Bio-Intervention of Naturally Occurring Silicate Minerals for Alternative Source of Potassium: Challenges and Opportunities

Soil needs simultaneous replenishment of various nutrients to maintain its inherent fertility status under extensive cropping systems. Replenishing soil nutrients with commercial fertilizer is costly. Among various fertilizers, deposits of potassium (K) ore suitable for the production of commercial K fertilizer (KCl) are distributed in few northern hemisphere countries (Canada, Russia, Belarus, and Germany) which control more than 70% of the world's potash market. Naturally occurring minerals, particularly silicate minerals, could be used as a source of K, but not as satisfactorily as commercial K fertilizers. In this context, bio-intervention (in combination with microorganisms and/or composting) of silicate minerals has been found quite promising to improve plant K availability and assimilation. This is an energy efficient and environmentally friendly approach. Here we present a critical review of existing literature on direct application of silicate minerals as a source of K for plant nutrition as well as soil fertility enhancement by underpinning the bio-intervention strategies and related K solubilization mechanisms. An advancement of knowledge in this field will not only contribute to a better understanding of the complex natural processes of soil K fertility, but also help to develop a new approach to utilize natural mineral resources for sustainable and environmental friendly agricultural practices

Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: comparison of models

An evaluation of general models that describe gas production profiles is presented. The models are derived from first principles by considering a simple three-pool scheme and permit the extent of ruminal degradation to be calculated, as described in the companion paper. The models evaluated were the generalized Mitscherlich, simple Mitscherlich, generalized Michaelis–Menten, simple Michaelis–Menten, Gompertz, and logistic. Five sets of gas production data consisting of 216 curves, obtained using a wide range of feeds (including straw, hay, silage, grain and various byproducts), were analysed to study the performance of these gas production models. Application of the non-sigmoidal models (simple Mitscherlich and Michaelis–Menten) to the data resulted in convergence problems and these models were found to be inadequate in many cases. Based on results of a pairwise comparison between models (variance ratio test), ranking of residual mean squares, lack-of-fit test, and of analyses of residuals, the generalized Mitscherlich and the generalized Michaelis–Menten models seemed particularly suited because of their flexibility to encompass sigmoidal and non-sigmoidal shapes of gas production profiles, whether symmetrical or not

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

Can Extensive Reticulation and Concerted Evolution Result in a Cladistically Structured Molecular Data Set?

Hierarchy is the main criterion for informativeness in a data set, even if no explicit reference to evolution as a causal process is provided. Sequence data (nuclear ribosomal DNA ITS) from Armeria (Plumbaginaceae) contains a certain amount of hierarchical structure as suggested by data decisiveness (DD) and distribution of tree lengths (DTL). However, ancillary evidence suggests that extensive gene flow and biased concerted evolution in these multi-copy regions have significantly shaped the ITS data set. This argument is discussed using parsimony analysis of four data sets, constructed by combining wild sequences with those from different generations of artificial hybrids (wild + F1, F2, and backcrosses; wild + backcrosses; wild + F1; wild + F2). As compared to the F1 hybrids, F2 show a certain degree of homogenization in polymorphic sites. This effect reduces topological disruption caused by F1 and is considered to be illustrative of how extensive gene flow and biased concerted evolution may have modeled the wild ITS data. The possibility that hierarchy has arisen as a result of ⎯or despite a significant contribution from⎯ those two such potentially perturbing forces raises the question of what kind of signal are we recovering from this molecular data set.This work has been supported by Grants DGICYT PB94-0110 and DGES PB97-1146 of the Spanish Dirección General de Enseñanza Superior e Investigación CientíficaPeer reviewe