Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.

Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage.

Agricultural droughts are a threat to local economies, as they disrupt crops. The monitoring of agricultural droughts is of practical significance for mitigating loss. Even though satellite data have been extensively used in agricultural studies, realizing wide-range, high-resolution, and high-precision agricultural drought monitoring is still difficult. This study combined the high spatial resolution of unmanned aerial vehicle (UAV) remote sensing with the wide-range monitoring capability of Landsat-8 and employed the local average method for upscaling to match the remote sensing images of the UAVs with satellite images. Based on the measured ground data, this study employed two machine learning algorithms, namely, random forest (RF) and eXtreme Gradient Boosting (XGBoost1.5.1), to establish the inversion models for the relative soil moisture. The results showed that the XGBoost model achieved a higher accuracy for different soil depths. For a soil depth of 0-20 cm, the XGBoost model achieved the optimal result (R2 = 0.6863; root mean square error (RMSE) = 3.882%). Compared with the corresponding model for soil depth before the upscaling correction, the UAV correction can significantly improve the inversion accuracy of the relative soil moisture according to satellite remote sensing. To conclude, a map of the agricultural drought grade of winter wheat in the Huaibei Plain in China was drawn up.

Criticality of Nursing Care for Patients With Alzheimer's Disease in the ICU: Insights From MIMIC III Dataset.

Alzheimer's disease (AD) patients admitted to intensive care units (ICUs) exhibit varying survival outcomes due to the unique challenges in managing AD patients. Stratifying patient mortality risk and understanding the criticality of nursing care are important to improve the clinical outcomes of AD patients. This study aimed to leverage machine learning (ML) and electronic health records (EHRs) only consisting of demographics, disease history, and routine lab tests, with a focus on nursing care, to facilitate the optimization of nursing practices for AD patients. We utilized Medical Information Mart for Intensive Care III, an open-source EHR dataset, and AD patients were identified based on the International Classification of Diseases, Ninth Revision codes. From a cohort of 453 patients, a total of 60 features, encompassing demographics, laboratory tests, disease history, and number of nursing events, were extracted. ML models, including XGBoost, random forest, logistic regression, and multi-layer perceptron, were trained to predict the 30-day mortality risk. In addition, the influence of nursing care was analyzed in terms of feature importance using values calculated from both the inherent XGBoost module and the SHapley Additive exPlanations (SHAP) library. XGBoost emerged as the lead model with a high accuracy of 0.730, area under the curve (AUC) of 0.750, sensitivity of 0.688, and specificity of 0.740. Feature importance analyses using inherent XGBoost module or SHAP both indicated the number of nursing care within 14 days post-admission as an important denominator for 30-day mortality risk. When nursing care events were excluded as a feature, stratifying patient mortality risk was also possible but the model's AUC of receiver operating characteristic curve was reduced to 0.68. Nursing care plays a pivotal role in the survival outcomes of AD patients in ICUs. ML models can be effectively employed to predict mortality risks and underscore the importance of specific features, including nursing care, in patient outcomes. Early identification of high-risk AD patients can aid in prioritizing intensive nursing care, potentially improving survival rates.

Identifying Psychosocial and Ecological Determinants of Enthusiasm In Youth: Integrative Cross-Sectional Analysis Using Machine Learning.

BACKGROUND: Understanding the factors contributing to mental well-being in youth is a public health priority. Self-reported enthusiasm for the future may be a useful indicator of well-being and has been shown to forecast social and educational success. Typically, cross-domain measures of ecological and health-related factors with relevance to public policy and programming are analyzed either in isolation or in targeted models assessing bivariate interactions. Here, we capitalize on a large provincial data set and machine learning to identify the sociodemographic, experiential, behavioral, and other health-related factors most strongly associated with levels of subjective enthusiasm for the future in a large sample of elementary and secondary school students. OBJECTIVE: The aim of this study was to identify the sociodemographic, experiential, behavioral, and other health-related factors associated with enthusiasm for the future in elementary and secondary school students using machine learning. METHODS: We analyzed data from 13,661 participants in the 2019 Ontario Student Drug Use and Health Survey (OSDUHS) (grades 7-12) with complete data for our primary outcome: self-reported levels of enthusiasm for the future. We used 50 variables as model predictors, including demographics, perception of school experience (i.e., school connectedness and academic performance), physical activity and quantity of sleep, substance use, and physical and mental health indicators. Models were built using a nonlinear decision tree-based machine learning algorithm called extreme gradient boosting to classify students as indicating either high or low levels of enthusiasm. Shapley additive explanations (SHAP) values were used to interpret the generated models, providing a ranking of feature importance and revealing any nonlinear or interactive effects of the input variables. RESULTS: The top 3 contributors to higher self-rated enthusiasm for the future were higher self-rated physical health (SHAP value=0.62), feeling that one is able to discuss problems or feelings with their parents (SHAP value=0.49), and school belonging (SHAP value=0.32). Additionally, subjective social status at school was a top feature and showed nonlinear effects, with benefits to predicted enthusiasm present in the mid-to-high range of values. CONCLUSIONS: Using machine learning, we identified key factors related to self-reported enthusiasm for the future in a large sample of young students: perceived physical health, subjective school social status and connectedness, and quality of relationship with parents. A focus on perceptions of physical health and school connectedness should be considered central to improving the well-being of youth at the population level.

Performance analysis of machine learning models for AQI prediction in Gorakhpur City: a critical study.

Air pollution and climate change are two complementary forces that directly or indirectly affect the environment's physical, chemical, and biological processes. The air quality index is a parameter defined to cope with this effect of air pollution. This study delves deeper into predicting this AQI parameter using multiple machine learning-based models. The AQI pollutants considered for this study are particulate matter (PM10, PM2.5), SO2, and NO2. It also tries to develop a comparative analysis of two different machine learning (ML) models viz. a viz. XGBoost and Lasso regression. An ever-changing emission concentration of pollutants is displayed by this study conducted in the urban city of Gorakhpur Uttar Pradesh, India. The validation of prediction accuracies of models was done over several statistical metrics. The value of the R2 metric for XGBoost (0.9985) is comparatively more than the R2 value for Lasso regression (0.9218) indicating lesser variance and higher accuracy of XGBoost in predicting AQI. Various statistical measures are taken into consideration in this study, including mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), T-test and p-values, and confidence intervals (CI). An increased degree of model accuracy is suggested as XGBoost's MAE, MSE, and RMSE values are significantly lower than Lasso's. Statistically significant performance differences between the XGBoost and Lasso regression models are demonstrated by T-statistics and p-values for MAE, MSE, RMSE, and R2.

CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations.

Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPred method, utilizing the top 10 features and XGBoost, achieved an area under the receiver operating characteristic curve (AUC) value of 0.83 and 0.80 on the training and independent test sets, respectively. Furthermore, CDMPred demonstrated superior performance compared to existing state-of-the-art methods for cancer-specific and general diseases, as measured by AUC and area under the precision-recall curve. Including high-quality passenger mutations in the training data proves advantageous for CDMPred's prediction performance. We anticipate that CDMPred will be a valuable tool for predicting cancer driver mutations, furthering our understanding of personalized therapy.

Long-term contributions of VOC sources and their link to ozone pollution in Bronx, New York City.

Changes in energy and environmental policies along with changes in the energy markets of New York State over the past two decades, have spurred interest in evaluating their impacts on emissions from various energy generation sectors. This study focused on quantifying these effects on VOC (volatile organic compounds) emissions and their subsequent impacts on air quality within the New York City (NYC) metropolitan area. NYC is an EPA nonattainment region for ozone (O3) and likely is a VOC limited region. NYC has a complex coastal topography and meteorology with low-level jets and sea/bay/land breeze circulation associated with heat waves, leading to summertime O3 exceedances and formation of secondary organic aerosol (SOA). To date, no comprehensive source apportionment studies have been done to understand the contributions of local and long-range sources of VOCs in this area. This study applied an improved Positive Matrix Factorization (PMF) methodology designed to incorporate atmospheric dispersion and photochemical reaction losses of VOCs to provide improved apportionment results. Hourly measurements of VOCs were obtained from a Photochemical Assessment Monitoring Station located at an urban site in the Bronx from 2000 to 2021. The study further explores the role of VOC sources in O3 and SOA formation and leverages advanced machine learning tools, XGBoost and SHAP algorithms, to identify synergistic interactions between sources and provided VOC source impacts on ambient O3 concentrations. Isoprene demonstrated a substantial influence in the source contribution of the biogenic factor, emphasizing its role in O3 formation. Notable contributions from anthropogenic emissions, such as fuel evaporation and various industrial processes, along with significant traffic-related sources that likely contribute to SOA formation, underscore the combined impact of natural and human-made sources on O3 pollution. Findings of this study can assist regulatory agencies in developing appropriate policy and management initiatives to control O3 pollution in NYC.

Machine learning based stellar classification with highly sparse photometry data.

Background: Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying these classes becomes necessary. Methods: Many classes of stars are identified based on their emitted spectra. In this paper, we use a combination of the multi-class multi-label Machine Learning (ML) method XGBoost and the PySSED spectral-energy-distribution fitting algorithm to classify stars into nine different classes, based on their photometric data. The classifier is trained on subsets of the SIMBAD database. Particular challenges are the very high sparsity (large fraction of missing values) of the underlying data as well as the high class imbalance. We discuss the different variables available, such as photometric measurements on the one hand, and indirect predictors such as Galactic position on the other hand. Results: We show the difference in performance when excluding certain variables, and discuss in which contexts which of the variables should be used. Finally, we show that increasing the number of samples of a particular type of star significantly increases the performance of the model for that particular type, while having little to no impact on other types. The accuracy of the main classifier is ∼0.7 with a macro F1 score of 0.61. Conclusions: While the current accuracy of the classifier is not high enough to be reliably used in stellar classification, this work is an initial proof of feasibility for using ML to classify stars based on photometry.

Astronomy is at the forefront of the 'Big Data' regime, with telescopes collecting increasingly large volumes of data. The tools astronomers use to analyse and draw conclusions from these data need to be able to keep up, with machine learning providing many of the solutions. Being able to classify different astronomical objects by type helps to disentangle the astrophysics making them unique, offering new insights into how the Universe works. Here, we present how machine learning can be used to classify different kinds of stars, in order to augment large databases of the sky. This will allow astronomers to more easily extract the data they need to perform their scientific analyses.

Sensitivity analysis of slope stability based on eXtreme gradient boosting and SHapley Additive exPlanations: An exploratory study.

Slope instability through can cause catastrophic consequences, so slope stability analysis has been a key topic in the field of geotechnical engineering. Traditional analysis methods have shortcomings such as high operational difficulty and time-consuming, for this reason many researchers have carried out slope stability analysis based on AI. However, the current relevant studies only judged the importance of each factor and did not specifically quantify the correlation between factors and slope stability. For this purpose, this paper carried out a sensitivity analysis based on the XGBoost and SHAP. The sensitivity analysis results of SHAP were also validated using GeoStudio software. The selected influence factors included slope height ( H ), slope angle ( ß ), unit weight ( γ ), cohesion ( c ), angle of internal friction ( φ ) and pore water pressure coefficient ( r u ). The results showed that c and γ were the most and least important influential parameters, respectively. GeoStudio simulation results showed a negative correlation between γ , ß , H , r u and slope stability, while a positive correlation between c , φ and slope stability. However, for real data, SHAP misjudged the correlation between γ and slope stability. Because current AI lacked common sense knowledge and, leading SHAP unable to effectively explain the real mechanism of slope instability. For this reason, this paper overcame this challenge based on the priori data-driven approach. The method provided more reliable and accurate interpretation of the results than a real sample, especially with limited or low-quality data. In addition, the results of this method showed that the critical values of c , φ , ß , H , and r u in slope destabilization are 18 Kpa, 28°, 32°, 30 m, and 0.28, respectively. These results were closer to GeoStudio simulations than real samples.

A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications.

RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA's operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis.

Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy.

The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models and comparing their prediction performance, XGBoost is finally selected as the predictive model for creep rupture life. Moreover, we introduce an interpretable method, Shapley additive explanations (SHAP), to explain the creep rupture life predicted by the XGBoost model. The SHAP values are then calculated, and the feature importance of the creep rupture life yielded by the XGBoost model is discussed. Finally, the creep fracture life is optimized by using the chaotic sparrow optimization algorithm. We then show that our proposed method can accurately predict and optimize creep properties in a cheaper and faster way than other approaches in the experiments. The proposed method can also be used to optimize the material design across various engineering domains.

Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy.

Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in agriculture. However, directly extracting constituent information from the NIR spectra is challenging due to many issues such as broad absorption band, overlapping and non-specific nature. To solve these problems and extract implicit features from the raw data of NIR spectra to improve performance of quantitative models, a one-dimensional shallow convolutional neural network (CNN) model based on an eXtreme Gradient Boosting (XGBoost) feature extraction method was proposed in this paper. The leaf node feature information in the XGBoost was encoded and reconstructed to obtain the implicit features of raw data in the NIR spectra. A two-parametric Swish (TSwish or TS) activation function was proposed to improve the performance of CNN, and the elastic net (EN) was also applied to avoid the overfitting problem of the CNN model. Performance of the developed XGBoost-CNN-TS-EN model was evaluated using two public NIR spectroscopy datasets of corn and soil, and the obtained determination coefficients (R2) for moisture, oil, protein, and starch of the corn on test set were 0.993, 0.991, 0.998, and 0.992, respectively, with that of the soil organic matter being 0.992. The XGBoost-CNN-TS-EN model exhibits superior stability, good prediction accuracy, and generalization ability, demonstrating its great potentials for quantitative analysis of multi-constituents in spectroscopic applications.

A rigorous workflow and comparative analysis for accurate determination of vitrinite reflectance using data-driven approaches in the Persian Gulf region.

Vitrinite reflectance (VR) is a critical measure of source rock maturity in geochemistry. Although VR is a widely accepted measure of maturity, its accurate measurement often proves challenging and costly. Rock-Eval pyrolysis offers the advantages of being cost-effective, fast, and providing accurate data. Previous studies have employed empirical equations and traditional machine learning methods using T-max data for VR prediction, but these approaches often yielded subpar results. Therefore, the quest to develop a precise method for predicting vitrinite reflectance based on Rock-Eval data becomes particularly valuable. This study presents a novel approach to predicting VR using advanced machine learning models, namely ExtraTree and XGBoost, along with new ways to prepare the data, such as winsorization for outlier treatment and principal component analysis (PCA) for dimensionality reduction. The depth and three Rock-Eval parameters (T-max, S1/TOC, and HI) were used as input variables. Three model sets were examined: Set 1, which involved both Winsorization and PCA; Set 2, which only included Winsorization; and Set 3, which did not include either. The results indicate that the ExtraTree model in Set 1 demonstrated the highest level of predictive accuracy, whereas Set 3 exhibited the lowest level of accuracy, confirming the methodology's effectiveness. The ExtraTree model obtained an overall R2 score of 0.997, surpassing traditional methods by a significant margin. This approach improves the accuracy and dependability of virtual reality predictions, showing significant advancements compared to conventional empirical equations and traditional machine learning methods.

Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China.

Extreme meteorological events and rapid urbanization have led to serious urban flooding problems. Characterizing spatial variations in flooding susceptibility and elucidating its driving factors are essential for preventing damages from urban pluvial flooding. However, conventional methods, limited by spatial heterogeneity and the intricate mechanisms of urban flooding, frequently demonstrated a deficiency in precision when assessing flooding susceptibility in dense urban areas. Therefore, this study proposed a novel framework for an integrated assessment of urban flood susceptibility, based on a comprehensive cascade modeling chain consisting of XGBoost, SHapley Additive exPlanations (SHAP), and Partial Dependence Plots (PDP) in combination with K-means. It aimed to recognize the specific influence of urban morphology and the spatial patterns of flooding risk agglomeration under different rainfall scenarios in high-density urban areas. The XGBoost model demonstrated enhanced accuracy and robustness relative to other three benchmark models: RF, SVR, and BPDNN. This superiority was effectively validated during both training and independent testing in Shenzhen. The results indicated that urban 3D morphology characteristics were the dominant factors for waterlogging magnitude, which occupied 46.02 % of relative contribution. Through PDP analysis, multi-staged trends highlighted critical thresholds and interactions between significant indicators like building congestion degree (BCD) and floor area ratio (FAR). Specifically, optimal intervals like BCD between 0 and 0.075 coupled with FAR values between 0.5 and 1 have the potential to substantially mitigate flooding risks. These findings emphasize the need for strategic building configuration within urban planning frameworks. In terms of the spatial-temporal assessment, a significant aggregation effect of high-risk areas that prone to prolonged duration or high-intensity rainfall scenarios emerged in the old urban districts. The approach in the present study provides quantitative insights into waterlogging adaptation strategies for sustainable urban planning and design.

Machine learning-based estimation of evapotranspiration under adaptation conditions: a case study in Heilongjiang Province, China.

The prediction of evapotranspiration (ET0) is crucial for agricultural ecosystems, irrigation management, and environmental climate regulation. Traditional methods for predicting ET0 require a variety of meteorological parameters. However, obtaining data for these multiple parameters can be challenging, leading to inaccuracies or inability to predict ET0 using traditional methods. This affects decision-making in critical applications such as agricultural irrigation scheduling and water management, consequently impacting the development of agricultural ecosystems. This issue is particularly pronounced in economically underdeveloped regions. Therefore, this paper proposes a machine learning-based evapotranspiration estimation method adapted to evapotranspiration conditions. Compared to traditional methods, our approach relies less on the variety of meteorological parameters and yields higher prediction accuracy. Additionally, we introduce a 'region of evapotranspiration adaptability' division method, which takes into account geographical differences in ET0 prediction. This effectively mitigates the negative impact of anomalies or missing data from individual meteorological stations, making our method more suitable for practical agricultural irrigation and ecosystem water resource management. We validated our approach using meteorological data from 25 stations in Heilongjiang, China. Our results indicate that non-adjacent geographical areas, despite different climatic conditions, can have similar impacts on ET0 prediction. In summary, our method facilitates accurate ET0 prediction, offering new insights for the development of agricultural irrigation and ecosystems, and further contributes to agricultural food supply.

A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations.

MicroRNAs (miRNAs) are a key class of endogenous non-coding RNAs that play a pivotal role in regulating diseases. Accurately predicting the intricate relationships between miRNAs and diseases carries profound implications for disease diagnosis, treatment, and prevention. However, these prediction tasks are highly challenging due to the complexity of the underlying relationships. While numerous effective prediction models exist for validating these associations, they often encounter information distortion due to limitations in efficiently retaining information during the encoding-decoding process. Inspired by Multi-layer Heterogeneous Graph Transformer and Machine Learning XGboost classifier algorithm, this study introduces a novel computational approach based on multi-layer heterogeneous encoder-machine learning decoder structure for miRNA-disease association prediction (MHXGMDA). First, we employ the multi-view similarity matrices as the input coding for MHXGMDA. Subsequently, we utilize the multi-layer heterogeneous encoder to capture the embeddings of miRNAs and diseases, aiming to capture the maximum amount of relevant features. Finally, the information from all layers is concatenated to serve as input to the machine learning classifier, ensuring maximal preservation of encoding details. We conducted a comprehensive comparison of seven different classifier models and ultimately selected the XGBoost algorithm as the decoder. This algorithm leverages miRNA embedding features and disease embedding features to decode and predict the association scores between miRNAs and diseases. We applied MHXGMDA to predict human miRNA-disease associations on two benchmark datasets. Experimental findings demonstrate that our approach surpasses several leading methods in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve.

Eco-friendly nanotechnology in rheumatoid arthritis: ANFIS-XGBoost enhanced layered nanomaterials.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by inflammation and pain in the joints, which can lead to joint damage and disability over time. Nanotechnology in RA treatment involves using nano-scale materials to improve drug delivery efficiency, specifically targeting inflamed tissues and minimizing side effects. The study aims to develop and optimize a new class of eco-friendly and highly effective layered nanomaterials for targeted drug delivery in the treatment of RA. The study's primary objective is to develop and optimize a new class of layered nanomaterials that are both eco-friendly and highly effective in the targeted delivery of medications for treating RA. Also, by employing a combination of Adaptive Neuron-Fuzzy Inference System (ANFIS) and Extreme Gradient Boosting (XGBoost) machine learning models, the study aims to precisely control nanomaterials synthesis, structural characteristics, and release mechanisms, ensuring delivery of anti-inflammatory drugs directly to the affected joints with minimal side effects. The in vitro evaluations demonstrated a sustained and controlled drug release, with an Encapsulation Efficiency (EE) of 85% and a Loading Capacity (LC) of 10%. In vivo studies in a murine arthritis model showed a 60% reduction in inflammation markers and a 50% improvement in mobility, with no significant toxicity observed in major organs. The machine learning models exhibited high predictive accuracy with a Root Mean Square Error (RMSE) of 0.667, a correlation coefficient (r) of 0.867, and an R2 value of 0.934. The nanomaterials also demonstrated a specificity rate of 87.443%, effectively targeting inflamed tissues with minimal off-target effects. These findings highlight the potential of this novel approach to significantly enhance RA treatment by improving drug delivery precision and minimizing systemic side effects.

Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification.

Otitis media (OM), a highly prevalent inflammatory middle-ear disease in children worldwide, is commonly caused by an infection, and can lead to antibiotic-resistant bacterial biofilms in recurrent/chronic OM cases. A biofilm related to OM typically contains one or multiple bacterial species. OCT has been used clinically to visualize the presence of bacterial biofilms in the middle ear. This study used OCT to compare microstructural image texture features from bacterial biofilms. The proposed method applied supervised machine-learning-based frameworks (SVM, random forest, and XGBoost) to classify multiple species bacterial biofilms from in vitro cultures and clinically-obtained in vivo images from human subjects. Our findings show that optimized SVM-RBF and XGBoost classifiers achieved more than 95% of AUC, detecting each biofilm class. These results demonstrate the potential for differentiating OM-causing bacterial biofilms through texture analysis of OCT images and a machine-learning framework, offering valuable insights for real-time in vivo characterization of ear infections.

Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center.

Tremor, defined as an "involuntary, rhythmic, oscillatory movement of a body part", is a key feature of many neurological conditions including Parkinson's disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson's disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81-0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.