Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour

The often opportunistic nature of biological recording via citizen science leads to taxonomic, spatial and temporal biases which add uncertainty to biodiversity estimates. However, such biases may also give valuable insight into volunteers’ recording behaviour. Using Greater London as a case-study we examined the composition of three citizen science datasets – from Greenspace Information for Greater London CIC, iSpot and iRecord - with respect to recorder contribution and spatial and taxonomic biases, i.e. when, where and what volunteers record. We found most volunteers contributed few records and were active for just one day. Each dataset had its own taxonomic and spatial signature suggesting that volunteers’ personal recording preferences may attract them towards particular schemes. There were also patterns across datasets: species’ abundance and ease of identification were positively associated with number of records, as was plant height. We found clear hotspots of recording activity, the 10 most popular sites containing open water. We note that biases are accrued as part of the recording process (e.g. species’ detectability) as well as from volunteer preferences. An increased understanding of volunteer behaviour gained from analysing the composition of records could thus enhance the fit between volunteers’ interests and the needs of scientific projects

From the kilns to the fair: producing building materials at Faragola and Canusium (northern Apulia, Italy)

Faragola and Canusium potters used Ca-rich clays—widely available nearby—for the production of build- ing materials. The clayey materials were used as received, before being fired in the local kilns at temperatures between 600 and ~1000 ° C. No technological distinctions were made in relation to the type of object to be produced (tile, brick, etc). The investigated productions are compositionally distinguish- able from both coarse wares for cooking and fine table ware produced in the same archaeological sites. A fine clayey ma- terial, very similar to that used for table ware, was supplied for the production of these building materials, which are chemi- cally, mineralogically and petrographically very similar among themselves. Hence, the Faragola and Canusium bricks and tiles cannot be easily discriminated but the presence/ absence of volcanites and volcanic glass represents an effec- tive discriminating factor, able to indicate areas of different supplies within two main deposits: the Pleistocene marine and alluvial terraced deposits, typical of northern Apulia

From education to action: How technology enables public participation in the context of environmental conservation

UCL’s ExCiteS group develops theories, tools and methodologies to enable communities anywhere to engage in Citizen Science. One goal is to apply ICT to stimulate and support public participation in environmental conservation. The work discussed here approaches this goal from two perspectives. First, we intend to educate the general public about endangered species in a playful and engaging manner and thereby raise environmental awareness. The other challenge is to provide non-literate indigenous people with a tool that empowers them to take action to protect their local environment and way of life. We used participatory mobile sensing and Augmented Reality (AR) to implement these. We present two smartphone application prototypes, which employ ICT as a medium between the environment and human appreciation and action. Our “Augmented Zoology” app leverages AR and game-elements to bring dead bones to life and thereby creates an interactive learning experience in an otherwise static museum exhibit. The experience also triggers social interaction and discussions. Our “Anti-Poaching” app co-designed and currently being tested by Pygmy hunter-gatherers in the Cameroon rainforest. Through a decision tree of pictorial icons, representing various illegal activities, users can record and geolocate incidents. Therefore, technology plays a twofold role in supporting public participation and action

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification

A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks

Background: Several problems in network biology and medicine can be cast into a framework where entities are represented through partially labeled networks, and the aim is inferring the labels (usually binary) of the unlabeled part. Connections represent functional or genetic similarity between entities, while the labellings often are highly unbalanced, that is one class is largely under-represented: for instance in the automated protein function prediction (AFP) for most Gene Ontology terms only few proteins are annotated, or in the disease-gene prioritization problem only few genes are actually known to be involved in the etiology of a given disease. Imbalance-aware approaches to accurately predict node labels in biological networks are thereby required. Furthermore, such methods must be scalable, since input data can be large-sized as, for instance, in the context of multi-species protein networks. Results: We propose a novel semi-supervised parallel enhancement of COSNet, an imbalance-aware algorithm build on Hopfield neural model recently suggested to solve the AFP problem. By adopting an efficient representation of the graph and assuming a sparse network topology, we empirically show that it can be efficiently applied to networks with millions of nodes. The key strategy to speed up the computations is to partition nodes into independent sets so as to process each set in parallel by exploiting the power of GPU accelerators. This parallel technique ensures the convergence to asymptotically stable attractors, while preserving the asynchronous dynamics of the original model. Detailed experiments on real data and artificial big instances of the problem highlight scalability and efficiency of the proposed method. Conclusions: By parallelizing COSNet we achieved on average a speed-up of 180x in solving the AFP problem in the S. cerevisiae, Mus musculus and Homo sapiens organisms, while lowering memory requirements. In addition, to show the potential applicability of the method to huge biomolecular networks, we predicted node labels in artificially generated sparse networks involving hundreds of thousands to millions of nodes

Neutron diffraction of Cu–Zn–Sn ternary alloys : non-invasive assessment of the compositions of historical bronze/brass copper ternary alloys

Neutron diffraction can be used as a tool for the characterization of metal materials in a totally non-invasive mode. In binary alloys with two elements in solid solution, crystallographic structure analysis provides information on the overall element compositions of the metal, based on the linear relationship between elemental fractions and lattice parameters known as Vegard's rule. However, for ternary solid-solution alloys the derivation of the overall metal composition is not straightforward because the problem is mathematically underdetermined. A number of artificially produced samples in the ternary system Cu–Zn–Sn, widely used in antiquity for gunmetal, were investigated by time-of-flight neutron diffraction, inductively coupled plasma mass spectroscopy, scanning electron microscopy with energy-dispersive X-ray spectroscopy and electron microprobe analysis. The multi-analysis approach allows definition of the limits and capabilities of neutron diffraction for obtaining the overall composition of a small sample set of ternary alloys, and thus moves the methodical approach a step forward even though it is applicable to the present sample set only. A relation showing an increasing Cu and Sn fraction counterbalanced by decreasing Zn content is presented, which allows the determination of the δ-phase composition from a lattice parameter measurement. Furthermore, the observed Zn loss up to 1.8 wt% for each melting step is of significance for the reconstruction of ancient technologies