Graph Metrics for Temporal Networks

Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node-node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.Comment: 26 pages, 5 figures, Chapter in Temporal Networks (Petter Holme and Jari Saram\"aki editors). Springer. Berlin, Heidelberg 201

The role of paediatric nurses in medication safety prior to the implementation of electronic prescribing:a qualitative case study

Objectives: To explore paediatric nurses’ experiences and perspectives of their role in the medication process and how this role is enacted in everyday practice. Methods: A qualitative case study on a general surgical ward of a paediatric hospital in England, one year prior to the planned implementation of ePrescribing. Three focus groups and six individual semi-structured interviews were conducted, involving 24 nurses. Focus groups and interviews were audio-recorded, transcribed, anonymized and subjected to thematic analysis. Results: Two overarching analytical themes were identified: the centrality of risk management in nurses’ role in the medication process and the distributed nature of nurses’ medication risk management practices. Nurses’ contribution to medication safety was seen as an intrinsic feature of a role that extended beyond just preparing and administering medications as prescribed and placed nurses at the heart of a dynamic set of interactions, practices and situations through which medication risks were managed. These findings also illustrate the collective nature of patient safety. Conclusions: Both the recognized and the unrecognized contributions of nurses to the management of medications needs to be considered in the design and implementation of ePrescribing systems

Mesoscopic organization reveals the constraints governing C. elegans nervous system

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. In this paper, we have considered the somatic nervous system of the nematode Caenorhabditis elegans, for which the entire neuronal connectivity diagram is known. We focus on the organization of the system into modules, i.e., neuronal groups having relatively higher connection density compared to that of the overall network. We show that this mesoscopic feature cannot be explained exclusively in terms of considerations, such as optimizing for resource constraints (viz., total wiring cost) and communication efficiency (i.e., network path length). Comparison with other complex networks designed for efficient transport (of signals or resources) implies that neuronal networks form a distinct class. This suggests that the principal function of the network, viz., processing of sensory information resulting in appropriate motor response, may be playing a vital role in determining the connection topology. Using modular spectral analysis, we make explicit the intimate relation between function and structure in the nervous system. This is further brought out by identifying functionally critical neurons purely on the basis of patterns of intra- and inter-modular connections. Our study reveals how the design of the nervous system reflects several constraints, including its key functional role as a processor of information.Comment: Published version, Minor modifications, 16 pages, 9 figure

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB

Background: There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). Methods. The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. Results: The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets indicated that all three clustering methods showed a near-perfect ability to detect known subgroups and correctly classify individuals into those subgroups. Conclusions: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data and clinical research questions

Network model of immune responses reveals key effectors to single and co-infection dynamics by a respiratory bacterium and a gastrointestinal helminth

Co-infections alter the host immune response but how the systemic and local processes at the site of infection interact is still unclear. The majority of studies on co-infections concentrate on one of the infecting species, an immune function or group of cells and often focus on the initial phase of the infection. Here, we used a combination of experiments and mathematical modelling to investigate the network of immune responses against single and co-infections with the respiratory bacterium Bordetella bronchiseptica and the gastrointestinal helminth Trichostrongylus retortaeformis. Our goal was to identify representative mediators and functions that could capture the essence of the host immune response as a whole, and to assess how their relative contribution dynamically changed over time and between single and co-infected individuals. Network-based discrete dynamic models of single infections were built using current knowledge of bacterial and helminth immunology; the two single infection models were combined into a co-infection model that was then verified by our empirical findings. Simulations showed that a T helper cell mediated antibody and neutrophil response led to phagocytosis and clearance of B. bronchiseptica from the lungs. This was consistent in single and co-infection with no significant delay induced by the helminth. In contrast, T. retortaeformis intensity decreased faster when co-infected with the bacterium. Simulations suggested that the robust recruitment of neutrophils in the co-infection, added to the activation of IgG and eosinophil driven reduction of larvae, which also played an important role in single infection, contributed to this fast clearance. Perturbation analysis of the models, through the knockout of individual nodes (immune cells), identified the cells critical to parasite persistence and clearance both in single and co-infections. Our integrated approach captured the within-host immuno-dynamics of bacteria-helminth infection and identified key components that can be crucial for explaining individual variability between single and co-infections in natural populations

Structural basis for CRISPR RNA-guided DNA recognition by Cascade

The CRISPR (clustered regularly interspaced short palindromic repeats) immune system in prokaryotes uses small guide RNAs to neutralize invading viruses and plasmids. In Escherichia coli, immunity depends on a ribonucleoprotein complex called Cascade. Here we present the composition and low-resolution structure of Cascade and show how it recognizes double-stranded DNA (dsDNA) targets in a sequence-specific manner. Cascade is a 405-kDa complex comprising five functionally essential CRISPR-associated (Cas) proteins (CasA1B2C6D1E1) and a 61-nucleotide CRISPR RNA (crRNA) with 5′-hydroxyl and 2′,3′-cyclic phosphate termini. The crRNA guides Cascade to dsDNA target sequences by forming base pairs with the complementary DNA strand while displacing the noncomplementary strand to form an R-loop. Cascade recognizes target DNA without consuming ATP, which suggests that continuous invader DNA surveillance takes place without energy investment. The structure of Cascade shows an unusual seahorse shape that undergoes conformational changes when it binds target DNA.

How Can Home Care Patients and Their Caregivers Better Manage Fall Risks by Leveraging Information Technology?

Objectives: From the perspectives of home care patients and caregivers, this study aimed to (a) identify the challenges for better fall-risk management during home care episodes and (b) explore the opportunities for them to leverage health information technology (IT) solutions to improve fall-risk management during home care episodes. Methods: Twelve in-depth semistructured interviews with the patients and caregivers were conducted within a descriptive single case study design in 1 home health agency (HHA) in the mid-Atlantic region of the United States. Results: Patients and caregivers faced challenges to manage fall risks such as unmanaged expectations, deteriorating cognitive abilities, and poor care coordination between the HHA and physician practices. Opportunities to leverage health IT solutions included patient portals, telehealth, and medication reminder apps on smartphones. Conclusion: Effectively leveraging health IT could further empower patients and caregivers to reduce fall risks by acquiring the necessary information and following clinical advice and recommendations. The HHAs could improve the quality of care by adopting IT solutions that show more promise of improving the experiences of patients and caregivers in fall-risk management

A Model of Proto-Anti-Codon RNA Enzymes Requiring l-Amino Acid Homochirality

All living organisms encode the 20 natural amino acid units of polypeptides using a universal scheme of triplet nucleotide “codons”. Disparate features of this codon scheme are potentially informative of early molecular evolution: (i) the absence of any codons for d-amino acids; (ii) the odd combination of alternate codon patterns for some amino acids; (iii) the confinement of synonymous positions to a codon’s third nucleotide; (iv) the use of 20 specific amino acids rather than a number closer to the full coding potential of 64; and (v) the evolutionary relationship of patterns in stop codons to amino acid codons. Here I propose a model for an ancestral proto-anti-codon RNA (pacRNA) auto-aminoacylation system and show that pacRNAs would naturally manifest features of the codon table. I show that pacRNAs could implement all the steps for auto-aminoacylation: amino acid coordination, intermediate activation of the amino acid by the 5′-end of the pacRNA, and 3′-aminoacylation of the pacRNA. The anti-codon cradles of pacRNAs would have been able to recognize and coordinate only a small number of l-amino acids via hydrogen bonding. A need for proper spatial coordination would have limited the number of chargeable amino acids for all anti-codon sequences, in addition to making some anti-codon sequences unsuitable. Thus, the pacRNA model implies that the idiosyncrasies of the anti-codon table and l-amino acid homochirality co-evolved during a single evolutionary period. These results further imply that early life consisted of an aminoacylated RNA world with a richer enzymatic potential than ribonucleotides alone