Post-assembly modification of kinetically metastable Fe(II)2L3 triple helicates.

We report the covalent post-assembly modification of kinetically metastable amine-bearing Fe(II)2L3 triple helicates via acylation and azidation. Covalent modification of the metastable helicates prevented their reorganization to the thermodynamically favored Fe(II)4L4 tetrahedral cages, thus trapping the system at the non-equilibrium helicate structure. This functionalization strategy also conveniently provides access to a higher-order tris(porphyrinatoruthenium)-helicate complex that would be difficult to prepare by de novo ligand synthesis.This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC). D.A.R. acknowledges the Gates Cambridge Trust for Ph.D. (Gates Cambridge Scholarship) and conference funding.This is the final published version. It first appeared at http://pubs.acs.org/doi/abs/10.1021/ja5042397

Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data

BACKGROUND: A hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied. METHODOLOGY/PRINCIPAL FINDINGS: We show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing the correspondence between the results of several algorithms (TD, glocal and BU) and the correct annotations provided by experts. The correspondence was tested in multiple domains including gene expression experiments, stock trade records and functional protein families. The performance of each of the algorithms is evaluated by statistical criteria that are assigned to clusters (nodes of the hierarchy tree) based on expert-labeled data. Whereas TD algorithms perform better on global patterns, BU algorithms perform well and are advantageous when finer granularity of the data is sought. In addition, a novel TD algorithm that is based on genuine density of the data points is presented and is shown to outperform other divisive and agglomerative methods. Application of the algorithm to more than 500 protein sequences belonging to ion-channels illustrates the potential of the method for inferring overlooked functional annotations. ClustTree, a graphical Matlab toolbox for applying various hierarchical clustering algorithms and testing their quality is made available. CONCLUSIONS: Although currently rarely used, global approaches, in particular, TD or glocal algorithms, should be considered in the exploratory process of clustering. In general, applying unsupervised clustering methods can leverage the quality of manually-created mapping of proteins families. As demonstrated, it can also provide insights in erroneous and missed annotations

Interactive Language Learning by Robots: The Transition from Babbling to Word Forms

The advent of humanoid robots has enabled a new approach to investigating the acquisition of language, and we report on the development of robots able to acquire rudimentary linguistic skills. Our work focuses on early stages analogous to some characteristics of a human child of about 6 to 14 months, the transition from babbling to first word forms. We investigate one mechanism among many that may contribute to this process, a key factor being the sensitivity of learners to the statistical distribution of linguistic elements. As well as being necessary for learning word meanings, the acquisition of anchor word forms facilitates the segmentation of an acoustic stream through other mechanisms. In our experiments some salient one-syllable word forms are learnt by a humanoid robot in real-time interactions with naive participants. Words emerge from random syllabic babble through a learning process based on a dialogue between the robot and the human participant, whose speech is perceived by the robot as a stream of phonemes. Numerous ways of representing the speech as syllabic segments are possible. Furthermore, the pronunciation of many words in spontaneous speech is variable. However, in line with research elsewhere, we observe that salient content words are more likely than function words to have consistent canonical representations; thus their relative frequency increases, as does their influence on the learner. Variable pronunciation may contribute to early word form acquisition. The importance of contingent interaction in real-time between teacher and learner is reflected by a reinforcement process, with variable success. The examination of individual cases may be more informative than group results. Nevertheless, word forms are usually produced by the robot after a few minutes of dialogue, employing a simple, real-time, frequency dependent mechanism. This work shows the potential of human-robot interaction systems in studies of the dynamics of early language acquisition

Alignment to the Actions of a Robot

Alignment is a phenomenon observed in human conversation: Dialog partners’ behavior converges in many respects. Such alignment has been proposed to be automatic and the basis for communicating successfully. Recent research on human–computer dialog promotes a mediated communicative design account of alignment according to which the extent of alignment is influenced by interlocutors’ beliefs about each other. Our work aims at adding to these findings in two ways. (a) Our work investigates alignment of manual actions, instead of lexical choice. (b) Participants interact with the iCub humanoid robot, instead of an artificial computer dialog system. Our results confirm that alignment also takes place in the domain of actions. We were not able to replicate the results of the original study in general in this setting, but in accordance with its findings, participants with a high questionnaire score for emotional stability and participants who are familiar with robots align their actions more to a robot they believe to be basic than to one they believe to be advanced. Regarding alignment over the course of an interaction, the extent of alignment seems to remain constant, when participants believe the robot to be advanced, but it increases over time, when participants believe the robot to be a basic version

The composite first person narrative: Texture, structure, and meaning in writing phenomenological descriptions

This paper illustrates the use of composite first person narrative interpretive methods, as described by Todres, across a range of phenomena. This methodology introduces texture into the presently understood structures of phenomena and thereby creates new understandings of the phenomenon, bringing about a form of understanding that is relationally alive that contributes to improved caring practices. The method is influenced by the work of Gendlin, Heidegger, van Manen, Gadamer, and Merleau-Ponty. The method's applicability to different research topics is demonstrated through the composite narratives of nursing students learning nursing practice in an accelerated and condensed program, obese female adolescents attempting weight control, chronically ill male parolees, and midlife women experiencing distress during menopause. Within current research, these four phenomena have been predominantly described and understood through quantified articulations that give the reader a structural understanding of the phenomena, but the more embodied or “contextual” human qualities of the phenomena are often not visible. The “what is it like” or the “unsaid” aspects of such human phenomena are not clear to the reader when proxies are used to “account for” a variety of situated conditions. This novel method is employed to re-present narrative data and findings from research through first person accounts that blend the voices of the participants with those of the researcher, emphasizing the connectedness, the “we” among all participants, researchers, and listeners. These re-presentations allow readers to develop more embodied understandings of both the texture and structure of each of the phenomena and illustrate the use of the composite account as a way for researchers to better understand and convey the wholeness of the experience of any phenomenon under inquiry

Lateral specialization in unilateral spatial neglect : a cognitive robotics model

In this paper, we present the experimental results of an embodied cognitive robotic approach for modelling the human cognitive deficit known as unilateral spatial neglect (USN). To this end, we introduce an artificial neural network architecture designed and trained to control the spatial attentional focus of the iCub robotic platform. Like the human brain, the architecture is divided into two hemispheres and it incorporates bio-inspired plasticity mechanisms, which allow the development of the phenomenon of the specialization of the right hemisphere for spatial attention. In this study, we validate the model by replicating a previous experiment with human patients affected by the USN and numerical results show that the robot mimics the behaviours previously exhibited by humans. We also simulated recovery after the damage to compare the performance of each of the two hemispheres as additional validation of the model. Finally, we highlight some possible advantages of modelling cognitive dysfunctions of the human brain by means of robotic platforms, which can supplement traditional approaches for studying spatial impairments in humans

A comparison of methods to assess the antimicrobial activity of nanoparticle combinations on bacterial cells

Copyright: © 2018 Bankier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.BACKGROUND: Bacterial cell quantification after exposure to antimicrobial compounds varies widely throughout industry and healthcare. Numerous methods are employed to quantify these antimicrobial effects. With increasing demand for new preventative methods for disease control, we aimed to compare and assess common analytical methods used to determine antimicrobial effects of novel nanoparticle combinations on two different pathogens. METHODS: Plate counts of total viable cells, flow cytometry (LIVE/DEAD BacLight viability assay) and qPCR (viability qPCR) were used to assess the antimicrobial activity of engineered nanoparticle combinations (NPCs) on Gram-positive (Staphylococcus aureus) and Gram-negative (Pseudomonas aeruginosa) bacteria at different concentrations (0.05, 0.10 and 0.25 w/v%). Results were analysed using linear models to assess the effectiveness of different treatments. RESULTS: Strong antimicrobial effects of the three NPCs (AMNP0-2) on both pathogens could be quantified using the plate count method and flow cytometry. The plate count method showed a high log reduction (>8-log) for bacteria exposed to high NPC concentrations. We found similar antimicrobial results using the flow cytometry live/dead assay. Viability qPCR analysis of antimicrobial activity could not be quantified due to interference of NPCs with qPCR amplification. CONCLUSION: Flow cytometry was determined to be the best method to measure antimicrobial activity of the novel NPCs due to high-throughput, rapid and quantifiable results.Peer reviewe

Evolving Synaptic Plasticity with an Evolutionary Cellular Development Model

Since synaptic plasticity is regarded as a potential mechanism for memory formation and learning, there is growing interest in the study of its underlying mechanisms. Recently several evolutionary models of cellular development have been presented, but none have been shown to be able to evolve a range of biological synaptic plasticity regimes. In this paper we present a biologically plausible evolutionary cellular development model and test its ability to evolve different biological synaptic plasticity regimes. The core of the model is a genomic and proteomic regulation network which controls cells and their neurites in a 2D environment. The model has previously been shown to successfully evolve behaving organisms, enable gene related phenomena, and produce biological neural mechanisms such as temporal representations. Several experiments are described in which the model evolves different synaptic plasticity regimes using a direct fitness function. Other experiments examine the ability of the model to evolve simple plasticity regimes in a task -based fitness function environment. These results suggest that such evolutionary cellular development models have the potential to be used as a research tool for investigating the evolutionary aspects of synaptic plasticity and at the same time can serve as the basis for novel artificial computational systems