Natural polysaccharides (such as cellulose) comprise a large bio-renewable resource. However, exploitation of this resource requires energy-efficient polysaccharide degradation, which is currently limited by the inherent recalcitrance of many naturally occurring polysaccharides. Catalytic breakdown of polysaccharides can be achieved more efficiently by means of the enzymes lytic polysaccharide monooxygenases (LPMOs). However, the LPMO mechanism has remained controversial, preventing full exploitation of their potential. One of the controversies has centered around an active site tyrosine, present in most LPMOs. Different roles for this tyrosine have been proposed without direct evidence, but two recent investigations have for the first time obtained direct (spectroscopic) evidence for that chemical modification of this tyrosine is possible. Surprisingly, the spectroscopic features obtained in the two investigations are remarkably different. In this paper we use density functional theory (DFT) in a QM/MM formulation to reconcile these (apparently) conflicting results. By modeling the spectroscopy as well as the underlying reaction mechanism we can show how formation of two isomers (both involving deprotonation of tyrosine) explain the difference in the experimental observed spectroscopic features. The link between our structures and the observed spectroscopy provides a firm ground to investigate the role of tyrosine.

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The criteria for measuring soil compaction parameters, such as optimum moisture content and maximum dry density, play an important role in construction projects. On construction sites, base/sub-base soils are compacted at the optimal moisture content to achieve the desirable level of compaction, generally between 95% and 98% of the maximum dry density. The present technique of determining compaction parameters in the laboratory is a time-consuming task. This study proposes an improved hybrid intelligence paradigm as an alternative tool to the laboratory method for estimating the optimum moisture content and maximum dry density of soils. For this purpose, an advanced version of the grey wolf optimiser (GWO) called improved GWO (IGWO) was integrated with an adaptive neuro-fuzzy inference system (ANFIS), which resulted in a high-performance hybrid model named ANFIS-IGWO. Overall, the results indicate that the proposed ANFIS-IGWO model achieved the most precise prediction of the optimum moisture content (degree of correlation = 0.9203 and root mean square error = 0.0635) and maximum dry density (degree of correlation = 0.9050 and root mean square error = 0.0709) of soils. The outcomes of the suggested model are noticeably superior to those attained by other hybrid ANFIS models, which are built with standard GWO, Moth-flame optimisation, slime mould algorithm, and marine predators algorithm. The results indicate that geotechnical engineers can benefit from the newly developed ANFIS-IGWO model during the design stage of civil engineering projects. The developed MATLAB models are also included for determining soil compaction parameters.

Hypothyroidism is a common disorder of small ruminants and is expected to alter the pharmacokinetics of drugs. Hypothyroidism was induced by feeding thiourea at the dose rate 50 mg.kg-1 daily for 28 days to goats. Disposition of lincomycin, after intravenous administration at dose rate 10 mg/kg, was investigated in hypothyroid goats to determine the potential dosage regimen against susceptible microorganisms. Blood samples were collected from 1 min to 24 h of drug administration. The drug was detected in plasma up to 8 h and lincomycin was rapidly distributed from blood to the tissue, as evidenced by the high value of the distribution coefficient (mean ± SEM) 12.3±1.09 h-1. The large Vd (1.78±0.18 L/kg) indicated vast tissue distribution of lincomycin in goats. The elimination half life, AUC and total body clearance were 3.99± 0.25 h, 33.2±1.71 ìg.h/mL and 0.31±0.02 L/h/kg, respectively. Based on results, lincomycin in hypothyroid goats is suggested to be repeated at 12 h interval for organisms sensitive to lincomycin having MIC up to 0.1 µg.ml-1.

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