Conflict over the age of the Universe

From Galactic Bars to the Hubble Tension: Weighing Up the Astrophysical Evidence for Milgromian Gravity

Astronomical observations reveal a major deficiency in our understanding of physics—the detectable mass is insufficient to explain the observed motions in a huge variety of systems given our current understanding of gravity, Einstein’s General theory of Relativity (GR). This missing gravity problem may indicate a breakdown of GR at low accelerations, as postulated by Milgromian dynamics (MOND). We review the MOND theory and its consequences, including in a cosmological context where we advocate a hybrid approach involving light sterile neutrinos to address MOND’s cluster-scale issues. We then test the novel predictions of MOND using evidence from galaxies, galaxy groups, galaxy clusters, and the large-scale structure of the universe. We also consider whether the standard cosmological paradigm (LCDM) can explain the observations and review several previously published highly significant falsifications of it. Our overall assessment considers both the extent to which the data agree with each theory and how much flexibility each has when accommodating the data, with the gold standard being a clear a priori prediction not informed by the data in question. Our conclusion is that MOND is favoured by a wealth of data across a huge range of astrophysical scales, ranging from the kpc scales of galactic bars to the Gpc scale of the local supervoid and the Hubble tension, which is alleviated in MOND through enhanced cosmic variance. We also consider several future tests, mostly at scales much smaller than galaxies.

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning

Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning.

Baking a mass-spectrometry data PIE with McMC and simulated annealing: predicting protein post-translational modifications from integrated top-down and bottom-up data

MOTIVATION

Post-translational modifications are vital to the function of proteins, but are hard to study, especially since several modified isoforms of a protein may be present simultaneously. Mass spectrometers are a great tool for investigating modified proteins, but the data they provide is often incomplete, ambiguous and difficult to interpret. Combining data from multiple experimental techniques-especially bottom-up and top-down mass spectrometry-provides complementary information. When integrated with background knowledge this allows a human expert to interpret what modifications are present and where on a protein they are located. However, the process is arduous and for high-throughput applications needs to be automated.

RESULTS

This article explores a data integration methodology based on Markov chain Monte Carlo and simulated annealing. Our software, the Protein Inference Engine (the PIE) applies these algorithms using a modular approach, allowing multiple types of data to be considered simultaneously and for new data types to be added as needed. Even for complicated data representing multiple modifications and several isoforms, the PIE generates accurate modification predictions, including location. When applied to experimental data collected on the L7/L12 ribosomal protein the PIE was able to make predictions consistent with manual interpretation for several different L7/L12 isoforms using a combination of bottom-up data with experimentally identified intact masses.

AVAILABILITY

Software, demo projects and source can be downloaded from http://pie.giddingslab.org/

Globular clusters, Hipparcos, and the age of the galaxy

We discuss the impact of the results from the recent Hipparcos astrometric satellite on distance estimates of galactic globular clusters. Recalibrating the clusters not only implies a relatively small change in the distance to the Large Magellanic Cloud, and hence a rescaling of several estimates of the Hubble constant, but also leads to significantly younger cluster ages. Although the data are not yet conclusive, the results so far point to a likely resolution of the apparent paradox of a universe younger than its constituents, without requiring significant modifications to simple cosmological models.