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What language do bats speak? I’m trying to find out
Molecular insights into the effect of hydrocarbon gas composition characteristics on tight oil migration
Rock burst mechanisms induced by dynamic and static loading under composite key strata: a case study
Ortho-functionalization of a 211At-labeled aryl compound provides stabilization of the C-At bond against oxidative dehalogenation
Abstract Targeted alpha therapy appears to be a promising approach in nuclear medicine for the treatment of cancers. Thanks to its appropriate physical properties, 211 At is an ideal candidate compared with other alpha emitters. Because of its enigmatic nature, the chemical element astatine is the subject of growing interest to better understand its radiochemistry. The application of 211 At in the clinic, which has shown good therapeutic results, is however still hampered. Stability issues of 211 At-radiolabeling were quickly encountered in early preclinical trials and later confirmed in the clinic that mainly studied 211 At-radiopharmaceuticals labeled by formation of an astatobenzamide derivative. Recent studies have shed light on the deastatination mechanisms encountered in vivo, in particular potential oxidative mechanisms that may weaken the carbon-astatine bond formed during the radiolabeling. In this work, we show that ortho -functionalization of astatoaryl compounds with benzyl alcohols protects radiolabeling from deastatination in a strongly oxidizing and acidic medium, as well as in liver microsomal media reproducing in vivo deastatination via cytochrome P450 (CYP450) mediated mechanisms. Our results open the way to the rational design of new 211 At-aryl-based compounds with improved stability.
Exploring the antitrypanosomal potential of rosemary root endophytic fungi with metabolomic profiling and molecular docking insights
North America’s birds are declining where they should be thriving
Synergistic effect of particle packing method, aggregate saturation levels, and paste content in improving the performance of high volume fine recycled aggregate concrete
Rice paddies produce food for billions ― and lots of methane
A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology
Abstract Detecting protein complexes is crucial in computational biology for understanding cellular mechanisms and facilitating drug discovery. Evolutionary algorithms (EAs) have proven effective in uncovering protein complexes within networks of protein–protein interactions (PPIs). However, their integration with functional insights from gene ontology (GO) annotations remains underexplored. This paper presents two primary contributions: First, it proposes a novel multi-objective optimization model for detecting protein complexes, conceptualizing the task as a problem with inherently conflicting objectives based on biological data. Second, it introduces an innovative gene ontology-based mutation operator, termed the Functional Similarity-Based Protein Translocation Operator ( $$FS-PTO$$ ). This operator enhances collaboration between the canonical model and the GO-informed mutation strategy, thereby improving the algorithm’s performance. As far as we know, this is the initial effort to incorporate the biological characteristics of PPIs into both the problem formulation and the development of intricate perturbation strategies. We assess the effectiveness of the proposed multi-objective evolutionary algorithm through experiments conducted on two widely recognized PPI networks and two standard complex datasets provided by the Munich Information Center for Protein Sequences (MIPS). To further assess the robustness of our algorithm, we create artificial networks by introducing different noise levels into the original Saccharomyces cerevisiae (yeast) PPI networks. This allows us to evaluate how perturbations in protein interactions affect the algorithm’s performance compared to other approaches. The experimental results highlight that our algorithm outperforms several state-of-the-art methods in accurately identifying protein complexes. Moreover, the findings emphasize the substantial advantages of incorporating our heuristic perturbation operator, which significantly improves the quality of the detected complexes over other evolutionary algorithm-based methods.