Using Artificial Intelligence to extract information on pathogen characteristics from scientific publications

Health risk assessment of environmental exposure to pathogens requires complete and up to date knowledge. With the rapid growth of scientific publications and the protocolization of literature reviews, an automated approach based on Artificial Intelligence (AI) techniques could help extract meaningful information from the literature and make literature reviews more efficient. The objective of this research was to determine whether it is feasible to extract both qualitative and quantitative information from scientific publications about the waterborne pathogen Legionella on PubMed, using Deep Learning and Natural Language Processing techniques. The model effectively extracted the qualitative and quantitative characteristics with high precision, recall and F-score of 0.91, 0.80, and 0.85 respectively. The AI extraction yielded results that were comparable to manual information extraction. Overall, AI could reliably extract both qualitative and quantitative information about Legionella from scientific literature. Our study paved the way for a better understanding of the information extraction processes and is a first step towards harnessing AI to collect meaningful information on pathogen characteristics from environmental microbiology publications.Sanitary EngineeringWater Resource

Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing—Case Study of a Dutch Water Utility

Most water utilities have to handle a substantial number of customer complaints every year. Traditionally, complaints are handled by skilled staff who know how to identify primary issues, classify complaints, find solutions, and communicate with customers. The effort associated with complaint processing is often great, depending on the number of customers served by a water utility. However, the rise of natural language processing (NLP), enabled by deep learning, and especially the use of deep recurrent and convolutional neural networks, has created new opportunities for comprehending and interpreting text complaints. As such, we aim to investigate the value of the use of NLP for processing customer complaints. Through a case study about the Water Utility Groningen in the Netherlands, we demonstrate that NLP can parse language structures and extract intents and sentiments from customer complaints. As a result, this study represents a critical and fundamental step toward fully automating consumer complaint processing for water utilities.Sanitary Engineerin

Microwave Heating Healing of Asphalt Mixture with Coal Gangue Powder and Basalt Aggregate

Microwave heating is an effective method to achieve autonomic crack healing in asphalt mixtures, and the use of microwave-absorbing materials can largely improve this healing efficiency. As a solid waste, coal gangue contains metal oxides, which shows the possibility of microwave heating. In order to further promote the application of coal gangue in the microwave healing of asphalt mixtures, this study looks into the synergistic effect of basalt and coal gangue powder (CGP) on the microwave heating self-healing of an asphalt mixture. The mechanical performance, water stability, low-temperature crack resistance and microwave healing efficiency of the asphalt mixture were investigated using the immersion Marshall test, standard Marshall test, Cantabro test and semi-circular bending (SCB), and healing tests, respectively. The results indicated that the addition of CGP in asphalt mixture can improve the microwave heating speed, which also showed a significant advantage in water stability and fracture energy recovery. The research results will further promote the utilization rate of coal gangue.Materials and Environmen

Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment

Rainfall-triggered shallow landslides are widespread natural hazards around the world, causing many damages to human lives and property. In this study, we focused on predicting landslides in a large region by coupling a 1 km-resolution hydrological model and a 90 m-resolution slope stability model, where a downscaling method for soil moisture via topographic wetness index was applied. The modeled hydrological processes show generally good agreements with the observed discharges: relative biases and correlation coefficients at three validation stations are all <20% and >0.60, respectively. The derived scaling law for soil moisture allows for near-conservative downscaling of the original 1-km soil moisture to 90-m resolution for slope stability assessment. For landslide prediction, the global accuracy and true positive rate are 97.2% and 66.9%, respectively. This study provides an effective and computationally efficient coupling method to predict landslides over large regions in which fine-scale topographical information is incorporated.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Water Resource

Superconducting persistent-current qubit

We present the design of a superconducting qubit that has circulating currents of opposite sign as its two states. The circuit consists of three nano-scale aluminum Josephson junctions connected in a superconducting loop and controlled by magnetic fields. The advantages of this qubit are that it can be made insensitive to background charges in the substrate, the flux in the two states can be detected with a SQUID, and the states can be manipulated with magnetic fields. Coupled systems of qubits are also discussed as well as sources of decoherence.Comment: 15 pages. Updated ref.[17]: Science 285, 1036 (1999

The FluidFlower Validation Benchmark Study for the Storage of CO ₂

Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the “FluidFlower”), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations.Applied GeologyReservoir EngineeringNumerical Analysi

Cofactor Specificity of the Bifunctional Alcohol and Aldehyde Dehydrogenase (AdhE) in Wild-Type and Mutant Clostridium thermocellum and Thermoanaerobacteriumsaccharolyticum

Clostridium thermocellum and Thermoanaerobacterium saccharolyticum are thermophilic bacteria that have been engineered to produce ethanol from the cellulose and hemicellulose fractions of biomass, respectively. Although engineered strains of T. saccharolyticum produce ethanol with a yield of 90% of the theoretical maximum, engineered strains of C. thermocellum produce ethanol at lower yields (?50% of the theoretical maximum). In the course of engineering these strains, a number of mutations have been discovered in their adhE genes, which encode both alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) enzymes. To understand the effects of these mutations, the adhE genes from six strains of C. thermocellum and T. saccharolyticum were cloned and expressed in Escherichia coli, the enzymes produced were purified by affinity chromatography, and enzyme activity was measured. In wild-type strains of both organisms, NADH was the preferred cofactor for both ALDH and ADH activities. In high-ethanol-producing (ethanologen) strains of T. saccharolyticum, both ALDH and ADH activities showed increased NADPH-linked activity. Interestingly, the AdhE protein of the ethanologenic strain of C. thermocellum has acquired high NADPH-linked ADH activity while maintaining NADH-linked ALDH and ADH activities at wild-type levels. When single amino acid mutations in AdhE that caused increased NADPH-linked ADH activity were introduced into C. thermocellum and T. saccharolyticum, ethanol production increased in both organisms. Structural analysis of the wild-type and mutant AdhE proteins was performed to provide explanations for the cofactor specificity change on a molecular level.BionanoscienceApplied Science

Colorectal cancer intrinsic subtypes predict chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition

In most colorectal cancer (CRC) patients, outcome cannot be predicted because tumors with similar clinicopathological features can have differences in disease progression and treatment response. Therefore, a better understanding of the CRC biology is required to identify those patients who will benefit from chemotherapy and to find a more tailored therapy plan for other patients. Based on unsupervised classification of whole genome data from 188 stages I–IV CRC patients, a molecular classification was developed that consist of at least three major intrinsic subtypes (A-, B- and C-type). The subtypes were validated in 543 stages II and III patients and were associated with prognosis and benefit from chemotherapy. The heterogeneity of the intrinsic subtypes is largely based on three biological hallmarks of the tumor: epithelial-to-mesenchymal transition, deficiency in mismatch repair genes that result in high mutation frequency associated with microsatellite instability and cellular proliferation. A-type tumors, observed in 22% of the patients, have the best prognosis, have frequent BRAF mutations and a deficient DNA mismatch repair system. C-type patients (16%) have the worst outcome, a mesenchymal gene expression phenotype and show no benefit from adjuvant chemotherapy treatment. Both A-type and B-type tumors have a more proliferative and epithelial phenotype and B-types benefit from adjuvant chemotherapy. B-type tumors (62%) show a low overall mutation frequency consistent with the absence of DNA mismatch repair deficiency. Classification based on molecular subtypes made it possible to expand and improve CRC classification beyond standard molecular and immunohistochemical assessment and might help in the future to guide treatment in CRC patients.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc

Emission factors for gaseous and particulate pollutants from offshore diesel engine vessels in China

Shipping emissions have significant influence on atmospheric environment as well as human health, especially in coastal areas and the harbour districts. However, the contribution of shipping emissions on the environment in China still need to be clarified especially based on measurement data, with the large number ownership of vessels and the rapid developments of ports, international trade and shipbuilding industry. Pollutants in the gaseous phase (carbon monoxide, sulfur dioxide, nitrogen oxides, total volatile organic compounds) and particle phase (particulate matter, organic carbon, elemental carbon, sulfates, nitrate, ammonia, metals) in the exhaust from three different diesel-engine-powered offshore vessels in China (350, 600 and 1600aEuro-kW) were measured in this study. Concentrations, fuel-based and power-based emission factors for various operating modes as well as the impact of engine speed on emissions were determined. Observed concentrations and emission factors for carbon monoxide, nitrogen oxides, total volatile organic compounds, and particulate matter were higher for the low-engine-power vessel (HH) than for the two higher-engine-power vessels (XYH and DFH); for instance, HH had NOx EF (emission factor) of 25.8aEuro-gaEuro-kWh(-1) compared to 7.14 and 6.97aEuro-gaEuro-kWh(-1) of DFH, and XYH, and PM EF of 2.09aEuro-gaEuro-kWh(-1) compared to 0.14 and 0.04aEuro-gaEuro-kWh(-1) of DFH, and XYH. Average emission factors for all pollutants except sulfur dioxide in the low-engine-power engineering vessel (HH) were significantly higher than that of the previous studies (such as 30.2aEuro-gaEuro-kg(-1) fuel of CO EF compared to 2.17 to 19.5aEuro-gaEuro-kg(-1) fuel in previous studies, 115aEuro-gaEuro-kg(-1) fuel of NOx EF compared to 22.3 to 87aEuro-gaEuro-kg(-1) fuel in previous studies and 9.40aEuro-gaEuro-kg(-1) fuel of PM EF compared to 1.2 to 7.6aEuro-gaEuro-kg(-1) fuel in previous studies), while for the two higher-engine-power vessels (DFH and XYH), most of the average emission factors for pollutants were comparable to the results of the previous studies, engine type was one of the most important influence factors for the differences. Emission factors for all three vessels were significantly different during different operating modes. Organic carbon and elemental carbon were the main components of particulate matter, while water-soluble ions and elements were present in trace amounts. The test inland ships and some test offshore vessels in China always had higher EFs for CO, NOx, and PM than previous studies. Besides, due to the significant influence of engine type on shipping emissions and that no accurate local EFs could be used in inventory calculation, much more measurement data for different vessels in China are still in urgent need. Best-fit engine speeds during actual operation should be based on both emission factors and economic costs