Review on catalytic cleavage of C-C inter-unit linkages in lignin model compounds: Towards lignin depolymerisation

Lignin depolymerisation has received considerable attention recently due to the pressing need to find sustainable alternatives to fossil fuel feedstock to produce chemicals and fuels. Two types of interunit linkages (C–C and C–O linkages) link several aromatic units in the structure of lignin. Between these two inter-unit linkages, the bond energies of C–C linkages are higher than that of C–O linkages, making them harder to break. However, for an efficient lignin depolymerisation, both types of inter-unit linkages have to be broken. This is more relevant because of the fact that many delignification processes tend to result in the formation of additional C–C inter-unit bonds. Here we review the strategies reported for the cleavage of C–C inter-unit linkages in lignin model compounds and lignin. Although a number of articles are available on the cleavage of C–O inter-unit linkages, reports on the selective cleavage of C–C inter-unit linkages are relatively less. Oxidative cleavage, hydrogenolysis, two-step redox-neutral process, microwave assisted cleavage, biocatalytic and photocatalytic methods have been reported for the breaking of C–C inter-unit linkages in lignin. Here we review all these methods in detail, focused only on the breaking of C–C linkages. The objective of this review is to motivate researchers to design new strategies to break this strong C–C inter-unit bonds to valorise lignins, technical lignins in particular

The role of acyl-coenzyme A carboxylase complex in lipstatin biosynthesis of Streptomyces toxytricini

Streptomyces toxytricini produces lipstatin, a specific inhibitor of pancreatic lipase, which is derived from two fatty acid moieties with eight and 14 carbon atoms. The pccB gene locus in 10.6 kb fragment of S. toxytricini chromosomal DNA contains three genes for acyl-coenzyme A carboxylase (ACCase) complex accA3, pccB, and pccE that are presumed to be involved in secondary metabolism. The pccB gene encoding a β subunit of ACCase [carboxyltransferase (CT)] was identified upstream of pccE gene for a small protein of ε subunit. The accA3 encoding the α subunit of ACCase [biotin carboxylase (BC)] was also identified downstream of pccB gene. When the pccB and pccE genes were inactivated by homologous recombination, the lipstatin production was reduced as much as 80%. In contrast, the accumulation of another compound, tetradeca-5.8-dienoic acid (the major lipstatin precursor), was 4.5-fold increased in disruptant compared with wild-type. It implies that PccB of S. toxytricini is involved in the activation of octanoic acid to hexylmalonic acid for lipstatin biosynthesis

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease

Oxidation of Alcohols and Activated Alkanes with Lewis Acid-Activated TEMPO

The reactivity of MCl3(η(1)-TEMPO) (M = Fe, 1; Al, 2; TEMPO = 2,2,6,6-tetramethylpiperidine-N-oxyl) with a variety of alcohols, including 3,4-dimethoxybenzyl alcohol, 1-phenyl-2-phenoxyethanol, and 1,2-diphenyl-2-methoxyethanol, was investigated using NMR spectroscopy and mass spectrometry. Complex 1 was effective in cleanly converting these substrates to the corresponding aldehyde or ketone. Complex 2 was also able to oxidize these substrates; however, in a few instances the products of overoxidation were also observed. Oxidation of activated alkanes, such as xanthene, by 1 or 2 suggests that the reactions proceed via an initial 1-electron concerted proton-electron transfer (CPET) event. Finally, reaction of TEMPO with FeBr3 in Et2O results in the formation of a mixture of FeBr3(η(1)-TEMPOH) (23) and [FeBr2(η(1)-TEMPOH)]2(μ-O) (24), via oxidation of the solvent, Et2O

Bayesian neural networks for uncertainty estimation of imaging biomarkers.

Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially across scans, and therefore yield unfaithful estimates in the follow-up statistical analysis of biomarkers. The core problem is that segmentation and biomarker analysis are performed independently. We propose to propagate segmentation uncertainty to the statistical analysis to account for variations in segmentation confidence. To this end, we evaluate four Bayesian neural networks to sample from the posterior distribution and estimate the uncertainty. We then assign confidence measures to the biomarker and propose statistical models for its integration in group analysis and disease classification. Our results for segmenting the liver in patients with diabetes mellitus clearly demonstrate the improvement of integrating biomarker uncertainty in the statistical inference