Multifunction adsorption materials: Part 1-Interaction of uranium and crystalline TiO₂.mH₂O modified by amorphous SiO₂.nH₂O

677-682The crystalline hydrous titanium dioxide modified by amorphous hydrous silicon dioxide (CTDASD), 2SiO2.3Ti O2.6H2O has been synthesized by ageing the amorphous mixed silicon-titanium hydroxide at 80°C for 36 hours. The prepared material possesses adjustable selectivity and apparent ion exchange capacity for a certain element or even for a group of elements. XRD, TGA and pH titration have been employed to characterize the prepared material. The uptake of uranyl ions on CTDASD is independent of concentration of sodium ions under the experimental conditions investigated, suggesting the material synthesized is reliable to remove uranyl ions from the media of high salt concentration. The uptake of uranium on the CTDASD is remarkably sensitive to the solution pH and reaches the maximum at pH 4.5. Plot of log KD of uranium versus equilibrium pH generates a series of lines with the mean slope of 0.63 at pH 1,→ 4, indicating the sophisticated loading mechanisms in H / UO2+2 reaction

Fluorene Side-Chained Benzodithiophene Polymers for Low Energy Loss Solar Cells

Here we design and synthesize one novel fluorene side-chained benzodithiophene (BDT) monomer for polymer solar cells (PSCs) donor. By copolymerizing this monomer with 4,7-di­(thiophen-2-yl)-2,1,3-benzo­thiadiazole (DTBT) or 4,7-di­(4-(2-ethylhexyl)-2-thienyl)-5,6-difluoro-2,1,3-benzo­thiadiazole (DT<i>ff</i>BT), two donor–acceptor (D–A) conjugated polymers PFBDT–DTBT and PFBDT–DT<i>ff</i>BT are prepared. PSCs are prepared with these polymers as donor and PC₇₁BM as acceptor. The maximum power conversion efficiency (PCE) of the two polymers PFBDT–DTBT and PFBDT–DT<i>ff</i>BT based PSCs is 7.13% (<i>V</i>_OC = 0.90 V, <i>J</i>_SC = 13.26 mA cm^–2, and FF = 0.598) and 7.33% (<i>V</i>_OC = 0.96 V, <i>J</i>_SC = 13.24 mA cm^–2, and FF = 0.577). The UV–vis absorption and electrochemical cyclic voltammetry test results show that F atoms in DT<i>ff</i>BT unit present an obvious influence on intermolecular effect and molecular energy levels of polymers. Furthermore, the energy loss of two PSCs devices in this work is confirmed to be 0.78 and 0.71 eV, lower than most results based on BDT PSCs devices, which is critical to obtain high PCE PSCs devices with a decent trade-off between <i>J</i>_SC and <i>V</i>_OC

Underappreciated Emission Spikes From Power Plants During Heatwaves Observed From Space: Case Studies in India and China

Abstract The frequency, intensity, and duration of extreme heatwaves are projected to increase in the global context of climate change. However, evidence of how anthropogenic emissions respond to heatwaves and further impact air quality remains elusive. Here, we use satellite remote sensing measurements alongside chemical transport model simulations to reveal abrupt variations in primary and secondary air pollutants introduced by extreme heatwaves. We highlight evidence from China and India, where satellite sulfur dioxide (SO2) and nitrogen dioxide (NO2) columns over thermal power plants enhance consistently responding to heatwaves. We attribute such spiked emissions to soaring electricity use and demonstrate that bottom‐up inventories underestimate the emissions from the power sector by 34.9% for the selected case. Elevated emissions facilitate fine particulate matter (PM2.5) and ozone (O3) formation over thermal power plants in an inhomogeneous manner, due to the combined effect of atmospheric oxidizing capacity, thermal decomposition of peroxyacetyl nitrate, planetary boundary layer rise, and air stagnation. Our results underscore the emerging challenge of pollution control attributable to the increasing climate penalty and the necessity of targeted control strategies and alternative energy sources during heatwaves

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields

Cell graph neural networks enable the precise prediction of patient survival in gastric cancer.

Gastric cancer is one of the deadliest cancers worldwide. An accurate prognosis is essential for effective clinical assessment and treatment. Spatial patterns in the tumor microenvironment (TME) are conceptually indicative of the staging and progression of gastric cancer patients. Using spatial patterns of the TME by integrating and transforming the multiplexed immunohistochemistry (mIHC) images as Cell-Graphs, we propose a graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for the digital staging of TME and precise prediction of patient survival in gastric cancer. In this study, patient survival prediction is formulated as either a binary (short-term and long-term) or ternary (short-term, medium-term, and long-term) classification task. Extensive benchmarking experiments demonstrate that the CGSignature achieves outstanding model performance, with Area Under the Receiver Operating Characteristic curve of 0.960 ± 0.01, and 0.771 ± 0.024 to 0.904 ± 0.012 for the binary- and ternary-classification, respectively. Moreover, Kaplan-Meier survival analysis indicates that the "digital grade" cancer staging produced by CGSignature provides a remarkable capability in discriminating both binary and ternary classes with statistical significance (P value < 0.0001), significantly outperforming the AJCC 8th edition Tumor Node Metastasis staging system. Using Cell-Graphs extracted from mIHC images, CGSignature improves the assessment of the link between the TME spatial patterns and patient prognosis. Our study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology

Global Observations of Tropospheric Bromine Monoxide (BrO) Columns From TROPOMI

Bromine monoxide (BrO) plays an important role in tropospheric chemistry. The state-of-thescience TROPOspheric Monitoring Instrument (TROPOMI) offers the potential to monitor atmospheric composition with a fine spatial resolution of up to 5.5 × 3.5 km2. We present here the retrieval of tropospheric BrO columns from TROPOMI. We implement a stratospheric correction scheme using a climatological approach based on the latest GEOS-Chem High Performance chemical transport model, and improve the tropospheric air mass factor calculation with TROPOMI surface albedo data accounting for the geometrical dependency. Our product presents a good level of consistency in comparison with measurements from ground-based zenith-sky differential optical absorption spectroscopy (r = 0.67), aircrafts (r = 0.46), and satellites (similar spatial distributions of BrO columns). Furthermore, our retrieval captures BrO enhancements in the polar springtime with values up to 7.8 × 1013 molecules cm−2 and identifies small-scale emission sources such as volcanoes and salt marshes. Based on TROPOMI data, we probe a blowing snow aerosol bromine mechanism in which the snow salinity is reduced to better match simulation and observation. Our TROPOMI tropospheric BrO product contributes high-resolution global information to studies investigating atmospheric bromine chemistr