The genomic potential of photosynthesis in piconanoplankton is functionally redundant but taxonomically structured at a global scale.

Carbon fixation is a key metabolic function shaping marine life, but the underlying taxonomic and functional diversity involved is only partially understood. Using metagenomic resources targeted at marine piconanoplankton, we provide a reproducible machine learning framework to derive the potential biogeography of genomic functions through the multi-output regression of gene read counts on environmental climatologies. Leveraging the Marine Atlas of Tara Oceans Unigenes, we investigate the genomic potential of primary production in the global ocean.

Life cycle strategies in free-living unicellular eukaryotes: Diversity, evolution, and current molecular tools to unravel the private life of microorganisms.

An astonishing range of morphologies and life strategies has arisen across the vast diversity of protists, allowing them to thrive in most environments. In model protists, like Tetrahymena, Dictyostelium, or Trypanosoma, life cycles involving multiple life stages with different morphologies have been well characterized. In contrast, knowledge of the life cycles of free-living protists, which primarily consist of uncultivated environmental lineages, remains largely fragmentary.

SENSE-PPI reconstructs interactomes within, across, and between species at the genome scale.

computational reconstructions of protein-protein interaction (PPI) networks will provide invaluable insights into cellular systems, enabling the discovery of novel molecular interactions and elucidating biological mechanisms within and between organisms. Leveraging the latest generation protein language models and recurrent neural networks, we present SENSE-PPI, a sequence-based deep learning model that efficiently reconstructs PPIs, distinguishing partners among tens of thousands of proteins and identifying specific interactions within functionally similar proteins. SENSE-PPI demonstrates high accuracy, limited training requirements, and versatility in cross-species predictions, even with non-model organisms and human-virus interactions.

Digital nature: Unveiling the impact and safety of FlowVR group intervention for depression in a feasibility trial.

Objective: This study addresses the limitations of existing interventions for depression, such as a deficit-oriented focus, overlooking the utilization of positive elements such as nature, and neglecting the incorporation of group effects. The present feasibility study examines FlowVR, a resource-oriented, nature-inspired virtual reality (VR)-based group therapy. Previously tested individually in a pilot study for non-clinical participants, FlowVR has demonstrated positive effects on depressive symptoms.

Effects of Two COVID-19 Lockdowns on HbA1c Levels in Patients with Type 1 Diabetes and Associations with Digital Treatment, Health Literacy, and Diabetes Self-Management: A Multicenter, Observational Cohort Study Over 3 Years.

Introduction: Short-term studies reported improved glycemic control and a decrease in eHbA1c (estimated hemoglobin A1c) in patients with type 1 diabetes during COVID-19 lockdown, but long-term changes are unknown. Therefore, the main objectives are to (1) analyze whether laboratory-measured HbA1c changed during and after two lockdowns and (2) investigate potential variables influencing HbA1c change.

Methods: In this cohort study, 291 adults with type 1 diabetes were followed over 3 years including the prepandemic phase and two lockdowns.