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Widely available digital technologies are empowering citizens who are increasingly well informed and involved in numerous water, climate, and environmental challenges. Citizen science can serve many different purposes, from the "pleasure of doing science" to complementing observations, increasing scientific literacy, and supporting collaborative behaviour to solve specific water management problems. Still, procedures on how to incorporate citizens' knowledge effectively to inform policy and decision-making are lagging behind. Moreover, general conceptual frameworks are unavailable, preventing the widespread uptake of citizen science approaches for more participatory cross-sectorial water governance. In this work, we identify the shared constituents, interfaces, and interlinkages between hydrological sciences and other academic and non-academic disciplines in addressing water issues. Our goal is to conceptualize a transdisciplinary framework for valuing citizen science and advancing the hydrological sciences. Joint efforts between hydrological, computer, and social sciences are envisaged for integrating human sensing and behavioural mechanisms into the framework. Expanding opportunities of online communities complement the fundamental value of on-site surveying and indigenous knowledge. This work is promoted by the Citizens AND HYdrology (CANDHY) Working Group established by the International Association of Hydrological Sciences (IAHS).

Two thirds of hydrological observation networks in developing countries are reported to be in poor or declining condition. At the same time innovation in sensor technologies and data processing are presenting opportunities for enhancing observation networks that are not being realized. The World Meteorological Organization’s Global Hydrometry Support Facility, or WMO HydroHub, was launched in 2016 to transform assistance to operational water monitoring agencies around the world. If successful, the initiative will increase the amount of hydrometric data available to researchers, catchment managers and water policy makers. To those unfamiliar with UN organizations, however, the nature of such initiatives, the reasoning behind the decisions taken to establish them and the mechanisms by which they try to deliver benefits for society, can be opaque. This paper adopts a novel dialogue-style format to explore the set-up of the WMO HydroHub and build awareness amongst those who ultimately may benefit from its approaches.

Two thirds of hydrological observation networks in developing countries are reported to be in poor or declining condition. At the same time innovation in sensor technologies and data processing are presenting opportunities for enhancing observation networks that are not being realized. The World Meteorological Organization’s Global Hydrometry Support Facility, or WMO HydroHub, was launched in 2016 to transform assistance to operational water monitoring agencies around the world. If successful, the initiative will increase the amount of hydrometric data available to researchers, catchment managers and water policy makers. To those unfamiliar with UN organizations, however, the nature of such initiatives, the reasoning behind the decisions taken to establish them and the mechanisms by which they try to deliver benefits for society, can be opaque. This paper adopts a novel dialogue-style format to explore the set-up of the WMO HydroHub and build awareness amongst those who ultimately may benefit from its approaches.

[Background]: Protein alignments are an essential tool for many bioinformatics analyses. While sequence alignments are accurate for proteins of high sequence similarity, they become unreliable as they approach the so-called 'twilight zone' where sequence similarity gets indistinguishable from random. For such distant pairs, structure alignment is of much better quality. Nevertheless, sequence alignment is the only choice in the majority of cases where structural data is not available. This situation demands development of methods that extend the applicability of accurate sequence alignment to distantly related proteins. [Results]: We develop a sequence alignment method that combines the prediction of a structural profile based on the protein's sequence with the alignment of that profile using our recently published alignment tool SABERTOOTH. In particular, we predict the contact vector of protein structures using an artificial neural network based on position-specific scoring matrices generated by PSI-BLAST and align these predicted contact vectors. The resulting sequence alignments are assessed using two different tests: First, we assess the alignment quality by measuring the derived structural similarity for cases in which structures are available. In a second test, we quantify the ability of the significance score of the alignments to recognize structural and evolutionary relationships. As a benchmark we use a representative set of the SCOP (structural classification of proteins) database, with similarities ranging from closely related proteins at SCOP family level, to very distantly related proteins at SCOP fold level. Comparing these results with some prominent sequence alignment tools, we find that SABERTOOTH produces sequence alignments of better quality than those of Clustal W, T-Coffee, MUSCLE, and PSI-BLAST. HHpred, one of the most sophisticated and computationally expensive tools available, outperforms our alignment algorithm at family and superfamily lev

Proteins are very complex physical objects consisting of thousands of atoms and hundreds of amino acids with complicated local and global interactions on length scales ranging from the microscopic neighbourhood of atoms to the macroscopic size of organisms. The spatial configuration, in spite of that, is encoded into one single character per amino acid using a twenty character alphabet, an apparent contradiction that is not fully understood to date. This thesis is concerned with problems of protein structure and the relationship of protein sequence and structure. It is tried to integrate the different approaches typically carried out by physicists in the field that investigate very simplified model systems, e.g. single helices, with the bioinformatics approach to build powerful analysis tools. The first approach often leads to oversimplified systems that do not describe native proteins as a whole, while the second can be too heuristic and too involved to answer fundamental questions. We start from defining vectorial descriptions of protein structure, similar in form to sequence descriptions, to firstly compare protein structures, i.e. to perform structure alignments, and discuss several measures for structural similarity. From these we derive a statistical structural similarity score for pairs of protein structure based on their spatial superimposition. Then we utilize a previously known ansatz to exploit the sequence to structure correlation in order to predict vectorial structure descriptions from protein sequence. These predicted profiles are then used within the same alignment framework to align protein sequences. For these alignments a basic evolutionary similarity measure between protein sequences is derived. Large part of this thesis is dedicated to the objective assessment of alignment methods including the new method presented and a number of establish programs. A commonly used measure of structural similarity, the Percentage of Structural Identity (PSI), i

The complexity of protein structures calls for simplified representations of their topology. The simplest possible mathematical description of a protein structure is a onedimensional profile representing, for instance, buriedness or secondary structure. This kind of representation has been introduced for studying the sequence to structure relationship, with applications to fold recognition. Here we define the effective connectivity profile (EC), a network theoretical profile that self-consistently represents the network structure of the protein contact matrix. The EC profile makes mathematically explicit the relationship between protein structure and protein sequence, because it allows predicting the average hydrophobicity profile (HP) and the distributions of amino acids at each site for families of homologous proteins sharing the same structure. In this sense, the EC provides an analytic solution to the statistical inverse folding problem, which consists in finding the statistical properties of the set of sequences compatible with a given structure. We tested these predictions with simulations of the structurally constrained neutral (SCN) model of protein evolution with structure conservation, for singleand multi-domain proteins, and for a wide range of mutation processes, the latter producing sequences with very different hydrophobicity profiles, finding that the EC-based predictions are accurate even when only one sequence of the family is known. The EC profile is very significantly correlated with the HP for sequence-structure pairs in the PDB as well. The EC profile generalizes the properties of previously introduced structural profiles to modular proteins such as multidomain chains, and its correlation with the sequence profile is substantially improved with respect to the previously defined profiles, particularly for long proteins. Furthermore, the EC profile has a dynamic interpretation, since the EC components are strongly inversely related with the temper

[Background] The task of computing highly accurate structural alignments of proteins in very short computation time is still challenging. This is partly due to the complexity of protein structures. Therefore, instead of manipulating coordinates directly, matrices of inter-atomic distances, sets of vectors between protein backbone atoms, and other reduced representations are used. These decrease the effort of comparing large sets of coordinates, but protein structural alignment still remains computationally expensive., [Results] We represent the topology of a protein structure through a structural profile that expresses the global effective connectivity of each residue. We have shown recently that this representation allows explicitly expressing the relationship between protein structure and protein sequence. Based on this very condensed vectorial representation, we develop a structural alignment framework that recognizes structural similarities with accuracy comparable to established alignment tools. Furthermore, our algorithm has favourable scaling of computation time with chain length. Since the algorithm is independent of the details of the structural representation, our framework can be applied to sequence-to-sequence and sequence-to-structure comparison within the same setup, and it is therefore more general than other existing tools.