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Single-particle tracking (SPT) is a powerful technique for probing the diverse physical properties of the cytoplasm. Genetically encoded nanoparticles provide an especially convenient tool for such investigations, as they can be expressed and tracked in cells via fluorescence. Among these, 40-nm genetically encoded multimerics (GEMs) provide a unique opportunity to explore the cytoplasm. Their size corresponds to that of ribosomes and big protein complexes, allowing us to investigate the effects of the cytoplasm on the diffusivity of these objects while excluding the influence of chemical interactions during stressful events and pathological conditions. However, the effects of GEM expression levels on the measured cytoplasmic diffusivity remain largely uncharacterized in mammalian cells. To optimize the GEMs tracking and assess expression level effects, we developed a doxycycline-inducible GEM expression system and compared it with a previously reported constitutive expression system. The inducible GEM expression system reduced the number of GEM particles from 2000 to as low as 5–500 per average 2D cell cytoplasmic area, depending on doxycycline concentration and incubation time. This optimization enabled adjustment of particle density for imaging and improved homogeneity across the cell population. Moreover, we enhanced the analysis of GEM diffusivity by incorporating an effective diffusion coefficient that accounts for the type of motion and by quantifying motion heterogeneity through standard deviations of particle displacements within and between cells. 

In 2020, the White House released the “Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset,” wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on the graph mining and transformer models. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover ongoing research findings such as the relationship between COVID-19 and oxytocin hormone.All code, details, and pre-trained models are available at https://github.com/IlyaTyagin/AGATHA-C-GP. 

Medical research is risky and expensive. Drug discovery requires researchers to efficiently winnow thousands of potential targets to a small candidate set. However, scientists spend significant time and money long before seeing the intermediate results that ultimately determine this smaller set. Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions.We present AGATHA, a deep-learning hypothesis generation system that learns a data-driven ranking criteria to recommend new biomedical connections. We massively validate our system with a temporal holdout wherein we predict connections first introduced after 2015 using data published beforehand. We additionally explore biomedical sub-domains, and demonstrate AGATHA’s predictive capacity across the twenty most popular relationship types. Furthermore, we perform an ablation study to examine the aspects of our semantic network that most contribute to recommendation quality. Overall, AGATHA achieves best-in-class recommendation quality when compared to other hypothesis generation systems built to predict across all available biomedical literature. Reproducibility: All code, experimental data, and pre-trained models are available online: sybrandt.com/2020/agatha. 

Abstract Background Deer mice (genus Peromyscus ) are the most common rodents in North America. Despite the availability of reference genomes for some species, a comprehensive database of polymorphisms, especially in those maintained as living stocks and distributed to academic investigators, is missing. In the present study we surveyed two populations of P. maniculatus that are maintained at the Peromyscus Genetic Stock Center (PGSC) for polymorphisms across their 2.5 × 10 9 bp genome. Results High density of variation was identified, corresponding to one SNP every 55 bp for the high altitude stock (SM2) or 207 bp for the low altitude stock (BW) using snpEff (v4.3). Indels were detected every 1157 bp for BW or 311 bp for SM2. The average Watterson estimator for the BW and SM2 populations is 248813.70388 and 869071.7671 respectively. Some differences in the distribution of missense, nonsense and silent mutations were identified between the stocks, as well as polymorphisms in genes associated with inflammation (NFATC2), hypoxia (HIF1a) and cholesterol metabolism (INSIG1) and may possess value in modeling pathology. Conclusions This genomic resource, in combination with the availability of P. maniculatus from the PGSC, is expected to promote genetic and genomic studies with this animal model.