We study the production and polarization of J/ψ mesons within a jet in proton-proton collisions at the LHC. We define the J/ψ-jet fragmentation function as a ratio of differential jet cross sections with and without the reconstructed J/ψ in the jet. We demonstrate that this is a very useful observable to help explore the J/ψ production mechanism, and to differentiate between different nonrelativistic QCD global fits based on inclusive J/ψ cross sections. Furthermore, we propose to measure the polarization of J/ψ mesons inside the jet, which can provide even more stringent constraints for the heavy quarkonium production mechanism.

We constructed a frequently updated, near-real-time global power generation dataset: CarbonMonitor-Power since January, 2016 at national levels with near-global coverage and hourly-to-daily time resolution. The data presented here are collected from 37 countries across all continents for eight source groups, including three types of fossil sources (coal, gas, and oil), nuclear energy and four groups of renewable energy sources (solar energy, wind energy, hydro energy and other renewables including biomass, geothermal, etc.). The global near-real-time power dataset shows the dynamics of the global power system, including its hourly, daily, weekly and seasonal patterns as influenced by daily periodical activities, weekends, seasonal cycles, regular and irregular events (i.e., holidays) and extreme events (i.e., the COVID-19 pandemic). The CarbonMonitor-Power dataset reveals that the COVID-19 pandemic caused strong disruptions in some countries (i.e., China and India), leading to a temporary or long-lasting shift to low carbon intensity, while it had only little impact in some other countries (i.e., Australia). This dataset offers a large range of opportunities for power-related scientific research and policy-making.

Bacterial symbioses allow annelids to colonise extreme ecological niches, such as hydrothermal vents and whale falls. Yet, the genetic principles sustaining these symbioses remain unclear. Here, we show that different genomic adaptations underpin the symbioses of phylogenetically related annelids with distinct nutritional strategies. Genome compaction and extensive gene losses distinguish the heterotrophic symbiosis of the bone-eating worm Osedax frankpressi from the chemoautotrophic symbiosis of deep-sea Vestimentifera. Osedax's endosymbionts complement many of the host's metabolic deficiencies, including the loss of pathways to recycle nitrogen and synthesise some amino acids. Osedax's endosymbionts possess the glyoxylate cycle, which could allow more efficient catabolism of bone-derived nutrients and the production of carbohydrates from fatty acids. Unlike in most Vestimentifera, innate immunity genes are reduced in O. frankpressi, which, however, has an expansion of matrix metalloproteases to digest collagen. Our study supports that distinct nutritional interactions influence host genome evolution differently in highly specialised symbioses.

The family Ampullariidae includes both aquatic and amphibious apple snails. They are an emerging model for evolutionary studies due to the high diversity, ancient history, and wide geographical distribution. Insight into drivers of ampullariid evolution is hampered, however, by the lack of genomic resources. Here, we report the genomes of four ampullariids spanning the Old World (Lanistes nyassanus) and New World (Pomacea canaliculata, P. maculata, and Marisa cornuarietis) clades. The ampullariid genomes have conserved ancient bilaterial karyotype features and a novel Hox gene cluster rearrangement, making them valuable in comparative genomic studies. They have expanded gene families related to environmental sensing and cellulose digestion, which may have facilitated some ampullarids to become notorious invasive pests. In the amphibious Pomacea, novel acquisition of an egg neurotoxin and a protein for making the calcareous eggshell may have been key adaptations enabling their transition from underwater to terrestrial egg deposition.

A polarized ep/eA collider (Electron-Ion Collider, or EIC), with polarized proton and light-ion beams and unpolarized heavy-ion beams with a variable center-of-mass energy s∼20 to ∼ 100 GeV (upgradable to ∼ 150 GeV) and a luminosity up to ∼ 10 34 cm-2s-1, would be uniquely suited to address several outstanding questions of Quantum Chromodynamics, and thereby lead to new qualitative and quantitative information on the microscopic structure of hadrons and nuclei. During this meeting at Jefferson Lab we addressed recent theoretical and experimental developments in the spin and the three-dimensional structure of the nucleon (sea quark and gluon spatial distributions, orbital motion, polarization, and their correlations). This mini-review contains a short update on progress in these areas since the EIC White paper (A. Accardi et al., Eur. Phys. J. A 52, 268 (2016)).

Background

Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.

Results

In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.

Conclusion

We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

Predictions made in Albacete et al. [Int. J. Mod. Phys. E 22 (2013) 1330007] prior to the LHC p+Pb run at sNN = 5 TeV are compared to currently available data. Some predictions shown here have been updated by including the same experimental cuts as the data. Some additional predictions are also presented, especially for quarkonia, that were provided to the experiments before the data were made public but were too late for the original publication.