Transmission spectra and valley processing of graphene and carbon nanotube superlattices with inter-valley coupling

published_or_final_versio

AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units

Introduction: Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level. Method: Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model. Results: Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model. Conclusions: Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation

Geometric phase outside a Schwarzschild black hole and the Hawking effect

We study the Hawking effect in terms of the geometric phase acquired by a two-level atom as a result of coupling to vacuum fluctuations outside a Schwarzschild black hole in a gedanken experiment. We treat the atom in interaction with a bath of fluctuating quantized massless scalar fields as an open quantum system, whose dynamics is governed by a master equation obtained by tracing over the field degrees of freedom. The nonunitary effects of this system are examined by analyzing the geometric phase for the Boulware, Unruh and Hartle-Hawking vacua respectively. We find, for all the three cases, that the geometric phase of the atom turns out to be affected by the space-time curvature which backscatters the vacuum field modes. In both the Unruh and Hartle-Hawking vacua, the geometric phase exhibits similar behaviors as if there were thermal radiation at the Hawking temperature from the black hole. So, a measurement of the change of the geometric phase as opposed to that in a flat space-time can in principle reveal the existence of the Hawking radiation.Comment: 14 pages, no figures, a typo in the References corrected, version to appear in JHEP. arXiv admin note: text overlap with arXiv:1109.033

First-principles study on the electronic and transport properties of periodically nitrogen-doped graphene and carbon nanotube superlattices

Prompted by recent reports on $\sqrt{3} \times \sqrt{3}$ graphene superlattices with intrinsic inter-valley interactions, we perform first-principles calculations to investigate the electronic properties of periodically nitrogen-doped graphene and carbon nanotube nanostructures. In these structures, nitrogen atoms substitute one-sixth of the carbon atoms in the pristine hexagonal lattices with exact periodicity to form perfect $\sqrt{3} \times \sqrt{3}$ superlattices of graphene and carbon nanotubes. Multiple nanostructures of $\sqrt{3} \times \sqrt{3}$ graphene ribbons and carbon nanotubes are explored, and all configurations show nonmagnetic and metallic behaviors. The transport properties of $\sqrt{3} \times \sqrt{3}$ graphene and carbon nanotube superlattices are calculated utilizing the non-equilibrium Green's function formalism combined with density functional theory. The transmission spectrum through the pristine and $\sqrt{3} \times \sqrt{3}$ armchair carbon nanotube heterostructure shows quantized behavior under certain circumstances

Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon – the reversal paradox

This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes have important implications for the interpretation of evidence from observational studies. This article uses hypothetical scenarios to illustrate how the three paradoxes are different manifestations of one phenomenon – the reversal paradox – depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. Although the three statistical paradoxes occur in different types of variables, they share the same characteristic: the association between two variables can be reversed, diminished, or enhanced when another variable is statistically controlled for. Understanding the concepts and theory behind these paradoxes provides insights into some controversial or contradictory research findings. These paradoxes show that prior knowledge and underlying causal theory play an important role in the statistical modelling of epidemiological data, where incorrect use of statistical models might produce consistent, replicable, yet erroneous results

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

ERCC1 expression and RAD51B activity correlate with cell cycle response to platinum drug treatment not DNA repair

Background: The H69CIS200 and H69OX400 cell lines are novel models of low-level platinum-drug resistance. Resistance was not associated with increased cellular glutathione or decreased accumulation of platinum, rather the resistant cell lines have a cell cycle alteration allowing them to rapidly proliferate post drug treatment. Results: A decrease in ERCC1 protein expression and an increase in RAD51B foci activity was observed in association with the platinum induced cell cycle arrest but these changes did not correlate with resistance or altered DNA repair capacity. The H69 cells and resistant cell lines have a p53 mutation and consequently decrease expression of p21 in response to platinum drug treatment, promoting progression of the cell cycle instead of increasing p21 to maintain the arrest. Conclusion: Decreased ERCC1 protein and increased RAD51B foci may in part be mediating the maintenance of the cell cycle arrest in the sensitive cells. Resistance in the H69CIS200 and H69OX400 cells may therefore involve the regulation of ERCC1 and RAD51B independent of their roles in DNA repair. The novel mechanism of platinum resistance in the H69CIS200 and H69OX400 cells demonstrates the multifactorial nature of platinum resistance which can occur independently of alterations in DNA repair capacity and changes in ERCC1

Phonon-assisted radiofrequency absorption by gold nanoparticles resulting in hyperthermia

It is suggested that in gold nanoparticles (GNPs) of about 5 nm sizes used in the radiofrequency (RF) hyperthermia, an absorption of the RF photon by the Fermi electron occurs with involvement of the longitudinal acoustic vibrational mode (LAVM), the dominating one in the distribution of vibrational density of states (VDOS). This physical mechanism helps to explain two observed phenomena: the size dependence of the heating rate (HR) in GNPs and reduced heat production in aggregated GNPs. The argumentation proceeds within the one-electron approximation, taking into account the discretenesses of energies and momenta of both electrons and LAVMs. The heating of GNPs is thought to consist of two consecutive processes: first, the Fermi electron absorbs simultaneously the RF photon and the LAVM available in the GNP; hereafter the excited electron gets relaxed within the GNP's boundary, exciting a LAVM with the energy higher than that of the previously absorbed LAVM. GNPs containing the Ta and/or Fe impurities are proposed for the RF hyperthermia as promising heaters with enhanced HRs, and GNPs with rare-earth impurity atoms are also brought into consideration. It is shown why the maximum HR values should be expected in GNPs with about 5-7 nm size.Comment: proceedings at the NATO Advanced Research workshop FANEM-2015 (Minsk, May 25-27, 2015). To be published in the final form in: "Fundamental and Applied NanoElectroMagnetics" (Springer Science + Business Media B.V.

Does mass drug administration for the integrated treatment of neglected tropical diseases really work? Assessing evidence for the control of schistosomiasis and soil-transmitted helminths in Uganda

This paper was one of four papers commissioned to review the role of social sciences in NTD control by TDR, the Special Programme for Research and Training on Tropical Diseases, which is executed by WHO and co-sponsored by UNICEF, UNDP, the World Bank and WHO.This article has been made available through the Brunel Open Access Publishing Fund.Background: Less is known about mass drug administration [MDA] for neglected tropical diseases [NTDs] than is suggested by those so vigorously promoting expansion of the approach. This paper fills an important gap: it draws upon local level research to examine the roll out of treatment for two NTDs, schistosomiasis and soil-transmitted helminths, in Uganda. Methods: Ethnographic research was undertaken over a period of four years between 2005-2009 in north-west and south-east Uganda. In addition to participant observation, survey data recording self-reported take-up of drugs for schistosomiasis, soil-transmitted helminths and, where relevant, lymphatic filariasis and onchocerciasis was collected from a random sample of at least 10% of households at study locations. Data recording the take-up of drugs in Ministry of Health registers for NTDs were analysed in the light of these ethnographic and social survey data. Results: The comparative analysis of the take-up of drugs among adults revealed that although most long term residents have been offered treatment at least once since 2004, the actual take up of drugs for schistosomiasis and soil-transmitted helminths varies considerably from one district to another and often also within districts. The specific reasons why MDA succeeds in some locations and falters in others relates to local dynamics. Issues such as population movement across borders, changing food supply, relations between drug distributors and targeted groups, rumours and conspiracy theories about the 'real' purpose of treatment, subjective experiences of side effects from treatment, alternative understandings of affliction, responses to social control measures and historical experiences of public health control measures, can all make a huge difference. The paper highlights the need to adapt MDA to local circumstances. It also points to specific generalisable issues, notably with respect to health education, drug distribution and more effective use of existing public health legislation. Conclusion: While it has been an achievement to have offered free drugs to so many adults, current standard practices of monitoring, evaluation and delivery of MDA for NTDs are inconsistent and inadequate. Efforts to integrate programmes have exacerbated the difficulties. Improved assessment of what is really happening on the ground will be an essential step in achieving long-term overall reduction of the NTD burden for impoverished communities.This article is available through the Brunel Open Access Publishing Fund

BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction

Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such as domain-specific ontologies. However, using deep learning methods, along with biomedical ontologies, has been recently shown to effectively advance the biomedical relation extraction field. To perform relation extraction, our deep learning system, BiOnt, employs four types of biomedical ontologies, namely, the Gene Ontology, the Human Phenotype Ontology, the Human Disease Ontology, and the Chemical Entities of Biological Interest, regarding gene-products, phenotypes, diseases, and chemical compounds, respectively. We tested our system with three data sets that represent three different types of relations of biomedical entities. BiOnt achieved, in F-score, an improvement of 4.93 percentage points for drug-drug interactions (DDI corpus), 4.99 percentage points for phenotype-gene relations (PGR corpus), and 2.21 percentage points for chemical-induced disease relations (BC5CDR corpus), relatively to the state-of-the-art. The code supporting this system is available at https://github.com/lasigeBioTM/BiOnt.Comment: ECIR 202