A Carbon-Balance Model of Stand Growth: A Derivation Employing the Pipe-Model Theory and the Self Thinning Rule

The pipe-model theory is used as a framework for the derivation of models describing the growth of average stem length, total basal area, and total volume of an even-aged, self-thinning, mono-species stand. Variations of the models are derived for two situations: (1) where the annual rates of substrate production and feeder-root turnover can be assumed constant over time, and (2) where these rates are expected to change over time, such as in polluted environments. The model describing the growth of stand volume for the first situation has been studied previously and shows good agreement with yield tables. Growth rate models applicable to individual trees are described and preferred over similar models derived previously by the author

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

Optimal function explains forest responses to global change

Plant responses to global changes in carbon dioxide (CO2), nitrogen, and water availability are critical to future atmospheric CO2 concentrations, hydrology, and hence climate. Our understanding of those responses is incomplete, however. Multiple-resource manipulation experiments and empirical observations have revealed a diversity of responses, as well as some consistent patterns. But vegetation models-currently dominated by complex numerical simulation models-have yet to achieve a consensus among their predicted responses, let alone offer a coherent explanation of the observed ones. Here we propose an alternative approach based on relatively simple optimization models (OMs). We highlight the results of three recent forest OMs, which together explain a remarkable range of observed forest responses to altered resource availability. We conclude that OMs now offer a simple yet powerful approach to predicting the responses of forests-and, potentially, other plant types-to global change. We recommend ways in which OMs could be developed further in this direction