Thermopower in the strongly overdoped region of single-layer Bi2Sr2CuO6+d superconductor

The evolution of the thermoelectric power S(T) with doping, p, of single-layer Bi2Sr2CuO6+d ceramics in the strongly overdoped region is studied in detail. Analysis in term of drag and diffusion contributions indicates a departure of the diffusion from the T-linear metallic behavior. This effect is increased in the strongly overdoped range (p~0.2-0.28) and should reflect the proximity of some topological change.Comment: 4 pages, 4 figure

Type I Interferon in Children with Viral or Bacterial Infections.

BACKGROUND: Fever is one of the leading causes of consultation in the pediatric emergency department for patients under the age of 3 years. Distinguishing between bacterial and viral infections etiologies in febrile patients remains challenging. We hypothesized that specific host biomarkers for viral infections, such as type I-interferon (IFN), could help clinicians' decisions and limit antibiotic overuse. METHODS: Paxgene tubes and serum were collected from febrile children (n = 101), age from 7 days to 36 months, with proven viral or bacterial infections, being treated at pediatric emergency departments in France. We assessed the performance of an IFN signature, which was based on quantification of expression of IFN-stimulated genes using the Nanostring® technology and plasma IFN-α quantified by digital ELISA technology. RESULTS: Serum concentrations of IFN-α were below the quantification threshold (30 fg/mL) for 2% (1/46) of children with proven viral infections and for 71% (39/55) of children with bacterial infections (P 0.91 for both) between viral and bacterial infection in febrile children, compared to C-reactive protein (0.83). CONCLUSIONS: IFN-α is increased in blood of febrile infants with viral infections. The discriminative performance of IFN-α femtomolar concentrations as well as blood transcriptional signatures could show a diagnostic benefit and potentially limit antibiotic overuse. CLINICAL TRIALS REGISTRATION: clinicaltrials.gov (NCT03163628)

Prior host-country work experience and immigrant labor market outcomes: evidence from Canada

Recent changes to immigration selection policies favor skilled workers with prior work experience in the immigrant host country. Using unique administrative tax data for Canada, we estimate earnings equations to quantify the difference in earnings of immigrants with prior Canadian experience (prefilers) and those without prior experience (non-prefilers). We find that, relative to non-prefilers, entry earnings are higher for prefilers and, for male immigrants, this earnings advantage persists for at least 20 years after arrival. We show that the primary source of the higher entry earnings of prefilers is a higher return to foreign experience. In addition, the prefiler earnings advantage is largest for university graduates and the return to foreign experience is higher for prefilers from Western countries than those from the rest of the world. Our findings suggest that a move towards an immigrant selection system which uses previous host-country work experience as a criterion will improve the labor market performance of immigrants."We are grateful for the funding from the Social Science and Humanities Research Council (SSHRC) and technical support from Statistics Canada."https://izajodm.springeropen.com/articles/10.1186/s40176-016-0075-

Convergent communication, sensing and localization in 6g systems: An overview of technologies, opportunities and challenges

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Persistent inequalities in 90-day colon cancer mortality: an English cohort study.

BACKGROUND: Variation in colon cancer mortality occurring shortly after diagnosis is widely reported between socio-economic status (SES) groups: we investigated the role of different prognostic factors in explaining variation in 90-day mortality. METHODS: National cancer registry data were linked with national clinical audit data and Hospital Episode Statistics records for 69 769 adults diagnosed with colon cancer in England between January 2010 and March 2013. By gender, logistic regression was used to estimate the effects of SES, age and stage at diagnosis, comorbidity and surgical treatment on probability of death within 90 days from diagnosis. Multiple imputations accounted for missing stage. We predicted conditional probabilities by prognostic factor patterns and estimated the effect of SES (deprivation) from the difference between deprivation-specific average predicted probabilities. RESULTS: Ninety-day probability of death rose with increasing deprivation, even after accounting for the main prognostic factors. When setting the deprivation level to the least deprived group for all patients and keeping all other prognostic factors as observed, the differences between deprivation-specific averaged predicted probabilities of death were greatly reduced but persisted. Additional analysis suggested stage and treatment as potential contributors towards some of these inequalities. CONCLUSIONS: Further examination of delayed diagnosis, access to treatment and post-operative care by deprivation group may provide additional insights into understanding deprivation disparities in mortality

Sensitization of interferon-γ induced apoptosis in human osteosarcoma cells by extracellular S100A4

BACKGROUND: S100A4 is a small Ca(2+)-binding protein of the S100 family with metastasis-promoting properties. Recently, secreted S100A4 protein has been shown to possess a number of functions, including induction of angiogenesis, stimulation of cell motility and neurite extension. METHODS: Cell cultures from two human osteosarcoma cell lines, OHS and its anti-S100A4 ribozyme transfected counterpart II-11b, was treated with IFN-γ and recombinant S100A4 in order to study the sensitizing effects of extracellular S100A4 on IFN-γ mediated apoptosis. Induction of apoptosis was demonstrated by DNA fragmentation, cleavage of poly (ADP-ribose) polymerase and Lamin B. RESULTS: In the present work, we found that the S100A4-expressing human osteosarcoma cell line OHS was more sensitive to IFN-γ-mediated apoptosis than the II-11b cells. S100A4 protein was detected in conditioned medium from OHS cells, but not from II-11b cells, and addition of recombinant S100A4 to the cell medium sensitized II-11b cells to apoptosis induced by IFN-γ. The S100A4/IFN-γ-mediated induction of apoptosis was shown to be independent of caspase activation, but dependent on the formation of reactive oxygen species. Furthermore, addition of extracellular S100A4 was demonstrated to activate nuclear factor-κB (NF-κB). CONCLUSION: In conclusion, we have shown that S100A4 sensitizes osteosarcoma cells to IFN-γ-mediated induction of apoptosis. Additionally, extracellular S100A4 activates NF-κB, but whether these events are causally related remains unknown

Migration outflows and optimal migration policy: rules versus discretion

We study the effects of more open borders on return migration and show that migrants are more likely to return to the origin country when migration rules are softened, because this implies that they could more easily re-migrate if return migration is unsuccessful. As a result, softening migration rules leads to lower net inflows than is generally acknowledged. We show that if government follows rules to shape the optimal migration policy, it will choose more open “borders” than were its behaviour to be discretionary. However, this requires an appropriate commitment technology. We show that electoral accountability may be a solution to the commitment problem. As a matter of fact, observed softer immigration rules in western countries suggest the effectiveness of such a mechanism.info:eu-repo/semantics/publishedVersio