A more extensive training set with robust hardware can transform our image-recognition model into a foundational model where other clinical parameters can be added, and the final diagnostic probability will be based on multiple data streams. Our work takes a meaningful step in this direction, and future work at Karmanos Cancer Center will focus on extending our classifier to more sophisticated deep-learning algorithms. Our open-source platform will eventually support several niches in the future that are currently only served by commercial applications.

Real-World Demonstration of AI to Clinical Cytometry for Rapid and Reliable Decision Support and Automated Reporting of Blood Cancers Including B-NHL and Acute Leukemias

Introduction: Flow cytometry is an integral part of routine diagnostics for hematological malignancies. Unfortunately, a decreasing number of skilled operators have to cope with increasing case volumes. The current methods are also known to produce inter- and intra-observer variability. Aim: To reduce labor time, dependency on expert knowledge and interpretation variability, we aimed to build and validate clinical-grade decision support software in the routine using machine learning (ML) for clinical cytometry. Methods: We obtained ≈100k flow cytometry cases from multiple centers, taken from clinical routine and annotated by expert users using multiple diagnostic methods. These cases span the full spectrum of hematological malignancy, including 47% non-malignant reference samples. As the data were not measured using harmonized protocols, we developed data pooling techniques to merge the data, including synthetic imputation of missing markers. After data processing, we applied multi-layer artificial neural networks to predict sub-type or non-malignancy (B-NHL and acute leukemias, ten classes at time of writing), directly from the data without any human preprocessing. Transfer learning was then used to fine-tune the model to a particular protocol for which the model could be used in routine. Besides sub-typing recommendations, users need to report cell population frequencies, traditionally measured using manual ‘gating’. We trained separate supervised ML models to classify single cells based on a combination of curated and refined routine gating data, expanded with in-house annotations produced by a highly trained cytometrist. To enable clinicians to receive the diagnostic recommendations and produce reports for the treating doctor, we built a CE-IVD web-application (“hema.to”) in which selected users could upload cytometry data, inspect the raw data including abnormalities in the immunophenotype, and audit the ML recommendations against the WHO criteria. The user feedback from each case was recorded, from which the consistency of the ML recommendations with expert opinion could be compared. Before the product was integrated in the clinical routine, a ‘dry run’ retrospective two-arm, four-center blinded clinical study using 96 randomly selected non-overlapping B-NHL cases revealed that analysis time was reduced by &amp;gt;2x and accuracy saw a slight (non-significant) increase against a gold standard as compared to the control arm with a traditional workflow. Results: A worst-case sub-typing performance was computed by testing the model against historic data without expert supervision, and found an f1 score of &amp;gt;90% and sensitivity of 96%. The ML models for acute leukemias specifically show an f1 score of ≈90%, a false positive rate of ≈1.5% and false negative rate of 2-6%. These quality metrics demonstrate that the system can be used for both screening and sub-typing. We therefore integrated hema.to into the routine for B-NHL diagnosis at Result Laboratorium (Dordrecht) and the HpH (Hamburg) since January, for the latter including a deep integration into the laboratory database with automated reporting of expressed CD markers. To date, over a thousand routine cases have been analyzed using the system. We then measured the relationship between predicted sub-typing confidence of the model and agreement with the final expert judgment. The data showed that, on average, confidence≈accuracy ( R2=0.94), indicating that the model is neither over- nor underconfident. As such, the model is a “trustworthy” classifier. Expert agreement over time revealed that the quality of the diagnostic recommendations has been stable over time despite a change of device type and was consistently in excess of 90% across various metrics, including a 100% specificity during the four most recent recorded months. Conclusions: We've shown that machine learning can offer decision support and automated reporting for screening and classifying blood cancers from flow cytometry in a real-world routine setting. Our next focus is to expand the number of indications and increase the model sensitivity to small pathological populations, including measurable residual disease. These results represent major strides towards decision support software for any lab and all hematological neoplasms. Such a software will not only speed up and simplify diagnostic workflows, but also improve the quality of the analysis.

The Methylome Atlas of Acute Leukemia Enables Novel Clinical Diagnostic Tool for AML

In 2022, both the World Health Organization (WHO) and the European LeukemiaNet (ELN) highlighted the formidable challenges of categorizing certain Acute Leukemia subtypes, such as AML with rare recurring translocations, complex karyotypes, not otherwise specified, and myelodysplastic-like entities. As characterizations of new variants and fusion partners become more prevalent, it is difficult to assess which ones, alone or in combination, generate which leukemia phenotypes. To that end, WHO posed methylation profiling as “the technology at present best suited for addressing lineage and thereby the cell population of origin of tumors” (PMID: 34921008), which invites an opportunity to evaluate the DNA methylome as a clinical diagnostic avenue in hematopoietic malignancies like AML. Thus, we hypothesize that the DNA methylome holds promise in further refining AML classification. In this study we describe the assembly of the largest publicly available methylation dataset of acute leukemias so far, which combines 11 high-quality clinical trials/studies: NOPHO ALL92-2000 (n=796), AAML0531 (n=628), AAML1031 (n=581), BeatAML (n=316), TCGA AML (n=194), French GRAALL 2003-2005 (n=153), TARGET ALL (n=131), CETLAM SMD-09 (n=83), AAML03P1 (n=72), Japanese AML05 (n=64), and CCG2961 (n=41), resulting in a total of 3,059 subjects after preprocessing. Samples were obtained either from bone marrow or peripheral blood, with DNA methylation (meDNA) data procured using the Illumina methylation array 450k or EPIC array, which share 452,453 probes with same chemistry and design. To independently validate the findings derived from the discovery cohort, we processed in parallel meDNA array data from bone marrow specimens at diagnosis of AML patients treated on the multi-center clinical trials AML02 (n=159) and AML08 (n=42) led by St. Jude Children's Research Hospital. Processing the raw data followed best practices from the literature using SeSAMe (PMID: 30085201, 27924034). To generate the atlas, we used a novel dimensionality reduction unsupervised learning algorithm called Pairwise Controlled Manifold Approximation (PaCMAP), which allowed compression of 319,738 processed CpG values into two components for visualization (Figure 1) and five components for downstream classification analysis. To empirically assess classification accuracy of PaCMAP results, we implemented a machine learning pipeline with hyperparameter tuning, 10-fold cross validation, and assessed accuracy per class in discovery and validation cohorts. Not all samples, however, had clinical diagnostic annotation available in the dataset, so only those with the annotations were used in the supervised machine learning model (n=1399 in discovery and n=110 in validation). Our resulting atlas unveils several clusters of samples that defined 6 hematopoietic lineages: AML, ALL, MDS-related or secondary myeloid neoplasms (MDS-like), Acute promyelocytic leukemia (APL), mixed phenotype leukemia, and otherwise-normal control. These are further subdivided into 30 subclasses overlapping with WHO 2022 and ELN 2022 clinical diagnostic annotations. Subsequent analyses comparing methylation-based subtype prediction to clinically annotated subtypes revealed an overall concordance score of 0.936, with per class 10-fold CV concordance ranging from 0.82 for MDS-like to 1.00 in APL and NUP214 fusions. Importantly, the validation (test) cohort showed accuracies per class of 0.91 for AML with KMT2A-r (n=47) and 1.00 for AML rare recurring translocations (n=10). These are subtypes with large genomic heterogeneity that may be better represented at the epigenomic level. Additionally, these are largely considered standard to high-risk groups with poor prognosis, which invites further studies aiming at uncovering the biological mechanisms behind the methylation patterns governing this classification. Finally, the resulting classifier allowed prediction of WHO/ELN clinical diagnosis for 91 samples in the validation cohort that were previously categorized as “Normal Karyotype”, “Other” or blank. In conclusion, our study effectively showcases the use of a methylome atlas in enhancing the diagnosis of AML subtypes.

Establishing Quantifiable Guidelines for Antimicrobial α/β-Peptide Design: A Partial Least-Squares Approach to Improve Antimicrobial Activity and Reduce Mammalian Cell Toxicity.

Antimicrobial peptides (AMPs) are promising candidates to combat pathogens that are resistant to conventional antimicrobial drugs because they operate through mechanisms that involve membrane disruption. However, the use of AMPs in clinical settings has been limited, at least in part, by their susceptibility to proteolytic degradation and their lack of selectivity toward pathogenic microbes vs mammalian cells. We recently reported on the design of α- and β-peptide oligomers structurally templated upon the naturally occurring α-helical AMP aurein 1.2. These α/β-peptide oligomers are more proteolytically stable than aurein 1.2 and have several other attributes that render them attractive as alternatives to conventional AMPs. This study describes the influence of peptide physicochemical properties on the broad-spectrum activity of aurein 1.2-based α/β-peptide mimics against nine bacterial, fungal, and mammalian cell lines. We used a partial least-squares regression (PLSR)-supervised machine learning model to quantify and visualize relationships between experimentally determined physicochemical properties (e.g., hydrophobicity, charge, and helicity) and experimentally measured cell-type-specific activities of 21 peptides in a 149-member α/β-peptide library. Using this approach, we identified several peptides that were predicted to exhibit enhanced broad-spectrum selectivity, a measure that evaluates antimicrobial activity relative to mammalian cell toxicity compared to aurein 1.2. Experimental validation demonstrated high model predictive performance, and characterization of compounds with the highest broad-spectrum selectivity revealed peptide hydrophobicity, helicity, and helical rigidity to be strong predictors of broad-spectrum selectivity. The most selective peptide identified from the model prediction has more than a 13-fold improvement in broad-spectrum selectivity than that of aurein 1.2, demonstrating the ability of using PLSR models to identify quantitative structure-function relationships for nonstandard amino acid-containing peptides. Overall, this work establishes quantifiable guidelines for the rational design of helical antimicrobial α/β-peptides and identifies promising new α/β-peptides with significantly reduced mammalian toxicities and improved antifungal and antibacterial activities relative to aurein 1.2.

A Decade of Tweets: Visualizing Racial Sentiments Towards Minoritized Groups in the United States Between 2011 and 2021.

Research has demonstrated the negative impact of racism on health, yet the measurement of racial sentiment remains challenging. This article provides practical guidance on using social media data for measuring public sentiment. We describe the main steps of such research, including data collection, data cleaning, binary sentiment analysis, and visualization of findings. We randomly sampled 55,844,310 publicly available tweets from 1 January 2011 to 31 December 2021 using Twitter's Application Programming Interface. We restricted analyses to US tweets in English using one or more 90 race-related keywords. We used a Support Vector Machine, a supervised machine learning model, for sentiment analysis. The proportion of tweets referencing racially minoritized groups that were negative increased at the county, state, and national levels, with a 16.5% increase at the national level from 2011 to 2021. Tweets referencing Black and Middle Eastern people consistently had the highest proportion of negative sentiment compared with all other groups. Stratifying temporal trends by racial and ethnic groups revealed unique patterns reflecting historical events specific to each group, such as the killing of George Floyd regarding sentiment of posts referencing Black people, discussions of the border crisis near the 2018 midterm elections and anti-Latinx sentiment, and the emergence of COVID-19 and anti-Asian sentiment. This study demonstrates the utility of social media data as a quantitative means to measure racial sentiment over time and place. This approach can be extended to a range of public health topics to investigate how changes in social and cultural norms impact behaviors and policy.A supplemental digital video is available at http://links.lww.com/EDE/C91.

Analyzing the Interactions between Environmental Parameters and Cardiovascular Diseases Using Random Forest and SHAP Algorithms.

Cardiovascular diseases (CVD) remain the predominant global cause of mortality, with both low and high temperatures increasing CVD-related mortalities. Climate change impacts human health directly through temperature fluctuations and indirectly via factors like disease vectors. Elevated and reduced temperatures have been linked to increases in CVD-related hospitalizations and mortality, with various studies worldwide confirming the significant health implications of temperature variations and air pollution on cardiovascular outcomes. A database of daily Emergency Room admissions at the Giovanni XIII Polyclinic in Bari (Southern Italy) was developed, spanning from 2013 to 2019, including weather and air quality data. A Random Forest (RF) supervised machine learning model was used to simulate the trend of hospital admissions for CVD. The Seasonal and Trend decomposition using Loess (STL) decomposition model separated the trend component, while cross-validation techniques were employed to prevent overfitting. Model performance was assessed using specific metrics and error analysis. Additionally, the SHapley Additive exPlanations (SHAP) method, a feature importance technique within the eXplainable Artificial Intelligence (XAI) framework, was used to identify the feature importance. An of 0.97 and a Mean Absolute Error of 0.36 admissions were achieved by the model. Atmospheric pressure, minimum temperature, and carbon monoxide were found to collectively contribute about 74% to the model's predictive power, with atmospheric pressure being the dominant factor at 37%. This research underscores the significant influence of weather-climate variables on cardiovascular diseases. The identified key climate factors provide a practical framework for policymakers and healthcare professionals to mitigate the adverse effects of climate change on CVD and devise preventive strategies.

A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models

One of the most important sources of energy is the sun. Taiwan is located at a 22–25° north latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location can obtain sustainable and stable solar resources. This study uses research on solar radiation forecasts to maximize the benefits of solar power generation, and it develops methods that can predict future solar radiation patterns to help reduce the costs of solar power generation. This study built supervised machine learning models, known as a deep neural network (DNN) and a long–short-term memory neural network (LSTM). A hybrid supervised and unsupervised model, namely a cluster-based artificial neural network (k-means clustering- and fuzzy C-means clustering-based models) was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time-updated solar radiation forecast system capable of predicting the next 12 h. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models offer advantages and disadvantages in predicting different future times. The hybrid prediction system can predict future solar radiation more accurately than a single model.