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Abstract Prototypic receptors for human influenza viruses are N-glycans carrying α2,6-linked sialosides. Due to immune pressure, A/H3N2 influenza viruses have emerged with altered receptor specificities that bind α2,6-linked sialosides presented on extended N-acetyl-lactosamine (LacNAc) chains. Here, binding modes of such drifted hemagglutinin’s (HAs) are examined by chemoenzymatic synthesis of N-glycans having 13C-labeled monosaccharides at strategic positions. The labeled glycans are employed in 2D STD-1H by 13C-HSQC NMR experiments to pinpoint which monosaccharides of the extended LacNAc chain engage with evolutionarily distinct HAs. The NMR data in combination with computation and mutagenesis demonstrate that mutations distal to the receptor binding domain of recent HAs create an extended binding site that accommodates with the extended LacNAc chain. A fluorine containing sialoside is used as NMR probe to derive relative binding affinities and confirms the contribution of the extended LacNAc chain for binding.

The dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN) is an innate immune C-type lectin receptor that recognizes carbohydrate-based pathogen associated with molecular patterns of various bacteria, fungi, viruses and protozoa. Although a range of highly mannosylated glycoproteins have been shown to induce signaling via DC-SIGN, precise structure of the recognized oligosaccharide epitope is still unclear. Using the array of oligosaccharides related to selected fragments of main fungal antigenic polysaccharides we revealed a highly specific pentamannoside ligand of DC-SIGN, consisting of α-(1 → 2)-linked mannose chains with one inner α-(1 → 3)-linked unit. This structural motif is present in Candida albicans cell wall mannan and corresponds to its antigenic factors 4 and 13b. This epitope is not ubiquitous in other yeast species and may account for the species-specific nature of fungal recognition via DC-SIGN. The discovered highly specific oligosaccharide ligands of DC-SIGN are tractable tools for interdisciplinary investigations of mechanisms of fungal innate immunity and anti-Candida defense. Ligand- and receptor-based NMR data demonstrated the pentasaccharide-to-DC-SIGN interaction in solution and enabled the deciphering of the interaction topology.

The objective of this work is to analyze and compare AC microgrid (ACMG) solutions to introduce the topic to new researchers. The methodology used to achieve this goal is a systematic literature review using five questions: (1) How have ACMGs evolved in five years? (2) What are the standards for ACMGs? (3) What are the different schemes for connecting MGs to the utility grid? (4) What are the different control schemes in ACMGs? (5) What is an appropriate way to compare results when working with ACMGs? The articles were published in Q1/Q2 journals as based on either the Scimago Journal Rank (SJR) and/or the Journal Citation Report (JCR) between 2018 and 2022 and were from three databases: (1) Web of Science (WoS), (2) Scopus, and (3) IEEE Xplore. Publications not describing pure ACMGs, review papers, publications not related to the questions, and papers describing work that did not meet a quality assessment were excluded, resulting in 34 articles being included in this review. Results show: (1) the energy sources and AC bus nature of microgrids over five years, (2) the identification and quantification of cited standards for microgrids, (3) the pros and cons of different schemes for connecting an AC microgrid to the main grid, (4) the control schemes, classified in a hierarchical control structure, and (5) the simulation tools and experimental benches used in microgrids. Most studies considered a generic energy source and a low-voltage three-phase AC bus, 16 standards were found, and the most cited standard was IEEE Standard 1547. The most common connection scheme to the utility grid was a direct connection, most of the works proposed a modification to a hierarchical control system scheme, and the most common simulation tool was MATLAB. The preferred experimental setup consisted of parallel inverters for testing a control scheme, a prototype when proposing a power electronic system, and a laboratory microgrid for testing fault detection methods.