Data Based Mechanistic modelling optimal utilisation of raingauge data for rainfall-riverflow modelling of sparsely gauged tropical basin in Ghana

Data-Based Mechanistic (DBM) modelling is a Transfer Function (TF) modelling approach, whereby the data defines the model. The DBM approach, unlike physics-based distributed and conceptual models that fit existing laws to data-series, uses the data to identify the model structure in an objective statistical manner. The approach is parsimonious, in that it requires few spatially-distributed data and is, therefore, suitable for data limited regions like West Africa. Multiple Input Single Output (MISO) rainfall to riverflow modelling approach is the utilization of multiple rainfall time-series as separate input in parallel into a model to simulate a single riverflow time-series in a large scale. The approach is capable of simulating the effects of each rain gauge on a lumped riverflow response. Within this paper we present the application of DBM-MISO modelling approach to 20778 km2 humid tropical rain forest basin in Ghana. The approach makes use of the Bedford Ouse modelling technique to evaluate the non-linear behaviour of the catchment with the input of the model integrated in different ways including into new single-input time-series for subsequent Single Input Single Output (SISO) modelling. The identified MISO models were able to improve the efficiency and understanding of the rainfall-riverflow behaviour within the study catchment. The paper illustrates the potential benefits of the methodology in modelling large catchments with sparse network of rainfall stations

Exploratory studies into the prospects for seasonal forecasting of lake levels and outflows

Some of the largest lakes in the world are in Africa and seasonal forecasts of levels and outflows can potentially help with water supply, irrigation and hydropower operations; in particular regarding the risks from floods or droughts. Some factors which increase the prospects for real-time forecasting include the significant time delays between rainfall and outflows resulting from the huge volumes of water stored, and that many studies have shown possible links between regional rainfall and climate indices for the Indian Ocean and elsewhere. On the other hand, on account of the huge areas covered, catchments can span several climate zones and rainfall and flow monitoring networks are often sparse. Exploratory studies into some of these issues are described based on case studies for two large lakes, including some preliminary findings regarding data assimilation and the complexity of models required. The studies were performed using a range of stochastic signal identification tools and are compared with the findings from an ensemble streamflow prediction approach. Preliminary conclusions are then drawn regarding the relevance of these results to the development of operational forecasting models

Exploratory studies into the prospects for seasonal forecasting of lake levels and outflows

Some of the largest lakes in the world are in Africa and seasonal forecasts of levels and outflows can potentially help with water supply, irrigation and hydropower operations; in particular regarding the risks from floods or droughts. Some factors which increase the prospects for real-time forecasting include the significant time delays between rainfall and outflows resulting from the huge volumes of water stored, and that many studies have shown possible links between regional rainfall and climate indices for the Indian Ocean and elsewhere. On the other hand, on account of the huge areas covered, catchments can span several climate zones and rainfall and flow monitoring networks are often sparse. Exploratory studies into some of these issues are described based on case studies for two large lakes, including some preliminary findings regarding data assimilation and the complexity of models required. The studies were performed using a range of stochastic signal identification tools and are compared with the findings from an ensemble streamflow prediction approach. Preliminary conclusions are then drawn regarding the relevance of these results to the development of operational forecasting models

Bioconjugation Strategies for Connecting Proteins to DNA-Linkers for Single-Molecule Force-Based Experiments

The mechanical properties of proteins can be studied with single molecule force spectroscopy (SMFS) using optical tweezers, atomic force microscopy and magnetic tweezers. It is common to utilize a flexible linker between the protein and trapped probe to exclude short-range interactions in SMFS experiments. One of the most prevalent linkers is DNA due to its well-defined properties, although attachment strategies between the DNA linker and protein or probe may vary. We will therefore provide a general overview of the currently existing non-covalent and covalent bioconjugation strategies to site-specifically conjugate DNA-linkers to the protein of interest. In the search for a standardized conjugation strategy, considerations include their mechanical properties in the context of SMFS, feasibility of site-directed labeling, labeling efficiency, and costs

The known unknowns of the Hsp90 chaperone

Molecular chaperones are vital proteins that maintain protein homeostasis by assisting in protein folding, activation, degradation, and stress protection. Among them, heat-shock protein 90 (Hsp90) stands out as an essential proteostasis hub in eukaryotes, chaperoning hundreds of "clients" (substrates). After decades of research, several "known unknowns" about the molecular function of Hsp90 remain unanswered, hampering rational drug design for the treatment of cancers, neurodegenerative and other diseases. We highlight three fundamental open questions, reviewing the current state of the field for each, and discuss new opportunities, including single-molecule technologies, to answer the known unknowns of the Hsp90 chaperone.Comment: 29 pages, 4 figure

Dynamic harmonic regression and irregular sampling; avoiding pre-processing and minimising modelling assumptions

Many environmental time-series measurements are characterised by irregular sampling. A significant improvement of the Dynamic Harmonic Regression (DHR) modelling technique to accommodate irregular sampled time-series, without the need for data pre-processing, has been developed. Taylor's series is used to obtain the time-step state increments, modifying the transition equation matrices. This allows the user to avoid artefacts arising and insertion of assumptions from interpolation and regularisation of the data to a regular time-base and makes DHR more consistent with the Data-Based Mechanistic approach to modelling environmental systems. The new technique implemented as a Matlab package has been tested on demanding simulated data-sets and demonstrated on various environmental time-series data with significantly varying sampling times. The results have been compared with standard DHR, where possible, and the method reduces analysis time and produces unambiguous results (by removing the need for pre-processing – always based on assumptions) based only on the observed environmental data

Tuning protein mechanics through an ionic cluster graft from an extremophilic protein.

Proteins from extremophilic organisms provide excellent model systems to determine the role of non-covalent interactions in defining protein stability and dynamics as well as being attractive targets for the development of robust biomaterials. Hyperthermophilic proteins have a prevalence of salt bridges, relative to their mesophilic homologues, which are thought to be important for enhanced thermal stability. However, the impact of salt bridges on the mechanical properties of proteins is far from understood. Here, a combination of protein engineering, biophysical characterisation, single molecule force spectroscopy (SMFS) and molecular dynamics (MD) simulations directly investigates the role of salt bridges in the mechanical stability of two cold shock proteins; BsCSP from the mesophilic organism Bacillus subtilis and TmCSP from the hyperthermophilic organism Thermotoga maritima. Single molecule force spectroscopy shows that at ambient temperatures TmCSP is mechanically stronger yet, counter-intuitively, its native state can withstand greater deformation before unfolding (i.e. it is mechanically soft) compared with BsCSP. MD simulations were used to identify the location and quantify the population of salt bridges, and reveal that TmCSP contains a larger number of highly occupied salt bridges than BsCSP. To test the hypothesis that salt-bridges endow these mechanical properties on the hyperthermophilic CSP, a charged triple mutant (CTM) variant of BsCSP was generated by grafting an ionic cluster from TmCSP into the BsCSP scaffold. As expected CTM is thermodynamically more stable and mechanically softer than BsCSP. We show that a grafted ionic cluster can increase the mechanical softness of a protein and speculate that it could provide a mechanical recovery mechanism and that it may be a design feature applicable to other proteins

Probing temperature- and solvent-dependent protein dynamics using terahertz time-domain spectroscopy

The effect of temperature on the terahertz-frequency-range material properties of lyophilized and single-crystal hen egg-white lysozyme has been measured using terahertz time-domain spectroscopy, with the results presented and discussed in the context of protein and solvent dynamical and glass transitions. Lyophilized hen egg-white lysozyme was measured over a temperature range from 4 to 290 K, and a change in the dynamical behaviour of the sample at around 100 K was observed through a change in the terahertz absorption spectrum. Additionally, the effect of cryoprotectants on the temperature-dependent absorption coefficient is studied, and it is demonstrated that terahertz time-domain spectroscopy is capable of resolving the true glass transition temperature of single-crystal hen egg-white lysozyme at 150 K, which is in agreement with literature values measured using differential scanning calorimetry

Long-term variations in the net inflow record for Lake Malawi

Lake Malawi is the third largest lake in Africa and plays an important role in water supply, hydropower generation, agriculture and fisheries in the region. Lake level observations started in the 1890s and anecdotal evidence of variations dates back to the early 1800s. A chronology of lake level and outflow variations is presented together with updated estimates for the net inflow to the lake. The inflow series and selected rainfall records were also analysed using an unobserved component approach and, although there was little evidence of long-term trends, there was some indication of increasing interannual variability in recent decades. A weak quasi-periodic behaviour was also noted with a period of approximately 4–8 years. The results provide useful insights into the severity of drought and flood events in the region since the 1890s and the potential for seasonal forecasting of lake levels and outflows