Process Simulation and Control Optimization of a Blast Furnace Using Classical Thermodynamics Combined to a Direct Search Algorithm

Several numerical approaches have been proposed in the literature to simulate the behavior of modern blast furnaces: finite volume methods, data-mining models, heat and mass balance models, and classical thermodynamic simulations. Despite this, there is actually no efficient method for evaluating quickly optimal operating parameters of a blast furnace as a function of the iron ore composition, which takes into account all potential chemical reactions that could occur in the system. In the current study, we propose a global simulation strategy of a blast furnace, the 5-unit process simulation. It is based on classical thermodynamic calculations coupled to a direct search algorithm to optimize process parameters. These parameters include the minimum required metallurgical coke consumption as well as the optimal blast chemical composition and the total charge that simultaneously satisfy the overall heat and mass balances of the system. Moreover, a Gibbs free energy function for metallurgical coke is parameterized in the current study and used to fine-tune the simulation of the blast furnace. Optimal operating conditions and predicted output stream properties calculated by the proposed thermodynamic simulation strategy are compared with reference data found in the literature and have proven the validity and high precision of this simulation

Host preferences and differential contributions of deciduous tree species shape mycorrhizal species richness in a mixed Central European forest

Mycorrhizal species richness and host ranges were investigated in mixed deciduous stands composed of Fagus sylvatica, Tilia spp., Carpinus betulus, Acer spp., and Fraxinus excelsior. Acer and Fraxinus were colonized by arbuscular mycorrhizas and contributed 5% to total stand mycorrhizal fungal species richness. Tilia hosted similar and Carpinus half the number of ectomycorrhizal (EM) fungal taxa compared with Fagus (75 putative taxa). The relative abundance of the host tree the EM fungal richness decreased in the order Fagus > Tilia >> Carpinus. After correction for similar sampling intensities, EM fungal species richness of Carpinus was still about 30–40% lower than that of Fagus and Tilia. About 10% of the mycorrhizal species were shared among the EM forming trees; 29% were associated with two host tree species and 61% with only one of the hosts. The latter group consisted mainly of rare EM fungal species colonizing about 20% of the root tips and included known specialists but also putative non-host associations such as conifer or shrub mycorrhizas. Our data indicate that EM fungal species richness was associated with tree identity and suggest that Fagus secures EM fungal diversity in an ecosystem since it shared more common EM fungi with Tilia and Carpinus than the latter two among each other

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Lessons from non-canonical splicing

Recent improvements in experimental and computational techniques that are used to study the transcriptome have enabled an unprecedented view of RNA processing, revealing many previously unknown non-canonical splicing events. This includes cryptic events located far from the currently annotated exons and unconventional splicing mechanisms that have important roles in regulating gene expression. These non-canonical splicing events are a major source of newly emerging transcripts during evolution, especially when they involve sequences derived from transposable elements. They are therefore under precise regulation and quality control, which minimizes their potential to disrupt gene expression. We explain how non-canonical splicing can lead to aberrant transcripts that cause many diseases, and also how it can be exploited for new therapeutic strategies