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This replication package provides all the publicly available data, code, and instructions required to reproduce the main empirical results of the above-mentioned research paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.

A dynamic protein network at the leading edge of motile cells is needed to coordinate events required for efficient cell motility. Previous work has shown that the Ser/Thr kinase DYRK3 affects the assembly of this network, and phosphorylates its component Liprin-α1, a scaffold protein regulating adhesion turnover and cell motility. We have looked for phospho-sites of Liprin-α1 relevant for the regulation of cell motility, by examining the role played by serine/threonine residues phosphorylated within the intrinsically disordered regions of Liprin-α1. Phospho-null mutations within either the amino-terminal or the carboxy-terminal disordered regions affect Liprin-α1 phosphorylation induced by DYRK3. Functional analysis shows that mutations within the amino-terminal region do not affect cell motility, while a set of carboxy-terminal mutations reduces the positive effects of Liprin-α1 on cell spreading on the extracellular matrix. Among several candidate phospho-sites in this protein region, we identify Thr701 as one of the potential main targets of DYRK3 activity in Liprin-1. The phospho-null mutation of Thr701 specifically inhibits Liprin-1–induced potentiation of cell spreading on fibronectin. Our findings contribute to highlight the complexity of the regulation of Liprin-αprotein functions by phosphorylation/dephosphorylation events. Given the involvement of Liprin-α proteins in tumor cell motility and invasion, in-depth understanding of this regulatory complexity may highlight new possibilities for therapeutic intervention.

mRNA-sequencing raw data. Method: We collected algae after 72 h exposure to 10 mg/L AuNP and AuNS for RNA-seq to analyze mRNA expression. Chlamydomonas_reinhardtii (Version: CC-503 cw92 mt+). The differentially expressed transcripts and genes were selected with log2 (fold change) ≥ 1 or log2 (fold change) ≤ -1 and p value < 0.05 criteria with the R package edgeR(https://bioconductor.org/packages/edgeR). Results: RNA sequencing identified 9 upregulated and 38 downregulated differentially expressed genes (DEGs) in the 10 mg/L AuNP treated cells, impairing photosynthesis and energy storage via the photosystem II subunit S1 (PSBS1)/ early light-inducible protein (ELI3) pathway. In contrast, the AuNS group exhibits 246 upregulated and 145 downregulated DEGs, affecting membrane integrity and nitrogen metabolism through the nitrate reductase (NIT1)/ aminomethyl transferase (AMT1)/ protein kinase domain-containing protein (A0A2K3CRU5) pathway.

SOURCE DATA: Data from the manuscript of Springstein et al., "Repurposing of a DNA segregation machinery into a cytoskeletal system controlling cell shape”. - Alignments/ - Cyanobacteria_species.aln Concatenation of the multiple sequence alignments of the RNA polymerase β and β′ subunit protein sequences from all Cyanobacteria species in the dataset, used to reconstruct a species phylogeny of the Cyanobacteria phylum. - ParM_Bacteria.aln Multiple sequence alignment of representative ParM proteins identified across all bacterial phyla. - ParM_ParR_Cyanobacteria.aln Concatenation of the multiple sequence alignments of ParM and ParR protein sequences identified in Cyanobacteria. - Phylogenies/ - Cyanobacteria_species.treefile Species phylogeny of Cyanobacteria (in Newick format). - Cyanobacteria_species.pdf Species phylogeny of Cyanobacteria (in pdf format with annotations). - ParM_Bacteria.treefile Phylogeny of ParM proteins identified across all bacterial phyla (in Newick format). - ParM_Bacteria.pdf Phylogeny of ParM proteins identified across all bacterial phyla (in pdf format with annotations). - ParM_ParR_Cyanobacteria.treefiles Phylogeny of the concatenation of ParM and ParR proteins identified in Cyanobacteria (in Newick format). - ParM_ParR_Cyanobacteria.pdf Phylogeny of the concatenation of ParM and ParR proteins identified in Cyanobacteria (in pdf format with annotations). - Profiles/ - RNApolB.hmm HMM profile used for the identification of RNA polymerase β proteins in the Cyanobacteria genomes. - RNApolBp.hmm HMM profile used for the identification of RNA polymerase β′ proteins in the Cyanobacteria genomes. - ParM.hmm HMM profile used for the identification of ParM proteins in bacterial genomes. - ParR.hmm HMM profile used for the identification of ParR proteins in bacterial genomes.

The data come from NCBI BioProject PRJNA1088471, as originally analyzed in Overton et. al (2024). These data are short read sequences from wastewater in Toronto, Ontario. See Overton et. al (2024) for a detailed description of the genomic sequencing process. Data processing involved alignment of the short reads to the Wuhan-1 reference sequence (NC_045512) with `minimap2` v2.28, identifying the mutations relative to the reference, and recording the number of times a mutation was observed (counts) and the depth of coverage. The frequency is calculated as the counts divided by the coverage. The mutation pre-processing pipeline is available at \url{https://github.com/DASL-Lab/data-treatment-plant}, and heavily relies on the GromStole pipeline (\url{https://github.com/PoonLab/gromstole}). After being processed into counts and coverage, the data were filtered to only include mutations that are relevant to analysis. There were many mutations with either consistently low counts (possibly due to sequencing errors) or low coverage. We found all mutations that had both a frequency of at least 0.1 and a frequency below 0.9 (with a coverage at least 40) at at least two time points during the study in any location. This ensures that we have all of the mutations that were potentially part of a circulating lineage without relying on lineage definitions. Overton, Alyssa K., Jennifer J. Knapp, Opeyemi U. Lawal, et al. “Genomic Surveillance of a Canadian Airport Wastewater Samples Allows Early Detection of Emerging SARS-CoV-2 Lineages.” Preprint, April 9, 2024. https://doi.org/10.21203/rs.3.rs-4183960/v1.

The development of AI, at an above-benchmark pace, has become a worldwide worrying issue and a central narrative in the tertiary level learning institutions. Digital transformation, on the other hand, is updating the educational systems, which necessitates effective governance to steer these improvements. This paper provides an integrated overview of these three pillars – AI, digital transformation, and governance – and explores their interplay in reshaping the landscape of higher education. Drawing on key insights from the EUNIS 2024 conference and recent global studies, the paper examines AI’s transformative potential in optimizing learning and administration, the role of digital transformation in enabling scalable and innovative educational services, and the urgent need for governance frameworks to ensure ethical, equitable, and sustainable practices. The analysis highlights current gaps, such as the lagging development of regulatory frameworks amid rapid tech progress, and it sheds light on emerging needs, barriers, and innovations (including the use of AI and XR in teaching and learning, strategies for digital readiness, and models for ethical leadership). The argument provided by AI, digital transformation, and governance is of particular interest, which justifies the need for a Global Compact on AI in education that would foster international collaboration and standards. This all-encompassing tracking and mapping of trends, challenges, and opportunities provides higher education institutions a blueprint for strategic decisionmaking concerning technological integration and guides policymakers in reforming higher learning education.

The shared dataset compiles measured air-changes-per-hour values in a high-altitude university in Quito, presented as an alternative mitigation strategy to maintaining 2 m of social distancing in classroom environments. These data enable meaningful comparison with findings from other regions, thereby supporting informed decision-making aimed at reducing infection risk.

Supplementary Materials for Mironova et al, Serum proteomics of insulin resistance disorders distinguish MASLD from lipodystrophy and insulin receptor defects Journal of Clinical Endocrinology & Metabolism, 2025 https://doi.org/10.1210/clinem/dgaf516

Postgraduate students awareness of the gender pay gap

Functional imaging using genetically encoded indicators, such as GCaMP, has become a foundational tool for in vivo experiments and allows for the analysis of cellular dynamics, sensory processing, and cellular communication. However, large scale or complex functional imaging experiments pose analytical challenges. Many programs have worked to create pipelines to address these challenges, however, most platforms require proprietary software, impose operational restrictions, offer limited outputs, or require significant knowledge of various programming languages, which collectively can limit utility. To address this, we designed MCA (a Multicellular Analysis toolkit) to work with ImageJ, a widely used open-source software which has been the standard image analysis platform for the last 30 years. We developed MCA to be visually intuitive, utilizing ImageJ’s platform to generate new images based on completed tasks so users can visually see each step in the analysis pipeline. In addition, MCA implements a user-friendly GUI providing a simple interface which resembles other native ImageJ plugins. We incorporated functionality for rigid registration to correct motion artifacts, algorithms for cell body prediction, and methods for annotating cells and exporting data. For cell prediction, we trained a custom model in Cellpose 2.0 for segmentation of nuclei expressing pan-neuronal nuclear localized GCaMP in zebrafish. We validated the accuracy of MCA output to previously published zebrafish calcium imaging data which elicited visually evoked neuronal responses. To show the versatility of MCA, we also show that our software can be utilized for multiple sensory modalities, brain regions, and multiple model organisms including Drosophila and mouse. Together these data show that MCA is viable for extracting calcium dynamics in a user-friendly environment for multiple forms of functional imaging.