Possibility of persistent current in S-states.

In this study, we investigate the profound impact of the Pöschl-Teller double-ring-shaped Coulomb (PTDRSC) potential to induce persistent currents within the S-states of the hydrogenic atom. The confinement of the system is achieved through an impenetrable spherical boundary. Leveraging first-order perturbation theory, we quantify the charge current across various states induced by the PTDRSC potential with its inherent angular and azimuthal dependence, leading to angular and azimuthal distortion, respectively. Notably, persistent currents are observed within S-states without external excitation mechanisms. The magnitude of the induced current is intricately linked to the strength of the PTDRSC potential parameters. These results underscore the prospect of manipulating persistent currents and their associated induced magnetic fields within S-states by tailoring the potential strength and confining boundary size. This discovery presents a compelling avenue for the controlled generation and experimental verification of induced S-state magnetism, opening new possibilities for innovative applications.

PHA4GE quality control contextual data tags: standardized annotations for sharing public health sequence datasets with known quality issues to facilitate testing and training.

As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower quality datasets be made available for analysis and comparison alongside those of higher quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse. The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability, and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies - community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The tags can also be applied to synthetic (lab created) data. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA's GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.

Octopamine receptor gene influences social grouping in the masked birch caterpillar.

OBJECTIVE: Group-living plays a key role in the success of many insects, but the mechanisms underlying group formation and maintenance are poorly understood. Here we use the masked birch caterpillar, Drepana arcuata, to explore genetic influences on social grouping. These larvae predictably transition from living in social groups to living solitarily during the 3rd instar of development. Our previous study showed a notable shift in the D. arcuata transcriptome that correlates with the transition from grouping to solitary behavior. We noted that one differentially regulated gene, octopamine receptor gene (DaOAR), is a prominent 'social' gene in other insect species, prompting us to test the hypothesis that DaOAR influences grouping behavior in D. arcuata. This was done using RNA interference (RNAi) methods by feeding second instar larvae synthetic dsRNAs. RESULTS: RT-qPCR analysis confirmed a significant reduction in DaOAR transcript abundance in dsRNA-fed larvae compared to controls. Behavioral trials showed that caterpillars with reduced transcript abundance of DaOAR remained solitary throughout the observation period compared to controls. These results provide evidence that regulation of the octopamine receptor gene influences social grouping in D. arcuata, and that specifically, a decrease in octopamine receptor expression triggers the larval transition from social to solitary.

Draft genome assembly and annotation of the masked birch caterpillar, Drepana arcuata (Lepidoptera: Drepanoidea).

The masked birch caterpillar, Drepana arcuata Walker (Lepidoptera: Drepanidae), and other Drepanoidea (Lepidoptera) species are excellent organisms for investigating the function and evolution of vibratory communication and sociality in caterpillars. We present a de novo assembled draft genome and functional annotation for D. arcuata, using a combination of short and long sequencing reads generated by Illumina HiSeq X and Oxford Nanopore Technologies (ONT) MinION sequencing platforms, respectively. A total of 460,694,612 150bp paired-end Illumina and 395,890 ONT raw reads were assembled into 11,493 scaffolds spanning a genome size of 270.5Mb. The resulting D. arcuata genome has a GC content of 38.79%, repeat content of 8.26%, is 86.5% complete based on Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment, and comprises 10,398 predicted protein-coding genes. These data represent the first genomic resources for the lepidopteran superfamily Drepanoidea. Although the order Lepidoptera comprises numerous ecologically and economically important species, assembled genomes and annotations are available for < 1% of the total species. These data can be further utilized for research on Lepidoptera genomics as well as on the function and evolution of vibratory communication and sociality in larval insects.

Transcriptome analysis of a social caterpillar, Drepana arcuata: De novo assembly, functional annotation and developmental analysis.

The masked birch caterpillar, Drepana arcuata, provides an excellent opportunity to study mechanisms mediating developmental changes in social behaviour. Larvae transition from being social to solitary during the 3rd instar, concomitant with shifts in their use of acoustic communication. In this study we characterize the transcriptome of D. arcuata to initiate sociogenomic research of this lepidopteran insect. We assembled and annotated the combined larval transcriptome of "social" early and "solitary" late instars using next generation Illumina sequencing, and used this transcriptome to conduct differential gene expression analysis of the two behavioural phenotypes. A total of 211,012,294 reads generated by RNA sequencing were assembled into 231,348 transcripts and 116,079 unigenes for the functional annotation of the transcriptome. Expression analysis revealed 3300 transcripts that were differentially expressed between early and late instars, with a large proportion associated with development and metabolic processes. We independently validated differential expression patterns of selected transcripts using RT-qPCR. The expression profiles of social and solitary larvae revealed differentially expressed transcripts coding for gene products that have been previously reported to influence social behaviour in other insects (e.g. cGMP- and cAMP- dependent kinases, and bioamine receptors). This study provides the first transcriptomic resources for a lepidopteran species belonging to the superfamily Drepanoidea, and gives insight into genetic factors mediating grouping behaviour in insects.