Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach.

Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.

Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches.

The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional in vivo and in vitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.

A case study on the relationship between risk assessment of scientific research projects and related factors under the Naive Bayesian algorithm.

This paper delves into the nuanced dynamics influencing the outcomes of risk assessment (RA) in scientific research projects (SRPs), employing the Naive Bayes algorithm. The methodology involves the selection of diverse SRPs cases, gathering data encompassing project scale, budget investment, team experience, and other pertinent factors. The paper advances the application of the Naive Bayes algorithm by introducing enhancements, specifically integrating the Tree-augmented Naive Bayes (TANB) model. This augmentation serves to estimate risk probabilities for different research projects, shedding light on the intricate interplay and contributions of various factors to the RA process. The findings underscore the efficacy of the TANB algorithm, demonstrating commendable accuracy (average accuracy 89.2%) in RA for SRPs. Notably, budget investment (regression coefficient: 0.68, P < 0.05) and team experience (regression coefficient: 0.51, P < 0.05) emerge as significant determinants obviously influencing RA outcomes. Conversely, the impact of project size (regression coefficient: 0.31, P < 0.05) is relatively modest. This paper furnishes a concrete reference framework for project managers, facilitating informed decision-making in SRPs. By comprehensively analyzing the influence of various factors on RA, the paper not only contributes empirical insights to project decision-making but also elucidates the intricate relationships between different factors. The research advocates for heightened attention to budget investment and team experience when formulating risk management strategies. This strategic focus is posited to enhance the precision of RAs and the scientific foundation of decision-making processes.

A Fault Diagnosis Method for 5G Cellular Networks Based on Knowledge and Data Fusion.

As 5G networks become more complex and heterogeneous, the difficulty of network operation and maintenance forces mobile operators to find new strategies to stay competitive. However, most existing network fault diagnosis methods rely on manual testing and time stacking, which suffer from long optimization cycles and high resource consumption. Therefore, we herein propose a knowledge- and data-fusion-based fault diagnosis algorithm for 5G cellular networks from the perspective of big data and artificial intelligence. The algorithm uses a generative adversarial network (GAN) to expand the data set collected from real network scenarios to balance the number of samples under different network fault categories. In the process of fault diagnosis, a naive Bayesian model (NBM) combined with domain expert knowledge is firstly used to pre-diagnose the expanded data set and generate a topological association graph between the data with solid engineering significance and interpretability. Then, as the pre-diagnostic prior knowledge, the topological association graph is fed into the graph convolutional neural network (GCN) model simultaneously with the training data set for model training. We use a data set collected by Minimization of Drive Tests under real network scenarios in Lu'an City, Anhui Province, in August 2019. The simulation results demonstrate that the algorithm outperforms other traditional models in fault detection and diagnosis tasks, achieving an accuracy of 90.56% and a macro F1 score of 88.41%.

Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking.

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.

Latent pathway-based Bayesian models to identify intervenable factors of racial disparities in breast cancer stage at diagnosis.

PURPOSE: We built Bayesian Network (BN) models to explain roles of different patient-specific factors affecting racial differences in breast cancer stage at diagnosis, and to identify healthcare related factors that can be intervened to reduce racial health disparities. METHODS: We studied women age 67-74 with initial diagnosis of breast cancer during 2006-2014 in the National Cancer Institute's SEER-Medicare dataset. Our models included four measured variables (tumor grade, hormone receptor status, screening utilization and biopsy delay) expressed through two latent pathways-a tumor biology path, and health-care access/utilization path. We used various Bayesian model assessment tools to evaluate these two latent pathways as well as each of the four measured variables in explaining racial disparities in stage-at-diagnosis. RESULTS: Among 3,010 Black non-Hispanic (NH) and 30,310 White NH breast cancer patients, respectively 70.2% vs 76.9% were initially diagnosed at local stage, 25.3% vs 20.3% with regional stage, and 4.56% vs 2.80% with distant stage-at-diagnosis. Overall, BN performed approximately 4.7 times better than Classification And Regression Tree (CART) (Breiman L, Friedman JH, Stone CJ, Olshen RA. Classification and regression trees. CRC press; 1984) in predicting stage-at-diagnosis. The utilization of screening mammography is the most prominent contributor to the accuracy of the BN model. Hormone receptor (HR) status and tumor grade are useful for explaining racial disparity in stage-at diagnosis, while log-delay in biopsy impeded good prediction. CONCLUSIONS: Mammography utilization had a significant effect on racial differences in breast cancer stage-at-diagnosis, while tumor biology factors had less impact. Biopsy delay also aided in predicting local and regional stages-at-diagnosis for Black NH women but not for white NH women.

A Bayesian latent class extension of naive Bayesian classifier and its application to the classification of gastric cancer patients.

BACKGROUND: The Naive Bayes (NB) classifier is a powerful supervised algorithm widely used in Machine Learning (ML). However, its effectiveness relies on a strict assumption of conditional independence, which is often violated in real-world scenarios. To address this limitation, various studies have explored extensions of NB that tackle the issue of non-conditional independence in the data. These approaches can be broadly categorized into two main categories: feature selection and structure expansion. In this particular study, we propose a novel approach to enhancing NB by introducing a latent variable as the parent of the attributes. We define this latent variable using a flexible technique called Bayesian Latent Class Analysis (BLCA). As a result, our final model combines the strengths of NB and BLCA, giving rise to what we refer to as NB-BLCA. By incorporating the latent variable, we aim to capture complex dependencies among the attributes and improve the overall performance of the classifier. METHODS: Both Expectation-Maximization (EM) algorithm and the Gibbs sampling approach were offered for parameter learning. A simulation study was conducted to evaluate the classification of the model in comparison with the ordinary NB model. In addition, real-world data related to 976 Gastric Cancer (GC) and 1189 Non-ulcer dyspepsia (NUD) patients was used to show the model's performance in an actual application. The validity of models was evaluated using the 10-fold cross-validation. RESULTS: The presented model was superior to ordinary NB in all the simulation scenarios according to higher classification sensitivity and specificity in test data. The NB-BLCA model using Gibbs sampling accuracy was 87.77 (95% CI: 84.87-90.29). This index was estimated at 77.22 (95% CI: 73.64-80.53) and 74.71 (95% CI: 71.02-78.15) for the NB-BLCA model using the EM algorithm and ordinary NB classifier, respectively. CONCLUSIONS: When considering the modification of the NB classifier, incorporating a latent component into the model offers numerous advantages, particularly within medical and health-related contexts. By doing so, the researchers can bypass the extensive search algorithm and structure learning required in the local learning and structure extension approach. The inclusion of latent class variables allows for the integration of all attributes during model construction. Consequently, the NB-BLCA model serves as a suitable alternative to conventional NB classifiers when the assumption of independence is violated, especially in domains pertaining to health and medicine.

Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models.

Purpose: The objective of this study was to employ machine learning (ML) models utilizing non-invasive factors to achieve early and low-cost identification of MetS in a large physical examination population. Patients and Methods: The study enrolled 9171 participants who underwent physical examinations at Northern Jiangsu People's Hospital in 2009 and 2019, to determine MetS based on criteria established by the Chinese Diabetes Society. Non-invasive characteristics such as gender, age, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected and used as input variables to train and evaluate ML models for MetS identification. Several ML models were used for MetS identification, including logistic regression (LR), k-nearest neighbors algorithm (k-NN), naive bayesian (NB), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). Results: Our ML models all showed good performance in the 10-fold cross-validation except for the SVM model. In the external validation, the NB model exhibited the best performance with an AUC of 0.976, accuracy of 0.923, sensitivity of 98.32%, and specificity of 91.32%. Conclusion: This study proposed a new non-invasive method for early and low-cost identification of MetS by using ML models. This approach has the potential to serve as a highly sensitive, convenient, and cost-effective tool for large-scale MetS screening.

Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.

BACKGROUND: Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of breast cancer. PURPOSE: In this review, we examine studies that have used these techniques for breast cancer classification and diagnosis, focusing on five groups of medical images: mammography, ultrasound, MRI, histology, and thermography. We discuss the use of five popular machine learning techniques, including Nearest Neighbor, SVM, Naive Bayesian Network, DT, and ANN, as well as deep learning architectures and convolutional neural networks. CONCLUSION: Our review finds that machine learning and deep learning techniques have achieved high accuracy rates in breast cancer classification and diagnosis across various medical imaging modalities. Furthermore, these techniques have the potential to improve clinical decision-making and ultimately lead to better patient outcomes.

Assessing urban flooding risk in response to climate change and urbanization based on shared socio-economic pathways.

Global climate change and rapid urbanization, mainly driven by anthropogenic activities, lead to urban flood vulnerability and uncertainty in sustainable stormwater management. This study projected the temporal and spatial variation in urban flood susceptibility during the period 2020-2050 on the basis of shared socioeconomic pathways (SSPs). A case study in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was conducted for verifying the feasibility and applicability of this approach. GBA is predicted to encounter the increase in extreme precipitation with high intensity and frequency, along with rapid expansion of constructed areas, resulting in exacerbating of urban flood susceptibility. The areas with medium and high flood susceptibility will be expected to increase continuously from 2020 to 2050, by 9.5 %, 12.0 %, and 14.4 % under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. In terms of the assessment of spatial-temporal flooding pattern, the areas with high flood susceptibility are overlapped with that in the populated urban center in GBA, surrounding the existing risk areas, which is consistent with the tendency of construction land expansion. The approach in the present study will provide comprehensive insights into the reliable and accurate assessment of urban flooding susceptibility in response to climate change and urbanization.

Ensemble Model for Diagnostic Classification of Alzheimer's Disease Based on Brain Anatomical Magnetic Resonance Imaging.

Alzheimer's is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about the brain's anatomy and enables the identification of diagnostic features. Artificial intelligence (AI) in neuroimaging has the potential to significantly enhance the treatment process for Alzheimer's disease (AD). The objective of this study is two-fold: (1) to compare existing Machine Learning (ML) algorithms for the classification of AD. (2) To propose an effective ensemble-based model for the same and to perform its comparative analysis. In this study, data from the Alzheimer's Diseases Neuroimaging Initiative (ADNI), an online repository, is utilized for experimentation consisting of 2125 neuroimages of Alzheimer's disease (n = 975), mild cognitive impairment (n = 538) and cognitive normal (n = 612). For classification, the framework incorporates a Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN) followed by some variations of Support Vector Machine (SVM), such as SVM (RBF kernel), SVM (Polynomial Kernel), and SVM (Sigmoid kernel), as well as Gradient Boost (GB), Extreme Gradient Boosting (XGB) and Multi-layer Perceptron Neural Network (MLP-NN). Afterwards, an Ensemble Based Generic Kernel is presented where Master-Slave architecture is combined to attain better performance. The proposed model is an ensemble of Extreme Gradient Boosting, Decision Tree and SVM_Polynomial kernel (XGB + DT + SVM). At last, the proposed method is evaluated using cross-validation using statistical techniques along with other ML models. The presented ensemble model (XGB + DT + SVM) outperformed existing state-of-the-art algorithms with an accuracy of 89.77%. The efficiency of all the models was optimized using Grid-based tuning, and the results obtained after such process showed significant improvement. XGB + DT + SVM with optimized parameters outperformed all other models with an efficiency of 95.75%. The implication of the proposed ensemble-based learning approach clearly shows the best results compared to other ML models. This experimental comparative analysis improved understanding of the above-defined methods and enhanced their scope and significance in the early detection of Alzheimer's disease.

Developing a Naïve Bayesian Classification Model with PI3Kγ structural features for virtual screening against PI3Kγ: Combining molecular docking and pharmacophore based on multiple PI3Kγ conformations.

Phosphatidylinositol 3-kinase gamma (PI3Kγ) plays a critical role in immune signaling, thus identifying PI3Kγ as a potential therapeutic target. However, developing selective PI3Kγ inhibitors is hampered by the highly conserved structure of the ATP-binding pocket. Focused effort would be needed to improve upon the γ-subtype selectivity of the inhibitors; therefore, in the present study, a naïve Bayesian classification (NBC) model with PI3Kγ structural features that integrates molecular docking and pharmacophore based on multiple PI3Kγ conformations was developed for virtual screening against PI3Kγ to find novel selective PI3Kγ inhibitors. First, the active PI3Kγ inhibitors/decoy dataset was used to prove whether molecular docking or pharmacophore, integrating multiple PI3Kγ conformations always has higher prediction accuracy than that of any single conformation. Second, both internal cross-validation and external prediction revealed that the NBC model combining molecular docking and pharmacophore could significantly improve the enrichment of active PI3Kγ inhibitors. Then, an analog dataset based on JN-PK1 (a reference compound) was constructed and submitted to virtual screening using the optimal NBC model. Finally, a novel inhibitor with higher PI3Kγ inhibitory activity than JN-PK1 was identified through a series of biological assays, showing both good accuracy and significant reliability of the NBC model with the PI3Kγ structural features. We hope that the developed virtual screening strategy will provide valuable guidance for the discovery of novel selective PI3Kγ inhibitors.

Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia.

Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. About 2327 (34.2%) children were stunted. Thirteen of fifty-eight features were selected for inclusion in the model using random forest. Calibrating the predicted probabilities improved the performance of machine learning algorithms when evaluated using calibration curves. RF was the most accurate algorithm, with an accuracy score of 79% in the testing and 61.6% in the training data while Naïve Bayesian was the worst performing algorithm for predicting stunting among children under five in Zambia using the 2018 ZDHS dataset. ML models aids quick diagnosis of stunting and the timely development of interventions aimed at preventing stunting.

Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems.

Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into a single classification framework has been instrumental in achieving greater accuracy and performing automated big data analysis. The article proposes a bit fusion ensemble algorithm that minimizes the classification error rate and has been tested on various datasets. Five diversified base classifiers k- nearest neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree (D.T.), and Naïve Bayesian Classifier (N.B.), are used in the implementation model. Bit fusion algorithm works on the individual input from the classifiers. Decision vectors of the base classifier are weighted transformed into binary bits by comparing with high-reliability threshold parameters. The output of each base classifier is considered as soft class vectors (CV). These vectors are weighted, transformed and compared with a high threshold value of initialized δ = 0.9 for reliability. Binary patterns are extracted, and the model is trained and tested again. The standard fusion approach and proposed bit fusion algorithm have been compared by average error rate. The error rate of the Bit-fusion algorithm has been observed with the values 5.97, 12.6, 4.64, 0, 0, 27.28 for Leukemia, Breast cancer, Lung Cancer, Hepatitis, Lymphoma, Embryonal Tumors, respectively. The model is trained and tested over datasets from UCI, UEA, and UCR repositories as well which also have shown reduction in the error rates.

Aeration strategy based on numerical modelling and the response mechanism of microbial communities under various operating conditions.

Aeration system is the main energy consumer in a wastewater treatment process. In this paper, the Naive Bayes classification (NBC) algorithm and response surface method (RSM) were firstly used to establish a methodology to improve the aeration efficiency and estimate effluent quality. Lab-scale experiments were conducted to verify the model. The errors between experimental values and predicted values were 3.36, -0.67 and -3.78% at operating temperatures of 20, 30 and 35 °C, indicating the applicability. To further elucidate the biological mechanisms of the experimental results, the microbial community composition was investigated under various operating conditions, the results shows that aerobic heterotrophic bacteria (HET) activity and COD removal efficiency were promoted at 30 °C. AOB and NOB activity and NH4+-N removal efficiency were promoted at 30-35 °C. These findings together suggest that operating temperature is crucial for activated sludge treatment, which should be considered when regulating DO content or aeration rate in practical application.

Discovery of novel selective PI3Kγ inhibitors through combining machine learning-based virtual screening with multiple protein structures and bio-evaluation.

Introduction: Phosphoinositide 3-kinase gamma (PI3Kγ) has been regarded as a promising drug target for the treatment of various diseases, and the diverse physiological roles of class I PI3K isoforms (α, ß, δ, and γ) highlight the importance of isoform selectivity in the development of PI3Kγ inhibitors. However, the high structural conservation among the PI3K family makes it a big challenge to develop selective PI3Kγ inhibitors. Objectives: A novel machine learning-based virtual screening with multiple PI3Kγ protein structures was developed to discover novel PI3Kγ inhibitors. Methods: A large chemical database was screened using the virtual screening model, the top-ranked compounds were then subjected to a series of bio-evaluations, which led to the discovery of JN-KI3. The selective inhibition mechanism of JN-KI3 against PI3Kγ was uncovered by a theoretical study. Results: 49 hits were identified through virtual screening, and the cell-free enzymatic studies found that JN-KI3 selectively inhibited PI3Kγ at a concentration as low as 3,873 nM but had no inhibitory effect on Class IA PI3Ks, leading to the selective cytotoxicity on hematologic cancer cells. Meanwhile, JN-KI3 potently blocked the PI3K signaling, finally led to distinct apoptosis of hematologic cell lines at a low concentration. Lastly, the key residues of PI3Kγ and the structural characteristics of JN-KI3, which both would influence γ isoform-selective inhibition, were highlighted by systematic theoretical studies. Conclusion: The developed virtual screening model strongly manifests the robustness to find novel PI3Kγ inhibitors. JN-KI3 displays a specific cytotoxicity on hematologic tumor cells, and significantly promotes apoptosis associated with the inhibition of the PI3K signaling, which depicts PI3Kγ as a potential target for the hematologic tumor therapy. The theoretical results reveal that those key residues interacting with JN-KI3 are less common compared to most of the reported PI3Kγ inhibitors, indicating that JN-KI3 has novel structural characteristics as a selective PIK3γ inhibitor.

Discovery of novel MIF inhibitors that attenuate microglial inflammatory activation by structures-based virtual screening and in vitro bioassays.

Macrophage migration inhibitory factor (MIF) is a pluripotent pro-inflammatory cytokine and is related to acute and chronic inflammatory responses, immune disorders, tumors, and other diseases. In this study, an integrated virtual screening strategy and bioassays were used to search for potent MIF inhibitors. Twelve compounds with better bioactivity than the prototypical MIF-inhibitor ISO-1 (IC50 = 14.41 µM) were identified by an in vitro enzymatic activity assay. Structural analysis revealed that these inhibitors have novel structural scaffolds. Compound 11 was then chosen for further characterization in vitro, and it exhibited marked anti-inflammatory efficacy in LPS-activated BV-2 microglial cells by suppressing the activation of nuclear factor kappa B (NF-κB) and mitogen-activated protein kinases (MAPKs). Our findings suggest that MIF may be involved in the regulation of microglial inflammatory activation and that small-molecule MIF inhibitors may serve as promising therapeutic agents for neuroinflammatory diseases.

Naïve Bayesian network-based contribution analysis of tumor biology and healthcare factors to racial disparity in breast cancer stage-at-diagnosis.

BACKGROUND: Variation in breast cancer stage at initial diagnosis (including racial disparities) is driven both by tumor biology and healthcare factors. METHODS: We studied women age 67-74 with initial diagnosis of breast cancer from 2006 through 2014 in the SEER-Medicare database. We extracted variables related to tumor biology (histologic grade and hormone receptor status) and healthcare factors (screening mammography [SM] utilization and time delay from mammography to diagnostic biopsy). We used naïve Bayesian networks (NBNs) to illustrate the relationships among patient-specific factors and stage-at-diagnosis for African American (AA) and white patients separately. After identifying and controlling confounders, we conducted counterfactual inference through the NBN, resulting in an unbiased evaluation of the causal effects of individual factors on the expected utility of stage-at-diagnosis. An NBN-based decomposition mechanism was developed to evaluate the contributions of each patient-specific factor to an actual racial disparity in stage-at-diagnosis. 2000 bootstrap samples from our training patients were used to compute the 95% confidence intervals (CIs) of these contributions. RESULTS: Using a causal-effect contribution analysis, the relative contributions of each patient-specific factor to the actual racial disparity in stage-at-diagnosis were as follows: tumor grade, 45.1% (95% CI: 44.5%, 45.8%); hormone receptor status, 5.0% (4.5%, 5.4%); mammography utilization, 23.1% (22.4%, 24.0%); and biopsy delay 26.8% (26.1%, 27.3%). CONCLUSION: The modifiable mechanisms of mammography utilization and biopsy delay drive about 49.9% of racial difference in stage-at-diagnosis, potentially guiding more targeted interventions to eliminate cancer outcome disparities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-021-00165-5.

Integration of DNA extraction, metabarcoding and an informatics pipeline to underpin a national citizen science honey monitoring scheme.

Worldwide honeybees (Apis mellifera L.) are one of the most widely kept domesticated animals, supporting domestic and commercial livelihoods through the production of honey and wax, as well as in the delivery of pollination services to crops. Quantifying which plant species are foraged upon by honeybees provides insights into their nutritional status as well as patterns of landscape scale habitat utilization. Here we outline a rapid and reproducible methodology for identifying environmental DNA (eDNA) originating principally from pollen grains suspended within honey. The process is based on a DNA extraction incorporating vacuum filtration prior to universal eukaryotic internal transcribed spacer 2 region (ITS2) amplicon generation, sequencing and identification. To provide a pre-cursor to sequence phylotyping, we outline systems for error-corrected processing amplicon sequence variant abundance tables that removes chimeras. This methodology underpins the new UK National Honey Monitoring Scheme.•We compare the efficacy and speed of centrifugation and filtration systems for removing pollen from honey samples as a precursor to plant DNA barcoding.•We introduce the 'HONEYPI' informatics pipeline, an open access resource implemented in python 2.7, to ensure long-term reproducibility during the process of amplicon sequence variant classification.

Detection of Intrinsically Resistant Candida in Mixed Samples by MALDI TOF-MS and a Modified Naïve Bayesian Classifier.

MALDI-TOF MS is one of the major methods for clinical fungal identification, but it is currently only suitable for pure cultures of isolated strains. However, multiple fungal coinfections might occur in clinical practice. Some fungi involved in coinfection, such as Candida krusei and Candida auris, are intrinsically resistant to certain drugs. Identifying intrinsically resistant fungi from coinfected mixed cultures is extremely important for clinical treatment because different treatment options would be pursued accordingly. In this study, we counted the peaks of various species generated by Bruker Daltonik MALDI Biotyper software and accordingly constructed a modified naïve Bayesian classifier to analyze the presence of C. krusei and C. auris in simulated mixed samples. When reasonable parameters were fixed, the modified naïve Bayesian classifier effectively identified C. krusei and C. auris in the mixed samples (sensitivity 93.52%, specificity 92.5%). Our method not only provides a viable solution for identifying the two highlighted intrinsically resistant Candida species but also provides a case for the use of MALDI-TOF MS for analyzing coinfections of other species.