Deciphering long-term records of natural variability and human impact as recorded in lake sediments: a palaeolimnological puzzle

Global aquatic ecosystems are under increasing threat from anthropogenic activity, as well as being exposed to past (and projected) climate change, however, the nature of how climate and human impacts are recorded in lake sediments is often ambiguous. Natural and anthropogenic drivers can force a similar response in lake systems, yet the ability to attribute what change recorded in lake sediments is natural, from that which is anthropogenic, is increasingly important for understanding how lake systems have, and will continue to function when subjected to multiple stressors; an issue that is particularly acute when considering management options for aquatic ecosystems. The duration and timing of human impacts on lake systems varies geographically, with some regions of the world (such as Africa and South America) having a longer legacy of human impact than others(e.g. New Zealand). A wide array of techniques (biological, chemical, physical and statistical) is available to palaeolimnologists to allow the deciphering of complex sedimentary records. Lake sediments are an important archive of how drivers have changed through time, and how these impacts manifest in lake systems. With a paucity of ‘real‐time’ data pre‐dating human impact, palaeolimnological archives offer the only insight into both natural variability (i.e. that driven by climate and intrinsic lake processes) and the impact of people. Whilst there is a need to acknowledge complexity, and temporal and spatial variability when deciphering change from sediment archives, a palaeolimnological approach is a powerful tool for better understanding and managing global aquatic resources

Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data

Aim Macroecological studies that require habitat suitability data for many species often derive this information from expert opinion. However, expert-based information is inherently subjective and thus prone to errors. The increasing availability of GPS tracking data offers opportunities to evaluate and supplement expert-based information with detailed empirical evidence. Here, we compared expert-based habitat suitability information from the International Union for Conservation of Nature (IUCN) with habitat suitability information derived from GPS-tracking data of 1,498 individuals from 49 mammal species. Location Worldwide. Time period 1998-2021. Major taxa studied Forty-nine terrestrial mammal species. Methods Using GPS data, we estimated two measures of habitat suitability for each individual animal: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN's classification into suitable, marginal and unsuitable habitat types. Results IUCN habitat suitability data were in accordance with the GPS data (> 95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a > 50% probability of agreement based on proportional habitat use and selection ratios, respectively. Main conclusions We show how GPS-tracking data can be used to evaluate IUCN habitat suitability data. Our findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, we show that GPS-tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data

On the route to novel antibiotics

Antibiotic resistance is one of the biggest threats to human health of our time. We are being warned of a so-called post-antibiotic era, where a simple surgery or bacterial infection could kill human beings. Without the rapid development of novel antibiotics, the continued growth of antibiotic resistance will put our society in a crisis of unprecedented scale. The bacterial cell wall resembles a protective barrier and is crucial for bacterial survival. Hence, disruption of the cell wall synthesis will lead to cell death. The bacterial membrane protein MraY is involved in the peptidoglycan synthesis, which is a component of the bacterial cell wall, by catalysing the synthesis of lipid I - a peptidoglycan precursor. In this thesis, functional and structural studies of MraY with inhibitors were performed with the future aim of designing novel antibiotics. We solved the crystal structure of MraY from the Gram-positive pathogen Clostridium bolteae in complex with the natural product inhibitor tunicamycin at 2.6 Å resolution and provided a biophysical characterisation of the binding mode of tunicamycin. A structural comparison between MraY and its human homologue GPT identified regions to modify tunicamycin to selectively target MraY. We modified and purified tunicamycins to explore their inhibitory effect and potency towards MraY and identified potent MraY inhibitors with reduced eukaryotic toxicity. Finally, we optimised the purification protocol for MraY for future biophysical and structural studies and developed a novel method using teabags for membrane protein purification

The rapid “teabag” method for high-end purification of membrane proteins

Overproduction and purification of membrane proteins are generally challenging and time-consuming procedures due to low expression levels, misfolding, and low stability once extracted from the membrane. Reducing processing steps and shortening the timespan for purification represent attractive approaches to overcome some of these challenges. We have therefore compared a fast “teabag” purification method with conventional purification for five different membrane proteins (MraY, AQP10, ClC-1, PAR2 and KCC2). Notably, this new approach reduces the purification time significantly, and the quality of the purified membrane proteins is equal to or exceeds conventional methods as assessed by size exclusion chromatography, SDS-PAGE and downstream applications such as ITC, crystallization and cryo-EM. Furthermore, the method is scalable, applicable to a range of affinity resins and allows for parallelization. Consequently, the technique has the potential to substantially simplify purification efforts of membrane proteins in basic and applied sciences

Deep learning for laser beam imprinting

Methods of ablation imprints in solid targets are widely used to characterize focusedX-ray laser beams due to a remarkable dynamic range and resolving power. A detailed descriptionof intense beam profiles is especially important in high-energy-density physics aiming at nonlinearphenomena. Complex interaction experiments require an enormous number of imprints to becreated under all desired conditions making the analysis demanding and requiring a huge amountof human work. Here, for the first time, we present ablation imprinting methods assistedby deep learning approaches. Employing a multi-layer convolutional neural network (U-Net)trained on thousands of manually annotated ablation imprints in poly(methyl methacrylate), wecharacterize a focused beam of beamline FL24/FLASH2 at the Free-electron laser in Hamburg.The performance of the neural network is subject to a thorough benchmark test and comparisonwith experienced human analysts. Methods presented in this Paper pave the way towards a virtualanalyst automatically processing experimental data from start to end