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Copy-number variations (CNV), loss of heterozygosity (LOH), and uniparental disomy (UPD) are large genomic aberrations leading to many common inherited diseases, cancers, and other complex diseases. An integrated tool to identify these aberrations is essential in understanding diseases and in designing clinical interventions. Previous discovery methods based on whole-genome sequencing (WGS) require very high depth of coverage on the whole genome scale, and are cost-wise inefficient. Another approach, whole exome genome sequencing (WEGS), is limited to discovering variations within exons. Thus, we are lacking efficient methods to detect genomic aberrations on the whole genome scale using next-generation sequencing technology. Here we present a method to identify genome-wide CNV, LOH and UPD for the human genome via selectively sequencing a small portion of genome termed Selected Target Regions (SeTRs). In our experiments, the SeTRs are covered by 99.73%~99.95% with sufficient depth. Our developed bioinformatics pipeline calls genome-wide CNVs with high confidence, revealing 8 credible events of LOH and 3 UPD events larger than 5M from 15 individual samples. We demonstrate that genome-wide CNV, LOH and UPD can be detected using a cost-effective SeTRs sequencing approach, and that LOH and UPD can be identified using just a sample grouping technique, without using a matched sample or familial information. [ABSTRACT FROM AUTHOR]

The objective of document-level relation extraction (RE) is to identify the semantic connections that exist between named entities present within a document. However, most entities are distributed among different sentences, there is a need for inter-entity relation prediction across sentences. Existing research has focused on framing sentences throughout documents to predict relationships between entities. However, not all sentences play a substantial role in relation extraction, which inevitably introduces noisy information. Based on this phenomenon, we believe that we can extract evidence sentences in advance and use these evidence sentences to construct graphs to mine semantic information between entities. Thus, we present a document-level RE model that leverages an Enhancing Cross-evidence Reasoning Graph (ECRG) for improved performance. Specifically, we design an evidence extraction rule based on center-sentence to pre-extract higher-quality evidence. Then, this evidence is constructed into evidence graphs to mine the connections between mentions within the same evidence. In addition, we construct entity-level graphs by aggregating mentions from the same entities within the evidence graphs, aiming to capture distant interactions between entities. Experiments result on both DocRED and RE-DocRED datasets demonstrate that our model improves entity RE performance compared to existing work., Competing Interests: The authors declare that they have no competing interests., (© 2024 Pi et al.)

Conventional cerium-based denitrification catalysts show good catalytic activity at moderate and high temperatures, but their denitrification performance may be decreased due to poisoning by SO 2 in the flue gas. In this paper, V was introduced into Ce-La/TiO 2 catalysts by a ball-milling method, and the effects of the V content on catalyst denitrification performance and SO 2 resistance were investigated. Fourier-transform diffuse reflectance in situ infrared spectroscopy was used to examine the denitrification mechanism and evaluate the catalysts for surface acidity, redox characteristics, and SO 2 adsorption. After introducing V, Brønsted acids played the dominant role in the catalytic reaction by increasing the number of acidic sites on the catalyst surface, adsorbing NH 3 to participate in the reaction, and improving the sulfur resistance by inhibiting SO 2 poisoning. The Ce 3+ and O ratio on the catalyst surface were also enhanced by V doping, which reduced interactions between SO 2 and the primary metal oxide active ingredients. The modified catalyst inhibited the formation of sulfate species on the catalyst surface and prevented the generation of additional nitrate species on the surface, which protected the main active sites. After V doping, the NH 3 -SCR reaction on the catalyst surface followed the Langmuir-Hinshelwood mechanism., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (This journal is © The Royal Society of Chemistry.)

High-temperature 10Ce-2La/TiO 2 catalysts for selective catalytic reduction of NO with NH 3 were prepared by the ball milling, impregnation and co-precipitation methods and their catalytic performance was compared. The effects of different starting materials of the ball milling method on the catalytic activity were investigated. The results showed that the 10Ce-2La/TiO 2 catalyst prepared by the ball milling method using carbonates as starting materials exhibited the highest NO conversion, which was more than 80% in the temperature range of 330-550 °C. The as-prepared catalysts were characterized by XRD, TEM, and XPS. Results showed that the ball milling prepared 10Ce-2La/TiO 2 had the advantages of uniform active site distribution, high oxygen storage capacity, and high Ce 3+ and O α ratio. The results of NH 3 -TPD and H 2 -TPR showed that the ball milling method not only improved the redox ability but also increased the quantities and concentration of the acidic sites. The green production and economically viable concept of this process provides a new solution for the production application of industrial catalysts., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (This journal is © The Royal Society of Chemistry.)