Measurements and Models for Hazardous chemical and Mixed Wastes

Mixed solvent aqueous waste of various chemical compositions constitutes a significant fraction of the total waste produced by industry in the United States. Not only does the chemical process industry create large quantities of aqueous waste, but the majority of the waste inventory at the DOE sites previously used for nuclear weapons production is mixed solvent aqueous waste. In addition, large quantities of waste are expected to be generated in the clean-up of those sites. In order to effectively treat, safely handle, and properly dispose of these wastes, accurate and comprehensive knowledge of basic thermophysical properties is essential. The goal of this work is to develop a phase equilibrium model for mixed solvent aqueous solutions containing salts. An equation of state was sought for these mixtures that (a) would require a minimum of adjustable parameters and (b) could be obtained from a available data or data that were easily measured. A model was developed to predict vapor composition and pressure given the liquid composition and temperature. It is based on the Peng-Robinson equation of state, adapted to include non-volatile and salt components. The model itself is capable of predicting the vapor-liquid equilibria of a wide variety of systems composed of water, organic solvents, salts, nonvolatile solutes, and acids or bases. The representative system o water + acetone + 2-propanol + NaNo3 was selected to test and verify the model. Vapor-liquid equilibrium and phase density measurements were performed for this system and its constituent binaries

Genomic alterations indicate tumor origin and varied metastatic potential of disseminated cells from prostate-cancer patients

Disseminated epithelial cells can be isolated from the bone marrow of a far greater fraction of prostate-cancer patients than the fraction of patients who progress to metastatic disease. To provide a better understanding of these cells, we have characterized their genomic alterations. We first present an array comparative genomic hybridization method capable of detecting genomic changes in the small number of disseminated cells (10-20) that can typically be obtained from bone-marrow aspirates of prostate-cancer patients. We show multiple regions of copy-number change, including alterations common in prostate cancer, such as 8p loss, 8q gain, and gain encompassing the androgen-receptor gene on Xq, in the disseminated cell pools from 11 metastatic patients. We found fewer and less striking genomic alterations in the 48 pools of disseminated cells from patients with organ-confined disease. However, we identify changes shared by these samples with their corresponding primary tumors and prostate-cancer alterations reported in the literature, evidence that these cells, like those in advanced disease, are disseminated tumor cells (DTCs). We also demonstrate that DTCs from patients with advanced and localized disease share several abnormalities, including losses containing cell-adhesion genes and alterations reported to associate with progressive disease. These shared alterations might confer the capability to disseminate or establish secondary disease. Overall, the spectrum of genomic deviations is evidence for metastatic capacity in advanced-disease DTCs and variation in that capacity in DTCs from localized disease. Our analysis lays the foundation for elucidation of the relationship between DTC genomic alterations and progressive prostate cancer

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Measurement and Model for Hazardous Chemical and Mixed Waste

Mixed solvent aqueous waste of various chemical compositions constitutes a significant fraction of the total waste produced by industry in the United States. Not only does the chemical process industry create large quantities of aqueous waste, but the majority of the waste inventory at the Department of Energy (DOE) sites previously used for nuclear weapons production is mixed solvent aqueous waste. In addition, large quantities of waste are expected to be generated in the clean-up of those sites. In order to effectively treat, safely handle, and properly dispose of these wastes, accurate and comprehensive knowledge of basic thermophysical properties is essential. The goal of this work is to develop a phase equilibrium model for mixed solvent aqueous solutions containing salts. An equation of state was sought for these mixtures that (a) would require a minimum of adjustable parameters and (b) could be obtained from a available data or data that were easily measured. A model was developed to predict vapor composition and pressure given the liquid composition and temperature. It is based on the Peng-Robinson equation of state, adapted to include non-volatile and salt components. The model itself is capable of predicting the vapor-liquid equilibria of a wide variety of systems composed of water, organic solvents, salts, nonvolatile solutes, and acids or bases. The representative system of water + acetone + 2-propanol + NaNO3 was selected to test and verify the model. Vapor-liquid equilibrium and phase density measurements were performed for this system and its constituent binaries

Measurements And Models For Hazardous Chemical and Mixed Wastes

Aqueous waste of various chemical compositions constitutes a significant fraction of the total waste produced by industry in the United States. A large quantity of the waste generated by the U.S. chemical process industry is waste water. In addition, the majority of the waste inventory at DoE sites previously used for nuclear weapons production is aqueous waste. Large quantities of additional aqueous waste are expected to be generated during the clean-up of those sites. In order to effectively treat, safely handle, and properly dispose of these wastes, accurate and comprehensive knowledge of basic thermophysical property information is paramount. This knowledge will lead to huge savings by aiding in the design and optimization of treatment and disposal processes. The main objectives of this project are: Develop and validate models that accurately predict the phase equilibria and thermodynamic properties of hazardous aqueous systems necessary for the safe handling and successful design of separation and treatment processes for hazardous chemical and mixed wastes. Accurately measure the phase equilibria and thermodynamic properties of a representative system (water + acetone + isopropyl alcohol + sodium nitrate) over the applicable ranges of temperature, pressure, and composition to provide the pure component, binary, ternary, and quaternary experimental data required for model development