Spawning rings of exceptional points out of Dirac cones

The Dirac cone underlies many unique electronic properties of graphene and topological insulators, and its band structure--two conical bands touching at a single point--has also been realized for photons in waveguide arrays, atoms in optical lattices, and through accidental degeneracy. Deformations of the Dirac cone often reveal intriguing properties; an example is the quantum Hall effect, where a constant magnetic field breaks the Dirac cone into isolated Landau levels. A seemingly unrelated phenomenon is the exceptional point, also known as the parity-time symmetry breaking point, where two resonances coincide in both their positions and widths. Exceptional points lead to counter-intuitive phenomena such as loss-induced transparency, unidirectional transmission or reflection, and lasers with reversed pump dependence or single-mode operation. These two fields of research are in fact connected: here we discover the ability of a Dirac cone to evolve into a ring of exceptional points, which we call an "exceptional ring." We experimentally demonstrate this concept in a photonic crystal slab. Angle-resolved reflection measurements of the photonic crystal slab reveal that the peaks of reflectivity follow the conical band structure of a Dirac cone from accidental degeneracy, whereas the complex eigenvalues of the system are deformed into a two-dimensional flat band enclosed by an exceptional ring. This deformation arises from the dissimilar radiation rates of dipole and quadrupole resonances, which play a role analogous to the loss and gain in parity-time symmetric systems. Our results indicate that the radiation that exists in any open system can fundamentally alter its physical properties in ways previously expected only in the presence of material loss and gain

Grey and white matter correlates of recent and remote autobiographical memory retrieval:Insights from the dementias

The capacity to remember self-referential past events relies on the integrity of a distributed neural network. Controversy exists, however, regarding the involvement of specific brain structures for the retrieval of recently experienced versus more distant events. Here, we explored how characteristic patterns of atrophy in neurodegenerative disorders differentially disrupt remote versus recent autobiographical memory. Eleven behavioural-variant frontotemporal dementia, 10 semantic dementia, 15 Alzheimer's disease patients and 14 healthy older Controls completed the Autobiographical Interview. All patient groups displayed significant remote memory impairments relative to Controls. Similarly, recent period retrieval was significantly compromised in behavioural-variant frontotemporal dementia and Alzheimer's disease, yet semantic dementia patients scored in line with Controls. Voxel-based morphometry and diffusion tensor imaging analyses, for all participants combined, were conducted to investigate grey and white matter correlates of remote and recent autobiographical memory retrieval. Neural correlates common to both recent and remote time periods were identified, including the hippocampus, medial prefrontal, and frontopolar cortices, and the forceps minor and left hippocampal portion of the cingulum bundle. Regions exclusively implicated in each time period were also identified. The integrity of the anterior temporal cortices was related to the retrieval of remote memories, whereas the posterior cingulate cortex emerged as a structure significantly associated with recent autobiographical memory retrieval. This study represents the first investigation of the grey and white matter correlates of remote and recent autobiographical memory retrieval in neurodegenerative disorders. Our findings demonstrate the importance of core brain structures, including the medial prefrontal cortex and hippocampus, irrespective of time period, and point towards the contribution of discrete regions in mediating successful retrieval of distant versus recently experienced events

U.S. academic libraries: understanding their web presence and their relationship with economic indicators

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-013-1001-0The main goal of this research is to analyze the web structure and performance of units and services belonging to U.S. academic libraries in order to check their suitability for webometric studies. Our objectives include studying their possible correlation with economic data and assessing their use for complementary evaluation purposes. We conducted a survey of library homepages, institutional repositories, digital collections, and online catalogs (a total of 374 URLs) belonging to the 100 U.S. universities with the highest total expenditures in academic libraries according to data provided by the National Center for Education Statistics. Several data points were taken and analyzed, including web variables (page count, external links, and visits) and economic variables (total expenditures, expenditures on printed and electronic books, and physical visits). The results indicate that the variety of URL syntaxes is wide, diverse and complex, which produces a misrepresentation of academic libraries’ web resources and reduces the accuracy of web analysis. On the other hand, institutional and web data indicators are not highly correlated. Better results are obtained by correlating total library expenditures with URL mentions measured by Google (r = 0.546) and visits measured by Compete (r = 0.573), respectively. Because correlation values obtained are not highly significant, we estimate such correlations will increase if users can avoid linkage problems (due to the complexity of URLs) and gain direct access to log files (for more accurate data about visits).Orduña Malea, E.; Regazzi, JJ. (2014). U.S. academic libraries: understanding their web presence and their relationship with economic indicators. Scientometrics. 98(1):315-336. doi:10.1007/s11192-013-1001-0S315336981Adecannby, J. (2011). Web link analysis of interrelationship between top ten African universities and world universities. Annals of library and information studies, 58(2), 128–138.Aguillo, I. F. (2009). Measuring the institutions’ footprint in the web. Library Hi Tech, 27(4), 540–556.Aguillo, I. F., Ortega, J. L., & Fernández, M. (2008). Webometric Ranking of World Universities: Introduction, methodology, and future developments. Higher education in Europe, 33(2/3), 234–244.Aguillo, I. F., Ortega, J. L., Fernandez, M., & Utrilla, A. M. (2010). Indicators for a webometric ranking of open Access repositories. Scientometrics, 82(3), 477–486.Arakaki, M., & Willet, P. (2009). Webometric analysis of departments of librarianship and information science: A follow-up study. Journal of information science, 35(2), 143–152.Arlitsch, K., & O’Brian, P. S. (2012). Invisible institutional repositories: Addresing the low indexing ratios of IR in Google Scholar. Library Hi Tech, 30(1), 60–81.Bar-Ilan, J. (1999). Search engine results over time—A case study on search engine stability”. Cybermetrics, 2/3. Retrieved February 18, 2013 from http://www.cindoc.csic.es/cybermetrics/articles/v2i1p1.html.Bar-Ilan, J. (2001). Data collection methods on the Web for informetric purposes: A review and analysis. Scientometrics, 50(1), 7–32.Bermejo, F. (2007). The internet audience: Constitution & measurement. New York: Peter Lang Pub Incorporated.Buigues-Garcia, M., & Gimenez-Chornet, V. (2012). Impact of Web 2.0 on national libraries. International Journal of Information Management, 32(1), 3–10.Chu, H., He, S., & Thelwall, M. (2002). Library and information science schools in Canada and USA: A Webometric perspective. Journal of education for Library and Information Science, 43(2), 110–125.Chua, Alton, Y. K., & Goh, D. H. (2010). A study of Web 2.0 applications in library websites. Library and Information Science Research, 32(3), 203–211.Gallego, I., García, I.-M., & Rodríguez, L. (2009). Universities’ websites: Disclosure practices and the revelation of financial information. The International Journal of Digital Accounting Research, 9(15), 153–192.Gomes, B. & Smith, B. T. (2003). Detecting query-specific duplicate documents. [Patent]. Retrieved February 18, 2013 from http://www.patents.com/Detecting-query-specific-duplicate-documents/US6615209/en-US .Harinarayana, N. S., & Raju, N. V. (2010). Web 2.0 features in university library web sites. Electronic Library, 28(1), 69–88.Lewandowski, D., Wahlig, H., & Meyer-Bautor, G. (2006). The freshness of web search engine databases. Journal of Information Science, 32(2), 131–148.Mahmood, K., & Richardson, J. V, Jr. (2012). Adoption of Web 2.0 in US academic libraries: A survey of ARL library websites. Program, 45(4), 365–375.Orduña-Malea, E., & Ontalba-Ruipérez, J-A. (2012). Selective linking from social platforms to university websites: A case study of the Spanish academic system. Scientometrics. (in press).Ortega, J. L., & Aguillo, I. F. (2009). Mapping World-class universities on the Web. Information Processing and Management, 45(2), 272–279.Ortega, José L. & Aguillo, Isidro F. (2009b). North America Academic Web Space: Multicultural Canada vs. The United States Homogeneity. In: ASIST & ISSI pre-conference symposium on informetrics and scientometrics.Phan, T., Hardesty, L., Sheckells, C., & George, A. (2009). Documentation for the academic libraries survey (ALS) public-use data file: Fiscal year 2008. Washington DC: National Center for Education Statistics. Institute of Education Sciences U.S. Department of Education.Qiu, J., Cheng, J., & Wang, Z. (2004). An analysis of backlinks counts and web impact factors for Chinese university websites. Scientometrics, 60(3), 463–473.Regazzi, J. J. (2012a). Constrained?—An analysis of U.S. Academic Libraries and shifts in spending, staffing and utilization, 1998–2008. College and Research Libraries, 73(5), 449–468.Regazzi, J. J. (2012b). Comparing Academic Library Spending with Public Libraries, Public K-12 Schools, Higher Education Public Institutions, and Public Hospitals Between 1998–2008. Journal of Academic Librarianship, 38(4), 205–216.Rousseau, R. (1999). Daily time series of common single word searches in AltaVista and NorthernLight. Cybermetrics, 2/3. Retrieved February 18, 2013 from http://www.cindoc.csic.es/cybermetrics/articles/v2i1p2.html .Sato, S., & Itsumura, H. (2011). How do people use open access papers in non-academic activities? A link analysis of papers deposited in institutional repositories. Library, Information and Media Studies, 9(1), 51–64.Scholze, F. (2007). Measuring research impact in an open access environment. Liber Quarterly: The Journal of European Research Libraries, 17(1–4), 220–232.Smith, A. G. (2011). Wikipedia and institutional repositories: An academic symbiosis? In: Proceedings of the ISSI 2011 conference. Durban, South Africa, 4–7 July 2011. Retrieved February 18, 2013 from http://www.vuw.ac.nz/staff/alastair_smith/publns/SmithAG2011_ISSI_paper.pdf .Smith, A.G. (2012). Webometric evaluation of institutional repositories. In: Proceedings of the 8th international conference on webometrics informetrics and scientometrics & 13th collnet meeting. Seoul (Korea), 722–729.Smith, A., & Thelwall, M. (2002). Web impact factors for Australasian Universities. Scientometrics, 54(3), 363–380.Tang, R., & Thelwall, M. (2008). A hyperlink analysis of US public and academic libraries’ web sites. Library Quarterly, 78(4), 419–435.Thelwall, M. (2008). Extracting accurate and complete results from search engines: Case study Windows Live. Journal of the American Society for Information Science and Technology, 59(1), 38–50.Thelwall, M. (2009). Introduction to webometrics: Quantitative web research for the social sciences. San Rafael: Morgan & Claypool.Thelwall, M., & Sud, P. (2011). A comparison of methods for collecting web citation data for academic organisations. Journal of the American Society for Information Science and Technology, 62(8), 1488–1497.Thelwall, M., Sud, P., & Wilkinson, D. (2012). Link and co-inlink network diagrams with URL citations or title mentions. Journal of the American Society for Information Science and Technology, 63(10), 1960–1972.Thelwall, M., & Zuccala, A. (2008). A University-centred European Union link analysis. Scientometrics, 75(3), 407–442.Uyar, A. (2009a). Google stemming mechanisms. Journal of Information Science, 35(5), 499–514.Uyar, A. (2009b). Investigation of the accuracy of search engine hit counts. Journal of Information Science, 35(4), 469–480.Zuccala, A., Thelwall, M., Oppenheim, C., & Dhiensa, R. (2007). Web intelligence analyses of digital libraries: A case study of the National Electronic Library for Health (NeLH). Journal of Documentation, 63(4), 558–589

Chronic widespread musculoskeletal pain, fatigue, depression and disordered sleep in chronic post-SARS syndrome; a case-controlled study

<p>Abstract</p> <p>Background</p> <p>The long term adverse effects of Severe Acute Respiratory Syndrome (SARS), a viral disease, are poorly understood.</p> <p>Methods</p> <p>Sleep physiology, somatic and mood symptoms of 22 Toronto subjects, 21 of whom were healthcare workers, (19 females, 3 males, mean age 46.29 yrs.+/- 11.02) who remained unable to return to their former occupation (mean 19.8 months, range: 13 to 36 months following SARS) were compared to 7 healthy female subjects. Because of their clinical similarities to patients with fibromyalgia syndrome (FMS) these post-SARS subjects were similarly compared to 21 drug free female patients, (mean age 42.4 +/- 11.8 yrs.) who fulfilled criteria for fibromyalgia.</p> <p>Results</p> <p>Chronic post-SARS is characterized by persistent fatigue, diffuse myalgia, weakness, depression, and nonrestorative sleep with associated REM-related apneas/hypopneas, an elevated sleep EEG cyclical alternating pattern, and alpha EEG sleep anomaly. Post- SARS patients had symptoms of pre and post-sleep fatigue and post sleep sleepiness that were similar to the symptoms of patients with FMS, and similar to symptoms of patients with chronic fatigue syndrome. Both post-SARS and FMS groups had sleep instability as indicated by the high sleep EEG cyclical alternating pattern rate. The post-SARS group had a lower rating of the alpha EEG sleep anomaly as compared to the FMS patients. The post-SARS group also reported less pre-sleep and post-sleep musculoskeletal pain symptoms.</p> <p>Conclusions</p> <p>The clinical and sleep features of chronic post-SARS form a syndrome of chronic fatigue, pain, weakness, depression and sleep disturbance, which overlaps with the clinical and sleep features of FMS and chronic fatigue syndrome.</p

Knowledge of causes, clinical features and diagnosis of common zoonoses among medical practitioners in Tanzania

Many factors have been mentioned as contributing to under-diagnosis and under-reporting of zoonotic diseases particularly in the sub-Sahara African region. These include poor disease surveillance coverage, poor diagnostic capacity, the geographical distribution of those most affected and lack of clear strategies to address the plight of zoonotic diseases. The current study investigates the knowledge of medical practitioners of zoonotic diseases as a potential contributing factor to their under-diagnosis and hence under-reporting. The study was designed as a cross-sectional survey. Semi-structured open-ended questionnaire was administered to medical practitioners to establish the knowledge of anthrax, rabies, brucellosis, trypanosomiasis, echinococcosis and bovine tuberculosis in selected health facilities within urban and rural settings in Tanzania between April and May 2005. Frequency data were analyzed using likelihood ratio chi-square in Minitab version 14 to compare practitioners' knowledge of transmission, clinical features and diagnosis of the zoonoses in the two settings. For each analysis, likelihood ratio chi-square p-value of less than 0.05 was considered to be significant. Fisher's exact test was used where expected results were less than five. Medical practitioners in rural health facilities had poor knowledge of transmission of sleeping sickness and clinical features of anthrax and rabies in humans compared to their urban counterparts. In both areas the practitioners had poor knowledge of how echinococcosis is transmitted to humans, clinical features of echinococcosis in humans, and diagnosis of bovine tuberculosis in humans. Knowledge of medical practitioners of zoonotic diseases could be a contributing factor to their under-diagnosis and under-reporting in Tanzania. Refresher courses on zoonotic diseases should be conducted particularly to practitioners in rural areas. More emphasis should be put on zoonotic diseases in teaching curricula of medical practitioners' training institutions in Tanzania to improve the diagnosis, reporting and control of zoonotic diseases. Veterinary and medical collaboration should be strengthened to enable more effective control of zoonotic diseases in Tanzania

Searches for B0(s)→J/ψppˉ and B+→J/ψppˉπ+ decays

The results of searches for B0(s)→J/ψ pp¯ and B + → J/ψ p p¯ π+ decays are reported. The analysis is based on a data sample, corresponding to an integrated luminosity of 1.0 fb−1 of pp collisions, collected with the LHCb detector. An excess with 2.8 σ significance is seen for the decay B0s→J/ψ pp¯ and an upper limit on the branching fraction is set at the 90 % confidence level: B(B0s→J/ψ pp¯) < 4.8 × 10−6, which is the first such limit. No significant signals are seen for B0 → J/ψ pp¯ and B+ → J/ψ pp¯ π + decays, for which the corresponding limits are set: B(B0→J/ψ pp¯) < 5.2 × 10−7, which significantly improves the existing limit; and B(B+→J/ψ pp¯π+) < 5.0 × 10−7, which is the first limit on this branching fraction

Predictors of early recurrence after resection of colorectal liver metastases

BACKGROUND: Early recurrence after resection of colorectal liver metastases (CLM) is common. Patients at risk of early recurrence may be candidates for enhanced preoperative staging and/or earlier postoperative imaging. The aim of this study was to determine if there are any risk factors that specifically predict early liver-only and systemic recurrence. METHODS: Retrospective analysis of prospective database of patients undergoing liver resection (LR) for CLM from 2004 to 2006 was undertaken. Early recurrence was defined as occurring within 18 months of LR. Patients were classified into three groups: early liver-only recurrence, early systemic recurrence and recurrence-free. Preoperative factors were compared between patients with and without early recurrence. RESULTS: Two hundred and forty-three consecutive patients underwent LR for CLM. Twenty-seven patients (11%) developed early liver-only recurrence. Dukes C stage and male sex were significantly associated with early liver-only recurrence (P < 0.05). Sixty-six patients (27%) developed early systemic recurrence. Tumour size ≥3.6 cm and tumour number (>2) were significantly associated with early systemic recurrence (P < 0.001). CONCLUSIONS: It is possible to stratify patients according to the risk of early liver-only or systemic recurrence after resection of CLM. High-risk patients may be candidates for preoperative MRI and/or computed tomography-positron emission tomography (CT-PET) scan and should receive intensive postoperative surveillance

Features generated for computational splice-site prediction correspond to functional elements

<p>Abstract</p> <p>Background</p> <p>Accurate selection of splice sites during the splicing of precursors to messenger RNA requires both relatively well-characterized signals at the splice sites and auxiliary signals in the adjacent exons and introns. We previously described a feature generation algorithm (FGA) that is capable of achieving high classification accuracy on human 3' splice sites. In this paper, we extend the splice-site prediction to 5' splice sites and explore the generated features for biologically meaningful splicing signals.</p> <p>Results</p> <p>We present examples from the observed features that correspond to known signals, both core signals (including the branch site and pyrimidine tract) and auxiliary signals (including GGG triplets and exon splicing enhancers). We present evidence that features identified by FGA include splicing signals not found by other methods.</p> <p>Conclusion</p> <p>Our generated features capture known biological signals in the expected sequence interval flanking splice sites. The method can be easily applied to other species and to similar classification problems, such as tissue-specific regulatory elements, polyadenylation sites, promoters, etc.</p