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Low-mass (<1.2 Msun) main-sequence stars lose angular momentum over time, leading to a decrease in their magnetic activity. The details of this rotation–activity relation remain poorly understood, however. Using observations of members of the ≈700 Myr old Praesepe and Hyades open clusters, we aim to characterize the rotation–activity relation for different tracers of activity at this age. To complement published data, we obtained new optical spectra for 250 Praesepe stars, new X-ray detections for 10, and new rotation periods for 28. These numbers for Hyads are 131, 23, and 137, respectively. The latter increases the number of Hyads with periods by 50%. We used these data to measure the fractional Hα and X-ray luminosities, LHα/Lbol and LX/Lbol, and to calculate Rossby numbers Ro. We found that at ≈700 Myr almost all M dwarfs exhibit Hα emission, with binaries having the same overall color–Hα equivalent width distribution as single stars. In the Ro–LHα/Lbol plane, unsaturated single stars follow a power law with index β = −5.9 ± 0.8 for Ro > 0.3. In the Ro–LX/Lbol plane, we see evidence for supersaturation for single stars with R < 0.01, following a power law with index βsup = 0.5(+0.2,-0.1) supporting the hypothesis that the coronae of these stars are being centrifugally stripped. We found that the critical Ro value at which activity saturates is smaller for LX/Lbol than for LHα/Lbol. Finally, we observed an almost 1:1 relation between LHα/Lbol and LX/Lbol, suggesting that both the corona and the chromosphere experience similar magnetic heating. 

Parkinson’s disease (PD) is a movement disorder caused by a dopamine deficit in the brain. Current therapies primarily focus on dopamine modulators or replacements, such as levodopa. Although dopamine replacement can help alleviate PD symptoms, therapies targeting the underlying neurodegenerative process are limited. The study objective was to use artificial intelligence to rank the most promising repurposed drug candidates for PD. Natural language processing (NLP) techniques were used to extract text relationships from 33+ million biomedical journal articles from PubMed and map relationships between genes, proteins, drugs, diseases, etc., into a knowledge graph. Cross-domain text mining, hub network analysis, and unsupervised learning rank aggregation were performed in SemNet 2.0 to predict the most relevant drug candidates to levodopa and PD using relevance-based HeteSim scores. The top predicted adjuvant PD therapies included ebastine, an antihistamine for perennial allergic rhinitis; levocetirizine, another antihistamine; vancomycin, a powerful antibiotic; captopril, an angiotensin-converting enzyme (ACE) inhibitor; and neramexane, an N-methyl-D-aspartate (NMDA) receptor agonist. Cross-domain text mining predicted that antihistamines exhibit the capacity to synergistically alleviate Parkinsonian symptoms when used with dopamine modulators like levodopa or levodopa–carbidopa. The relationship patterns among the identified adjuvant candidates suggest that the likely therapeutic mechanism(s) of action of antihistamines for combatting the multi-factorial PD pathology include counteracting oxidative stress, amending the balance of neurotransmitters, and decreasing the proliferation of inflammatory mediators. Finally, cross-domain text mining interestingly predicted a strong relationship between PD and liver disease.