Sirtuin 1 facilitates generation of induced pluripotent stem cells from mouse embryonic fibroblasts through the miR-34a and p53 pathways

published_or_final_versio

Common Scaling Patterns in Intertrade Times of U. S. Stocks

We analyze the sequence of time intervals between consecutive stock trades of thirty companies representing eight sectors of the U. S. economy over a period of four years. For all companies we find that: (i) the probability density function of intertrade times may be fit by a Weibull distribution; (ii) when appropriately rescaled the probability densities of all companies collapse onto a single curve implying a universal functional form; (iii) the intertrade times exhibit power-law correlated behavior within a trading day and a consistently greater degree of correlation over larger time scales, in agreement with the correlation behavior of the absolute price returns for the corresponding company, and (iv) the magnitude series of intertrade time increments is characterized by long-range power-law correlations suggesting the presence of nonlinear features in the trading dynamics, while the sign series is anti-correlated at small scales. Our results suggest that independent of industry sector, market capitalization and average level of trading activity, the series of intertrade times exhibit possibly universal scaling patterns, which may relate to a common mechanism underlying the trading dynamics of diverse companies. Further, our observation of long-range power-law correlations and a parallel with the crossover in the scaling of absolute price returns for each individual stock, support the hypothesis that the dynamics of transaction times may play a role in the process of price formation.Comment: 8 pages, 5 figures. Presented at The Second Nikkei Econophysics Workshop, Tokyo, 11-14 Nov. 2002. A subset appears in "The Application of Econophysics: Proceedings of the Second Nikkei Econophysics Symposium", editor H. Takayasu (Springer-Verlag, Tokyo, 2003) pp.51-57. Submitted to Phys. Rev. E on 25 June 200

Prognostic factors and monomicrobial necrotizing fasciitis: gram-positive versus gram-negative pathogens

<p>Abstract</p> <p>Background</p> <p>Monomicrobial necrotizing fasciitis is rapidly progressive and life-threatening. This study was undertaken to ascertain whether the clinical presentation and outcome for patients with this disease differ for those infected with a gram-positive as compared to gram-negative pathogen.</p> <p>Methods</p> <p>Forty-six patients with monomicrobial necrotizing fasciitis were examined retrospectively from November 2002 to January 2008. All patients received adequate broad-spectrum antibiotic therapy, aggressive resuscitation, prompt radical debridement and adjuvant hyperbaric oxygen therapy. Eleven patients were infected with a gram-positive pathogen (Group 1) and 35 patients with a gram-negative pathogen (Group 2).</p> <p>Results</p> <p>Group 2 was characterized by a higher incidence of hemorrhagic bullae and septic shock, higher APACHE II scores at 24 h post-admission, a higher rate of thrombocytopenia, and a higher prevalence of chronic liver dysfunction. Gouty arthritis was more prevalent in Group 1. For non-survivors, the incidences of chronic liver dysfunction, chronic renal failure and thrombocytopenia were higher in comparison with those for survivors. Lower level of serum albumin was also demonstrated in the non-survivors as compared to those in survivors.</p> <p>Conclusions</p> <p>Pre-existing chronic liver dysfunction, chronic renal failure, thrombocytopenia and hypoalbuminemia, and post-operative dependence on mechanical ventilation represent poor prognostic factors in monomicrobial necrotizing fasciitis. Patients with gram-negative monobacterial necrotizing fasciitis present with more fulminant sepsis.</p

From BIM towards digital twin: Strategy and future development for smart asset management

With the rising adoption of Building Information Model (BIM) for as-set management within architecture, engineering, construction and owner-operated (AECO) sector, BIM-enabled asset management has been increasingly attracting more attentions in both research and practice. This study provides a comprehensive review and analysis of the state-of-the-art latest research and industry standards development that impact upon BIM and asset management within the operations and maintenance (O&M) phase. However, BIM is not always enough in whole-life cycle asset management, especially in the O&M phase. Therefore, a framework for future development of smart asset management are proposed, integrating the concept of Digital Twin (DT). DT integrates artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. The findings will contribute to inspiring novel research ideas and promote wide-spread adoption of smart DT-enabled asset management within the O&M phaseCentre for Digital Built Britain, Innovate U

Experimental simulation of quantum tunneling in small systems

It is well known that quantum computers are superior to classical computers in efficiently simulating quantum systems. Here we report the first experimental simulation of quantum tunneling through potential barriers, a widespread phenomenon of a unique quantum nature, via NMR techniques. Our experiment is based on a digital particle simulation algorithm and requires very few spin-1/2 nuclei without the need of ancillary qubits. The occurrence of quantum tunneling through a barrier, together with the oscillation of the state in potential wells, are clearly observed through the experimental results. This experiment has clearly demonstrated the possibility to observe and study profound physical phenomena within even the reach of small quantum computers.Comment: 17 pages and 8 figure

Developing a dynamic digital twin at a building level: Using Cambridge campus as case study

A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. A DT, if equipped with appropriate algorithms will represent and predict future condition and performance of their physical counterparts. Current developments related to DTs are still at an early stage with respect to buildings and other infrastructure assets. Most of these developments focus on the architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, where the value potential is immense. A systematic and clear architecture verified with practical use cases for constructing a DT is the foremost step for effective operation and maintenance of assets. This paper presents a system architecture for developing dynamic DTs in building levels for integrating heterogeneous data sources, support intelligent data query, and provide smarter decision-making processes. This will further bridge the gaps between human relationships with buildings/regions via a more intelligent, visual and sustainable channels. This architecture is brought to life through the development of a dynamic DT demonstrator of the West Cambridge site of the University of Cambridge. Specifically, this demonstrator integrates an as-is multi-layered IFC Building Information Model (BIM), building management system data, space management data, real-time Internet of Things (IoT)-based sensor data, asset registry data, and an asset tagging platform. The demonstrator also includes two applications: (1) improving asset maintenance and asset tracking using Augmented Reality (AR); and (2) equipment failure prediction. The long-term goals of this demonstrator are also discussed in this paper

Transverse electric field–induced deformation of armchair single-walled carbon nanotube

The deformation of armchair single-walled carbon nanotube under transverse electric field has been investigated using density functional theory. The results show that the circular cross-sections of the nanotubes are deformed to elliptic ones, in which the tube diameter along the field direction is increased, whereas the diameter perpendicular to the field direction is reduced. The electronic structures of the deformed nanotubes were also studied. The ratio of the major diameter to the minor diameter of the elliptic cross-section was used to estimate the degree of the deformation. It is found that this ratio depends on the field strength and the tube diameter. However, the field direction has little role in the deformation

ADH1B Arg47His Polymorphism Is Associated with Esophageal Cancer Risk in High-Incidence Asian Population: Evidence from a Meta-Analysis

with ESCC in Asian populations under a common ancestry scenario of the susceptibility loci, we combined all available studies into a meta-analysis.. Heterogeneity among studies and their publication bias were also tested. can bring more risk to ESCC (OR  = 13.46, 95% CI: 2.32–78.07). allele

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

As an essential approach to understanding human interactions, emotion classification is a vital component of behavioral studies as well as being important in the design of context-aware systems. Recent studies have shown that speech contains rich information about emotion, and numerous speech-based emotion classification methods have been proposed. However, the classification performance is still short of what is desired for the algorithms to be used in real systems. We present an emotion classification system using several one-against-all support vector machines with a thresholding fusion mechanism to combine the individual outputs, which provides the functionality to effectively increase the emotion classification accuracy at the expense of rejecting some samples as unclassified. Results show that the proposed system outperforms three state-of-the-art methods and that the thresholding fusion mechanism can effectively improve the emotion classification, which is important for applications that require very high accuracy but do not require that all samples be classified. We evaluate the system performance for several challenging scenarios including speaker-independent tests, tests on noisy speech signals, and tests using non-professional acted recordings, in order to demonstrate the performance of the system and the effectiveness of the thresholding fusion mechanism in real scenarios.Peer ReviewedPreprin