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This study employs physics-informed neural networks (PINNs) to reconstruct multiple flow fields in a transient natural convection system solely based on instantaneous temperature data at an arbitrary moment. Transient convection problems present reconstruction challenges due to the temporal variability of fields across different flow phases. In general, large reconstruction errors are observed during the incipient phase, while the quasi-steady phase exhibits relatively smaller errors, reduced by a factor of 2–4. We hypothesize that reconstruction errors vary across different flow phases due to the changing solution space of a PINN, inferred from the temporal gradients of the fields. Furthermore, we find that reconstruction errors tend to accumulate in regions where the spatial gradients are smaller than the order of 10−6, likely due to the vanishing gradient phenomenon. In convection phenomena, field variations often manifest across multiple scales in space. However, PINN-based reconstruction tends to preserve larger-scale variations, while smaller-scale variations become less pronounced due to the vanishing gradient problem. To mitigate the errors associated with vanishing gradients, we introduce a multi-scale approach that determines scaling constants for the PINN inputs and reformulates inputs across multiple scales. This approach improves the maximum and mean errors by 72.2% and 6.4%, respectively. Our research provides insight into the behavior of PINNs when applied to transient convection problems with large solution space and field variations across multiple scales. 

Hypoxic-Ischemic Encephalopathy (HIE) in the brain is the leading cause of morbidity and mortality in neonates and can lead to irreparable tissue damage and cognition. Thus, investigating key mediators of the HI response to identify points of therapeutic intervention has significant clinical potential. Brain repair after HI requires highly coordinated injury responses mediated by cell-derived extracellular vesicles (EVs). Studies show that stem cell-derived EVs attenuate the injury response in ischemic models by releasing neuroprotective, neurogenic, and anti-inflammatory factors. In contrast to 2D cell cultures, we successfully isolated and characterized EVs from whole brain rat tissue (BEV) to study the therapeutic potential of endogenous EVs. We showed that BEVs decrease cytotoxicity in an ex vivo oxygen glucose deprivation (OGD) brain slice model of HI in a dose- and time-dependent manner. The minimum therapeutic dosage was determined to be 25 μg BEVs with a therapeutic application time window of 4–24 h post-injury. At this therapeutic dosage, BEV treatment increased anti-inflammatory cytokine expression. The morphology of microglia was also observed to shift from an amoeboid, inflammatory phenotype to a restorative, anti-inflammatory phenotype between 24–48 h of BEV exposure after OGD injury, indicating a shift in phenotype following BEV treatment. These results demonstrate the use of OWH brain slices to facilitate understanding of BEV activity and therapeutic potential in complex brain pathologies for treating neurological injury in neonates. 

Adaptive radiation is an important mechanism of organismal diversification and can be triggered by new ecological opportunities. Although poorly studied in this regard, parasites are an ideal group in which to study adaptive radiations because of their close associations with host species. Both experimental and comparative studies suggest that the ectoparasitic wing lice of pigeons and doves have adaptively radiated, leading to differences in body size and overall coloration. Here, we show that long-distance dispersal by dove hosts was central to parasite diversification because it provided new ecological opportunities for parasites to speciate after host-switching. We further show that among extant parasite lineages host-switching decreased over time, with cospeciation becoming the more dominant mode of parasite speciation. Taken together, our results suggest that host dispersal, followed by host-switching, provided novel ecological opportunities that facilitated adaptive radiation by parasites. 

Green plants (Viridiplantae) include around 450,000–500,000 species of great diversity and have important roles in terrestrial and aquatic ecosystems. Here, as part of the One Thousand Plant Transcriptomes Initiative, we sequenced the vegetative transcriptomes of 1,124 species that span the diversity of plants in a broad sense (Archaeplastida), including green plants (Viridiplantae), glaucophytes (Glaucophyta) and red algae (Rhodophyta). Our analysis provides a robust phylogenomic framework for examining the evolution of green plants. Most inferred species relationships are well supported across multiple species tree and supermatrix analyses, but discordance among plastid and nuclear gene trees at a few important nodes highlights the complexity of plant genome evolution, including polyploidy, periods of rapid speciation, and extinction. Incomplete sorting of ancestral variation, polyploidization and massive expansions of gene families punctuate the evolutionary history of green plants. Notably, we find that large expansions of gene families preceded the origins of green plants, land plants and vascular plants, whereas whole-genome duplications are inferred to have occurred repeatedly throughout the evolution of flowering plants and ferns. The increasing availability of high-quality plant genome sequences and advances in functional genomics are enabling research on genome evolution across the green tree of life.