Intra-annual oxygen isotopes in the tree rings record precipitation extremes and water reservoir levels in the Metropolitan Area of São Paulo, Brazil

The impacts of climate change on precipitation and the growing demand for water have increased the water risks worldwide. Water scarcity is one of the main challenges of the 21st century, and the assessment of water risks is only possible from spatially distributed records of historical climate and levels of water reservoirs. One potential method to assess water supply is the reconstruction of oxygen isotopes in rainfall. We here investigated the use of tree-ring stable isotopes in urban trees to assess spatial/temporal variation in precipitation and level of water reservoirs. We analyzed the intra-annual variation of δ13C and δ18O in the tree rings of Tipuana tipu trees from northern and southern Metropolitan Area of São Paulo (MASP), Brazil. While variation in δ13C indicates low leaf-level enrichments from evapotranspiration, δ18O variation clearly reflects precipitation extremes. Tree-ring δ18O was highest during the 2014 drought, associated with the lowest historical reservoir levels in the city. The δ18O values from the middle of the tree rings have a strong association with the mid-summer precipitation (r = −0.71), similar to the association between the volume of precipitation and its δ18O signature (r = −0.76). These consistent results allowed us to test the association between tree-ring δ18O and water-level of the main reservoirs that supply the MASP. We observed a strong association between intra-annual tree-ring δ18O and the water-level of reservoirs in the northern and southern MASP (r = −0.94, r = −0.90, respectively). These results point to the potential use of high-resolution tree-ring stable isotopes to put precipitation extremes, and water supply, in a historical perspective assisting public policies related to water risks and climate change. The ability to record precipitation extremes, and previously reported capacity to record air pollution, place Tipuana tipu in a prominent position as a reliable environmental monitor for urban locations

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Transient non-stationarity and generalisation in deep reinforcement learning

Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is often not explicitly addressed in deep RL and a single neural network is continually updated. However, we find evidence that neural networks exhibit a memory effect, where these transient non-stationarities can permanently impact the latent representation and adversely affect generalisation performance. Consequently, to improve generalisation of deep RL agents, we propose Iterated Relearning (ITER). ITER augments standard RL training by repeated knowledge transfer of the current policy into a freshly initialised network, which thereby experiences less non-stationarity during training. Experimentally, we show that ITER improves performance on the challenging generalisation benchmarks ProcGen and Multiroom.Algorithmic

An Antibody to the aggregated synthetic prion protein peptide (PrP106-126) selectively recognizes disease-associated prion protein (PrPSc) from human brain specimens

Human prion diseases are characterized by the conversion of the normal host cellular prion protein (PrPC) into an abnormal misfolded form [disease‐associated prion protein (PrPSc)]. Antibodies that are capable of distinguishing between PrPC and PrPSc may prove to be useful, not only for the diagnosis of these diseases, but also for a better understanding of the molecular mechanisms involved in disease pathogenesis. In an attempt to produce such antibodies, we immunized mice with an aggregated peptide spanning amino acid residues 106 to 126 of human PrP (PrP106–126). We were able to isolate and single cell clone a hybridoma cell line (P1:1) which secreted an IgM isotype antibody [monoclonal antibody (mAb P1:1)] that recognized the aggregated, but not the monomeric form of the immunogen. When used in immunoprecipitation assays, the antibody did not recognize normal PrPC from non‐prion disease brain specimens, but did selectively immunoprecipitate full‐length PrPSc from cases of variant and sporadic Creutzfeldt–Jakob disease and Gerstmann–Straussler–Scheinker disease. These results suggest that P1:1 recognizes an epitope formed during the structural rearrangement or aggregation of the PrP that is common to the major PrPSc types found in the most common forms of human prion disease