Earlier and more uniform spring green-up linked to lower insect richness and biomass in temperate forests

Urbanization and agricultural intensification are considered the main causes of recent insect decline in temperate Europe, while direct climate warming effects are still ambiguous. Nonetheless, higher temperatures advance spring leaf emergence, which in turn may directly or indirectly affect insects. We therefore investigated how Sentinel-2-derived start of season (SOS) and its spatial variability (SV-SOS) are affected by spring temperature and whether these green-up variables can explain insect biomass and richness across a climate and land-use gradient in southern Germany. We found that the effects of both spring green-up variables on insect biomass and richness differed between land-use types, but were strongest in forests. Here, insect richness and biomass were higher with later green-up (SOS) and higher SV-SOS. In turn, higher spring temperatures advanced SOS, while SV-SOS was lower at warmer sites. We conclude that with a warming climate, insect biomass and richness in forests may be affected negatively due to earlier and more uniform green-up. Promising adaptation strategies should therefore focus on spatial variability in green-up in forests, thus plant species and structural diversity.

Realizing a deep reinforcement learning agent for real-time quantum feedback

Realizing the full potential of quantum technologies requires precise real-time control on time scales much shorter than the coherence time. Model-free reinforcement learning promises to discover efficient feedback strategies from scratch without relying on a description of the quantum system. However, developing and training a reinforcement learning agent able to operate in real-time using feedback has been an open challenge. Here, we have implemented such an agent for a single qubit as a sub-microsecond-latency neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit and train the agent based solely on measurements. Our work is a first step towards adoption of reinforcement learning for the control of quantum devices and more generally any physical device requiring low-latency feedback.

grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis

Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. Here we present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.

Moonlighting chromatin: when DNA escapes nuclear control

Extracellular chromatin, for example in the form of neutrophil extracellular traps (NETs), is an important element that propels the pathological progression of a plethora of diseases. DNA drives the interferon system, serves as autoantigen, and forms the extracellular scaffold for proteins of the innate immune system. An insufficient clearance of extruded chromatin after the release of DNA from the nucleus into the extracellular milieu can perform a secret task of moonlighting in immune-inflammatory and occlusive disorders. Here, we discuss (I) the cellular events involved in the extracellular release of chromatin and NET formation, (II) the devastating consequence of a dysregulated NET formation, and (III) the imbalance between NET formation and clearance. We include the role of NET formation in the occlusion of vessels and ducts, in lung disease, in autoimmune diseases, in chronic oral disorders, in cancer, in the formation of adhesions, and in traumatic spinal cord injury. To develop effective therapies, it is of utmost importance to target pathways that cause decondensation of chromatin during exaggerated NET formation and aggregation. Alternatively, therapies that support the clearance of extracellular chromatin are conceivable.

Protective mucosal immunity against SARS-CoV-2 after heterologous systemic prime-mucosal boost immunization

Several effective SARS-CoV-2 vaccines are currently in use, but effective boosters are needed to maintain or increase immunity due to waning responses and the emergence of novel variants. Here we report that intranasal vaccinations with adenovirus 5 and 19a vectored vaccines following a systemic plasmid DNA or mRNA priming result in systemic and mucosal immunity in mice. In contrast to two intramuscular applications of an mRNA vaccine, intranasal boosts with adenoviral vectors induce high levels of mucosal IgA and lung-resident memory T cells (TRM); mucosal neutralization of virus variants of concern is also enhanced. The mRNA prime provokes a comprehensive T cell response consisting of circulating and lung TRM after the boost, while the plasmid DNA prime induces mostly mucosal T cells. Concomitantly, the intranasal boost strategies lead to complete protection against a SARS-CoV-2 infection in mice. Our data thus suggest that mucosal booster immunizations after mRNA priming is a promising approach to establish mucosal immunity in addition to systemic responses.

Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies

Responding quickly to unknown pathogens is crucial to stop uncontrolled spread of diseases that lead to epidemics, such as the novel coronavirus, and to keep protective measures at a level that causes as little social and economic harm as possible. This can be achieved through computational approaches that significantly speed up drug discovery. A powerful approach is to restrict the search to existing drugs through drug repurposing, which can vastly accelerate the usually long approval process. In this Review, we examine a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources. Furthermore, we compare drug candidates predicted by computational methods to drugs being assessed by clinical trials. Finally, we discuss lessons learned from the reviewed research efforts, including how to successfully connect computational approaches with experimental studies, and propose a unified drug repurposing strategy for better preparedness in the case of future outbreaks.