Matching Patients to Clinical Trials with Large Language Models

Clinical trials are vital in advancing drug development and evidence-based medicine, but their success is often hindered by challenges in patient recruitment. In this work, we investigate the potential of large language models (LLMs) to assist individual patients and referral physicians in identifying suitable clinical trials from an extensive selection. Specifically, we introduce TrialGPT, a novel architecture employing LLMs to predict criterion-level eligibility with detailed explanations, which are then aggregated for ranking and excluding candidate clinical trials based on free-text patient notes. We evaluate TrialGPT on three publicly available cohorts of 184 patients and 18,238 annotated clinical trials. The experimental results demonstrate several key findings: First, TrialGPT achieves high criterion-level prediction accuracy with faithful explanations. Second, the aggregated trial-level TrialGPT scores are highly correlated with expert eligibility annotations. Third, these scores prove effective in ranking clinical trials and exclude ineligible candidates. Our error analysis suggests that current LLMs still make some mistakes due to limited medical knowledge and domain-specific context understanding. Nonetheless, we believe the explanatory capabilities of LLMs are highly valuable. Future research is warranted on how such AI assistants can be integrated into the routine trial matching workflow in real-world settings to improve its efficiency

Radiative Forcing Due to Major Aerosol Emitting Sectors in China and India

Understanding the radiative forcing caused by anthropogenic aerosol sources is essential for making effective emission control decisions to mitigate climate change. We examined the net direct plus indirect radiative forcing caused by carbonaceous aerosol and sulfur emissions in key sectors of China and India using the GISS-E2 chemistry-climate model. Diesel trucks and buses (67 mW/ sq. m) and residential biofuel combustion (52 mW/ sq. m) in India have the largest global mean, annual average forcings due mainly to the direct and indirect effects of BC. Emissions from these two sectors in China have near-zero net global forcings. Coal-fired power plants in both countries exert a negative forcing of about -30 mW/ sq. m from production of sulfate. Aerosol forcings are largest locally, with direct forcings due to residential biofuel combustion of 580 mW/ sq. m over India and 416 mW/ sq. m over China, but they extend as far as North America, Europe, and the Arcti

Ozone Monitoring Instrument Observations of Interannual Increases in SO2 Emissions from Indian Coal-fired Power Plants During 2005-2012

Due to the rapid growth of electricity demand and the absence of regulations, sulfur dioxide (SO2) emissions from coal-fired power plants in India have increased notably in the past decade. In this study, we present the first interannual comparison of SO2 emissions and the satellite SO2 observations from the Ozone Monitoring Instrument (OMI) for Indian coal-fired power plants during the OMI era of 2005-2012. A detailed unit-based inventory is developed for the Indian coal-fired power sector, and results show that its SO2 emissions increased dramatically by 71 percent during 2005-2012. Using the oversampling technique, yearly high-resolution OMI maps for the whole domain of India are created, and they reveal a continuous increase in SO2 columns over India. Power plant regions with annual SO2 emissions greater than 50 Gg year-1 produce statistically significant OMI signals, and a high correlation (R equals 0.93) is found between SO2 emissions and OMI-observed SO2 burdens. Contrary to the decreasing trend of national mean SO2 concentrations reported by the Indian Government, both the total OMI-observed SO2 and average SO2 concentrations in coal-fired power plant regions increased by greater than 60 percent during 2005-2012, implying the air quality monitoring network needs to be optimized to reflect the true SO2 situation in India

Five hundred years of anthropogenic mercury: spatial and temporal release profiles

When released to the biosphere, mercury (Hg) is very mobile and can take millennia to be returned to a secure, long-term repository. Understanding where and when Hg was released as a result of human activities allows better quantification of present-day reemissions and future trajectories of environmental concentrations. In this work, we estimate the time-varying releases of Hg in seven world regions over the 500 year period, 1510–2010. By our estimation, this comprises 95% of all-time anthropogenic releases. Globally, 1.47 Tg of Hg were released in this period, 23% directly to the atmosphere and 77% to land and water bodies. Cumulative releases have been largest in Europe (427 Gg) and North America (413 Gg). In some world regions (Africa/Middle East and Oceania), almost all (>99%) of the Hg is relatively recent (emitted since 1850), whereas in South America it is mostly of older vintage (63% emitted before 1850). Asia was the greatest-emitting region in 2010, while releases in Europe and North America have declined since the 1970s, as recognition of the risks posed by Hg have led to its phase-out in commercial usage. The continued use of Hg in artisanal and small-scale gold mining means that the Africa/Middle East region is now a major contributor. We estimate that 72% of cumulative Hg emissions to air has been in the form of elemental mercury (Hg0), which has a long lifetime in the atmosphere and can therefore be transported long distances. Our results show that 83% of the total Hg has been released to local water bodies, onto land, or quickly deposited from the air in divalent (HgII) form. Regionally, this value ranges from 77% in Africa/Middle East and Oceania to 89% in South America. Results from global biogeochemical modeling indicate improved agreement of the refined emission estimates in this study with archival records of Hg accumulation in estuarine and deep ocean sediment

Estimates of Power Plant NOx Emissions and Lifetimes from OMI NO2 Satellite Retrievals

Isolated power plants with well characterized emissions serve as an ideal test case of methods to estimate emissions using satellite data. In this study we evaluate the Exponentially-Modified Gaussian (EMG) method and the box model method based on mass balance for estimating known NOx emissions from satellite retrievals made by the Ozone Monitoring Instrument (OMI). We consider 29 power plants in the USA which have large NOx plumes that do not overlap with other sources and which have emissions data from the Continuous Emission Monitoring System (CEMS). This enables us to identify constraints required by the methods, such as which wind data to use and how to calculate background values. We found that the lifetimes estimated by the methods are too short to be representative of the chemical lifetime. Instead, we introduce a separate lifetime parameter to account for the discrepancy between estimates using real data and those that theory would predict. In terms of emissions, the EMG method required averages from multiple years to give accurate results, whereas the box model method gave accurate results for individual ozone seasons

A high-resolution and observationally constrained OMI NO₂ satellite retrieval

This work presents a new high-resolution NO2 dataset derived from the NASA Ozone Monitoring Instrument (OMI) NO2 version 3.0 retrieval that can be used to estimate surface-level concentrations. The standard NASA product uses NO2 vertical profile shape factors from a 1.25°  ×  1° (∼  110 km  ×  110 km) resolution Global Model Initiative (GMI) model simulation to calculate air mass factors, a critical value used to determine observed tropospheric NO2 vertical columns. To better estimate vertical profile shape factors, we use a high-resolution (1.33 km  ×  1.33 km) Community Multi-scale Air Quality (CMAQ) model simulation constrained by in situ aircraft observations to recalculate tropospheric air mass factors and tropospheric NO2 vertical columns during summertime in the eastern US. In this new product, OMI NO2 tropospheric columns increase by up to 160 % in city centers and decrease by 20–50 % in the rural areas outside of urban areas when compared to the operational NASA product. Our new product shows much better agreement with the Pandora NO2 and Airborne Compact Atmospheric Mapper (ACAM) NO2 spectrometer measurements acquired during the DISCOVER-AQ Maryland field campaign. Furthermore, the correlation between our satellite product and EPA NO2 monitors in urban areas has improved dramatically: r2  =  0.60 in the new product vs. r2  =  0.39 in the operational product, signifying that this new product is a better indicator of surface concentrations than the operational product. Our work emphasizes the need to use both high-resolution and high-fidelity models in order to recalculate satellite data in areas with large spatial heterogeneities in NOx emissions. Although the current work is focused on the eastern US, the methodology developed in this work can be applied to other world regions to produce high-quality region-specific NO2 satellite retrievals

A red Fuji apple appearance grading method based on improved whale optimization algorithm and CNN

Objective: In order to improve the accuracy of machine vision technology in grading the appearance quality of red Fuji apples, a red Fuji apple appearance grading method based on improved whale optimization algorithm （WOA） and CNN is proposed. Methods: A red Fuji apple image database with different appearance quality levels was established, and the database images were preprocessed so as to improve the training effect and generalization ability of the model. The improved CNN-LSTM was designed as the weighted grey correlation method was used to compress the CNN convolution scale, in order to reduce redundant interference between features and improve the computational speed of the model. The improved whale optimization algorithm was used to optimize the hyperparameters configuration of CNN-LSTM, effectively reducing the impact of improper hyperparameter configuration on model classification results. Results: The simulation results showed that the proposed classification method had a higher accuracy, with classification accuracy and sensitivity improved by about 2.05% and 2.46%. Conclusion: The proposed method can effectively achieve the appearance grading of red Fuji apples

Cold Atmospheric-Pressure Plasma Caused Protein Damage in Methicillin-Resistant \u3ci\u3eStaphylococcus aureus\u3c/i\u3e Cells in Biofilms

Biofilms formed by multidrug-resistant bacteria are a major cause of hospital-acquired infections. Cold atmospheric-pressure plasma (CAP) is attractive for sterilization, especially to disrupt biofilms formed by multidrug-resistant bacteria. However, the underlying molecular mechanism is not clear. In this study, CAP effectively reduced the living cells in the biofilms formed by methicillin-resistant Staphylococcus aureus, and 6 min treatment with CAP reduced the S. aureus cells in biofilms by 3.5 log10. The treatment with CAP caused the polymerization of SaFtsZ and SaClpP proteins in the S. aureus cells of the biofilms. In vitro analysis demonstrated that recombinant SaFtsZ lost its self-assembly capability, and recombinant SaClpP lost its peptidase activity after 2 min of treatment with CAP. Mass spectrometry showed oxidative modifications of a cluster of peaks differing by 16 Da, 31 Da, 32 Da, 47 Da, 48 Da, 62 Da, and 78 Da, induced by reactive species of CAP. It is speculated that the oxidative damage to proteins in S. aureus cells was induced by CAP, which contributed to the reduction of biofilms. This study elucidates the biological effect of CAP on the proteins in bacterial cells of biofilms and provides a basis for the application of CAP in the disinfection of biofilms

The Observed Response of Ozone Monitoring Instrument (OMI) NO2 Columns to NOx Emission Controls on Power Plants in the United States: 2005-2011

We show that Aura Ozone Monitoring Instrument (OMI) nitrogen dioxide (NO2) tropospheric column data may be used to assess changes of the emissions of nitrogen oxides (NOx) from power plants in the United States, though careful interpretation of the data is necessary. There is a clear response for OMI NO2 data to NOx emission reductions from power plants associated with the implementation of mandated emission control devices (ECDs) over the OMI record (2005e2011). This response is scalar for all intents and purposes, whether the reduction is rapid or incremental over several years. However, it is variable among the power plants, even for those with the greatest absolute decrease in emissions. We document the primary causes of this variability, presenting case examples for specific power plants