3,820 research outputs found
Non-invertible transformations and spatiotemporal randomness
We generalize the exact solution to the Bernoulli shift map. Under certain
conditions, the generalized functions can produce unpredictable dynamics. We
use the properties of the generalized functions to show that certain dynamical
systems can generate random dynamics. For instance, the chaotic Chua's circuit
coupled to a circuit with a non-invertible I-V characteristic can generate
unpredictable dynamics. In general, a nonperiodic time-series with truncated
exponential behavior can be converted into unpredictable dynamics using
non-invertible transformations. Using a new theoretical framework for chaos and
randomness, we investigate some classes of coupled map lattices. We show that,
in some cases, these systems can produce completely unpredictable dynamics. In
a similar fashion, we explain why some wellknown spatiotemporal systems have
been found to produce very complex dynamics in numerical simulations. We
discuss real physical systems that can generate random dynamics.Comment: Accepted in International Journal of Bifurcation and Chao
The intrapreneurial state: Singapore's emergence in the smart and sustainable urban solutions field
The East Asian developmental state model and the Anglo-American entrepreneurial state model profile varied ways in which the state continues to intervene in economic development. These models are developed by different disciplines and against diverse contexts to capture extrasocietal state responses to neoliberalism and globalization but leave the intrasocietal preconditions for state evolution little explored. We elaborate the concept of state intrapreneurialism as one way of understanding the interrelationship between economic and state transformation – one ingredient of the intrasocietal preconditions underpinning the responses to extrasocietal changes emphasized in the post-developmental state literature. Drawing on the case of Singapore's emergence in the field of smart/sustainable urban solutions, the subsidiary contributions of this paper are to suggest intrapreneurship as a specific and enduring advantage within the developmental state model, especially when set against its limitations signalled in the post-developmental state literature
The Fourth Element: Characteristics, Modelling, and Electromagnetic Theory of the Memristor
In 2008, researchers at HP Labs published a paper in {\it Nature} reporting
the realisation of a new basic circuit element that completes the missing link
between charge and flux-linkage, which was postulated by Leon Chua in 1971. The
HP memristor is based on a nanometer scale TiO thin-film, containing a
doped region and an undoped region. Further to proposed applications of
memristors in artificial biological systems and nonvolatile RAM (NVRAM), they
also enable reconfigurable nanoelectronics. Moreover, memristors provide new
paradigms in application specific integrated circuits (ASICs) and field
programmable gate arrays (FPGAs). A significant reduction in area with an
unprecedented memory capacity and device density are the potential advantages
of memristors for Integrated Circuits (ICs). This work reviews the memristor
and provides mathematical and SPICE models for memristors. Insight into the
memristor device is given via recalling the quasi-static expansion of Maxwell's
equations. We also review Chua's arguments based on electromagnetic theory.Comment: 28 pages, 14 figures, Accepted as a regular paper - the Proceedings
of Royal Society
A hybrid systematic narrative review of instruments measuring home-based care nurses\u27 competency
Aim: The aim of the study was to identify and synthesize the contents and the psychometric properties of the existing instruments measuring home-based care (HBC) nurses\u27 competencies. Design: A hybrid systematic narrative review was performed. Review Methods: The eligible studies were reviewed to identify the competencies measured by the instruments for HBC nurses. The psychometric properties of instruments in development and psychometric testing design studies were also examined. The methodological quality of the studies was evaluated using the Medical Education Research Study Quality Instrument and COSMIN checklist accordingly. Data Sources: Relevant studies were searched on CINAHL, MEDLINE (via PubMed), EMBASE, PsychINFO and Scopus from 2000 to 2022. The search was limited to full-text items in the English language. Results: A total of 23 studies reporting 24 instruments were included. 12 instruments were adopted or modified by the studies while the other 12 were developed and psychometrically tested by the studies. None of the instruments encompassed all of the 10 home-based nursing care competencies identified in an earlier study. The two most frequently measured competencies were the management of health conditions, and critical thinking and problem-solving skills, while the two least measured competencies were quality and safety, and technological literacy. The content and structural validity of most instruments were inadequate since the adopted instruments were not initially designed or tested among HBC nurses. Conclusion: This review provides a consolidation of existing instruments that were used to assess HBC nurses\u27 competencies. The instruments were generally not comprehensive, and the content and structural validity were limited. Nonetheless, the domains, items and approaches to instrument development could be adopted to develop and test a comprehensive competency instrument for home-based nursing care practice in the future. Impact: This review consolidated instruments used to measure home-based care nurses\u27 competency. The instruments were often designed for ward-based care nurses hence a comprehensive and validated home-based nursing care competency instrument is needed. Nurses, researchers and nursing leaders could consider the competency instruments identified in this review to measure nurses\u27 competencies, while a home-based nursing care competency scale is being developed. Patient or Public Contribution: No patient or public contribution was required in this review
An iterative approach of protein function prediction
Background: Current approaches of predicting protein functions from a protein-protein interaction (PPI) dataset are based on an assumption that the available functions of the proteins (a.k.a. annotated proteins) will determine the functions of the proteins whose functions are unknown yet at the moment (a.k.a. un-annotated proteins). Therefore, the protein function prediction is a mono-directed and one-off procedure, i.e. from annotated proteins to un-annotated proteins. However, the interactions between proteins are mutual rather than static and mono-directed, although functions of some proteins are unknown for some reasons at present. That means when we use the similarity-based approach to predict functions of un-annotated proteins, the un-annotated proteins, once their functions are predicted, will affect the similarities between proteins, which in turn will affect the prediction results. In other words, the function prediction is a dynamic and mutual procedure. This dynamic feature of protein interactions, however, was not considered in the existing prediction algorithms.Results: In this paper, we propose a new prediction approach that predicts protein functions iteratively. This iterative approach incorporates the dynamic and mutual features of PPI interactions, as well as the local and global semantic influence of protein functions, into the prediction. To guarantee predicting functions iteratively, we propose a new protein similarity from protein functions. We adapt new evaluation metrics to evaluate the prediction quality of our algorithm and other similar algorithms. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions.Conclusions: The iterative approach is more likely to reflect the real biological nature between proteins when predicting functions. A proper definition of protein similarity from protein functions is the key to predicting functions iteratively. The evaluation results demonstrated that in most cases, the iterative approach outperformed non-iterative ones with higher prediction quality in terms of prediction precision, recall and F-value
Selective laser sintering of hydroxyapatite reinforced polyethylene composites for bioactive implants and tissue scaffold development
Selective laser sintering (SLS) has been investigated for the production of bioactive implants and tissue scaffolds using composites of high-density polyethylene (HDPE) reinforced with hydroxyapatite (HA) with the aim of achieving the rapid manufacturing of customized implants. Single-layer and multilayer block specimens made of HA-HDPE composites with 30 and 40 vol % HA were sintered successfully using a CO2 laser sintering system. Laser power and scanning speed had a significant effect on the sintering behaviour. The degree of particle fusion and porosity were influenced by the laser processing parameters, hence control can be attained by varying these parameters. Moreover, the SLS processing allowed exposure of HA particles on the surface of the composites and thereby should provide bioactive products. Pores existed in the SLS-fabricated composite parts and at certain processing parameters a significant fraction of the pores were within the optimal sizes for tissue regeneration. The results indicate that the SLS technique has the potential not only to fabricate HA-HDPE composite products but also to produce appropriate features for their application as bioactive implants and tissue scaffolds
The GTPase Rab26 links synaptic vesicles to the autophagy pathway.
Small GTPases of the Rab family not only regulate target recognition in membrane traffic but also control other cellular functions such as cytoskeletal transport and autophagy. Here we show that Rab26 is specifically associated with clusters of synaptic vesicles in neurites. Overexpression of active but not of GDP-preferring Rab26 enhances vesicle clustering, which is particularly conspicuous for the EGFP-tagged variant, resulting in a massive accumulation of synaptic vesicles in neuronal somata without altering the distribution of other organelles. Both endogenous and induced clusters co-localize with autophagy-related proteins such as Atg16L1, LC3B and Rab33B but not with other organelles. Furthermore, Atg16L1 appears to be a direct effector of Rab26 and binds Rab26 in its GTP-bound form, albeit only with low affinity. We propose that Rab26 selectively directs synaptic and secretory vesicles into preautophagosomal structures, suggesting the presence of a novel pathway for degradation of synaptic vesicles
MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure
Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe
- …