A Comparison of a Conventional Chemical Coagulant and a Natural Coagulant Derived from Cassia fistula Seeds for the Removal of Heavy Metal Ions.

Cassia fistula seed-derived coagulant has been reported to exhibit high coagulating-flocculating activity, environmental friendliness, and cost-effectiveness for the wastewater treatment, especially of textile wastewater. For heavy metal removal, however, research focusing on evaluating the feasibility of this material is still limited. Therefore, this study reports jar-test experiments in which the Zn2+ and Ni2+ removal efficiency of C. fistula coagulant was assessed. Moreover, a comparison of coagulation performance using a conventional chemical coagulant and the natural coagulant was performed. Characterization of the C. fistula seed-derived coagulant revealed the presence of important functional groups and fibrous networks with rough surfaces. A bench-scale study indicated that the coagulation performance of the two coagulants depends strongly on the initial concentration of metal ions, pH level, and coagulant dosage. The C. fistula seed-derived coagulant was found to possess higher removal efficiency than polyaluminum chloride. This natural coagulant removed over 80% of metal ions at the optimal conditions of pH 5.0, a metal ion concentration of 25 ppm, and a dosage of 0.8 and 1.6 g/L for Zn2+ and Ni2+, respectively. This study shows that C. fistula seed-derived coagulant is a potential alternative to chemical coagulants and could be developed to provide an environmentally friendly, economical, and efficient wastewater treatment.

BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification.

Anatomical airway labeling is crucial for precisely identifying airways displaying symptoms such as constriction, increased wall thickness, and modified branching patterns, facilitating the diagnosis and treatment of pulmonary ailments. This study introduces an innovative airway labeling methodology, BranchLabelNet, which accounts for the fractal nature of airways and inherent hierarchical branch nomenclature. In developing this methodology, branch-related parameters, including position vectors, generation levels, branch lengths, areas, perimeters, and more, are extracted from a dataset of 1000 chest computed tomography (CT) images. To effectively manage this intricate branch data, we employ an n-ary tree structure that captures the complicated relationships within the airway tree. Subsequently, we employ a divide-and-group deep learning approach for multi-label classification, streamlining the anatomical airway branch labeling process. Additionally, we address the challenge of class imbalance in the dataset by incorporating the Tomek Links algorithm to maintain model reliability and accuracy. Our proposed airway labeling method provides robust branch designations and achieves an impressive average classification accuracy of 95.94% across fivefold cross-validation. This approach is adaptable for addressing similar complexities in general multi-label classification problems within biomedical systems.

Computed tomography-based imaging biomarker identifies coal workers' pneumoconiosis.

Rationale: The increase in the incidence and the diagnostic limitations of pneumoconiosis have emerged as a public health concern. This study aimed to conduct a computed tomography (CT)- based quantitative analysis to understand differences in imaging results of pneumoconiosis according to disease severity. Methods: According to the International Labor Organization (ILO) guidelines, coal workers' pneumoconiosis (CWP) are classified into five categories. CT images were obtained only at full inspiration and were quantitatively evaluated for airway structural variables such as bifurcation angle (θ), hydraulic diameter (Dh), wall thickness (WT), and circularity (Cr). Parenchymal functional variables include abnormal regions (emphysema, ground-glass opacities, consolidation, semi consolidation, and fibrosis) and blood vessel volume. Through the propensity score matching method, the confounding effects were decreased. Results: Category 4 demonstrated a reduced θ in TriLUL, a thicker airway wall in both the Trachea and Bronint compared to Category 0, and a decreased Cr in Bronint. Category 4 presented with higher abnormal regions except for ground-glass opacity and a narrower pulmonary blood vessel volume. A negative correlation was found between abnormal areas with lower Hounsfield units (HU) than the normal lung and the ratio of forced expiratory volume in 1 s/forced vital capacity, with narrowed pulmonary blood vessel volume which is positively correlated with abnormal areas with upper HU than the normal lung. Conclusion: This study provided valuable insight into pneumoconiosis progression through a comparison of quantitative CT images based on severity. Furthermore, as there has been paucity of studies on the pulmonary blood vessel volume of the CWP, in this study, a correlation between reduced pulmonary blood vessel volume and regions with low HU values holds significant importance.

Water lettuce (Pistia stratiotes L.) increases biogas effluent pollutant removal efficacy and proves a positive substrate for renewable energy production.

Background: Aquatic plants play a crucial role in nature-based wastewater treatment and provide a promising substrate for renewable energy production using anaerobic digestion (AD) technology. This study aimed to examine the contaminant removal from AD effluent by water lettuce (WL) and produce biogas from WL biomass co-digested with pig dung (PD) in a farm-scale biogas digester. Methods: The first experiment used styrofoam boxes containing husbandry AD effluent. WLs were initially arranged in 50%, 25%, 12.5%, and 0% surface coverage. Each treatment was conducted in five replicates under natural conditions. In the second experiment, WL biomass was co-digested with PD into an existing anaerobic digester to examine biogas production on a farm scale. Results: Over 30 days, the treatment efficiency of TSS, BOD5, COD, TKN, and TP in the effluent was 93.75-97.66%, 76.63-82.56%, 76.78-82.89%, 61.75-63.75%, and 89.00-89.57%, respectively. Higher WL coverage increased the pollutant elimination potential. The WL biomass doubled after 12 days for all treatments. In the farm-scale biogas production, the biogas yield varied between 190.6 and 292.9 L kg VSadded-1. The methane content reached over 54%. Conclusions: WL removed AD effluent nutrients effectively through a phytoremediation system and generated significant biomass for renewable energy production in a farm-scale model.

Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques.

Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability.

Development of a COVID-19 Application Ontology for the ACT Network.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Development of a Coronavirus Disease 2019 (COVID-19) Application Ontology for the Accrual to Clinical Trials (ACT) network.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Role of Clinical and Imaging Risk Factors in Predicting Breast Cancer Diagnosis Among BI-RADS 4 Cases.

PURPOSE: To analyze women with suspicious findings (assessed as Breast Imaging Reporting and Data System [BI-RADS] 4), examining the value of clinical and imaging predictors in predicting cancer diagnosis. PATIENTS AND METHODS: A set of 2138 examinations (1978 women) given a BI-RADS 4 with matching pathology results were analyzed. Predictors such as patient demographics, clinical risk factors, and imaging-derived features such as BI-RADS assessment and qualitative breast density were considered. Independent predictors of breast cancer were determined by univariate analysis and multivariate logistic regression. RESULTS: In univariate analysis, age, race, body mass index, age at first live birth, BI-RADS assessment, qualitative breast density, and risk triggers were found to be independent predictors. In multivariate analysis, age, BI-RADS score, breast density, race, presence of a lump, and number of risk triggers were the most predictive. An integrative logistic regression model achieved a performance of 0.84 cross-validated area under the curve. No variable was a constant independent predictor when stratifying the population on the basis of the BI-RADS score. CONCLUSION: While BI-RADS assessment remains the strongest predictor of breast cancer, the inclusion of clinical risk factors such as age, breast density, presence of a lump, and number of risk triggers derived from guidelines improves the specificity of identifying individuals with imaging descriptors associated with BI-RADS 4A and 4B that are more likely to be diagnosed with breast cancer.