A Dynamic Game Model of Collective Choice in Multi-Agent Systems

Inspired by successful biological collective decision mechanisms such as honey bees searching for a new colony or the collective navigation of fish schools, we consider a mean field games (MFG)-like scenario where a large number of agents have to make a choice among a set of different potential target destinations. Each individual both influences and is influenced by the group's decision, as well as the mean trajectory of all the agents. The model can be interpreted as a stylized version of opinion crystallization in an election for example. The agents' biases are dictated first by their initial spatial position and, in a subsequent generalization of the model, by a combination of initial position and a priori individual preference. The agents have linear dynamics and are coupled through a modified form of quadratic cost. Fixed point based finite population equilibrium conditions are identified and associated existence conditions are established. In general multiple equilibria may exist and the agents need to know all initial conditions to compute them precisely. However, as the number of agents increases sufficiently, we show that 1) the computed fixed point equilibria qualify as epsilon Nash equilibria, 2) agents no longer require all initial conditions to compute the equilibria but rather can do so based on a representative probability distribution of these conditions now viewed as random variables. Numerical results are reported

hTERT protein expression is independent of clinicopathological parameters and c-Myc protein expression in human breast cancer

Background Telomerase is a ribonucleoprotein enzyme that synthesises telomeres after cell division and maintains chromosomal stability leading to cellular immortalization. Telomerase has been associated with negative prognostic indicators in some studies. The present study aims to detect any association between telomerase sub-units: hTERT and hTR and the prognostic indicators including tumour's size and grade, nodal status and patient's age. Methods Tumour samples from 46 patients with primary invasive breast cancer and 3 patients with benign tumours were collected. RT-PCR analysis was used for the detection of hTR, hTERT, and PGM1 (as a housekeeping) genes expression. Results The expression of hTR and hTERT was found in 31(67.4%) and 38 (82.6%) samples respectively. We observed a significant association between hTR gene expression and younger age at diagnosis (p = 0.019) when comparing patients ≤ 40 years with those who are older than 40 years. None of the benign tumours expressed hTR gene. However, the expression of hTERT gene was revealed in 2 samples. No significant association between hTR and hTERT expression and tumour's grade, stage and nodal status was seen. Conclusion The expression of hTR and hTERT seems to be independent of tumour's stage. hTR expression probably plays a greater role in mammary tumourogenesis in younger women (≤ 40 years) and this may have therapeutic implications in the context of hTR targeting strategies

Pengaruh Inflasi, Jumlah Tenaga Kerja, dan Pengeluaran Pemerintah terhadap Pertumbuhan Ekonomi Bali

GDP is an indicator used to find out the condition of the economy in a region. The aimsof this study was to find out the effect of inflation, labor, and government spending on Bali economic growth simultaneously and partially. This study used multiple linear regression, the results shows simultaneously and partially have effect on inflation rates, the amount of labor, and government spending significant effect on the Bali economic growth

Open Access Article Processing Charges (OA APC) Longitudinal Study 2015 Preliminary Dataset

This article documents Open access article processing charges (OA APC) longitudinal study 2015 preliminary dataset available for download from the OA APC dataverse [1]. This dataset was gathered as part of Sustaining the Knowledge Commons (SKC), a research program funded by Canada’s Social Sciences and Humanities Research Council. The overall goal of SKC is to advance our collective knowledge about how to transition scholarly publishing from a system dependent on subscriptions and purchase to one that is fully open access. The OA APC preliminary data 2015 Version 12 dataset was developed as one of the lines of research of SKC, a longitudinal study of the minority (about a third) of the fully open access journals that use this business model. The original idea was to gather data during an annual two-week census period. The volume of data and growth in this area makes this an impractical goal. For this reason, we are posting this preliminary dataset in case it might be helpful to others working in this area. Future data gathering and analyses will be conducted on an ongoing basis. We encourage others to share their data as well. In order to merge datasets, note that the two most critical elements for matching data and merging datasets are the journal title and ISSN

Evaluation of nipple aspirate fluid as a diagnostic tool for early detection of breast cancer

YesThere has been tremendous progress in detection of breast cancer in postmenopausal women, resulting in two-thirds of women surviving more than 20 years after treatment. However, breast cancer remains the leading cause of cancerrelated deaths in premenopausal women. Breast cancer is increasing in younger women due to changes in life-style as well as those at high risk as carriers of mutations in high-penetrance genes. Premenopausal women with breast cancer are more likely to be diagnosed with aggressive tumours and therefore have a lower survival rate. Mammography plays an important role in detecting breast cancer in postmenopausal women, but is considerably less sensitive in younger women. Imaging techniques, such as contrast-enhanced MRI improve sensitivity, but as with all imaging approaches, cannot differentiate between benign and malignant growths. Hence, current well-established detection methods are falling short of providing adequate safety, convenience, sensitivity and specificity for premenopausal women on a global level, necessitating the exploration of new methods. In order to detect and prevent the disease in high risk women as early as possible, methods that require more frequent monitoring need to be developed. The emergence of “omics” strategies over the last 20 years, enabling the characterisation and understanding of breast cancer at the molecular level, are providing the potential for long term, longitudinal monitoring of the disease. Tissue and serum biomarkers for breast cancer stratification, diagnosis and predictive outcome have emerged, but have not successfully translated into clinical screening for early detection of the disease. The use of breast-specific liquid biopsies, such as nipple aspirate fluid (NAF), a natural secretion produced by breast epithelial cells, can be collected non-invasively for biomarker profiling. As we move towards an age of active surveillance, home-based liquid biopsy collection kits are increasingly being applied and these could provide a paradigm shift where NAF biomarker profiling is used for routine breast health monitoring. The current status of established and newly emerging imaging techniques for early detection of breast cancer and the potential for alternative biomarker screening of liquid biopsies, particularly those applied to high-risk, premenopausal women, will be reviewed.Proteomics research was supported by Yorkshire Cancer Research projects, BPP047 and B381PA, and co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation projects ΥΓΕΙΑ/ΒΙΟΣ/0311(ΒΙΕ/07) and NEKYP/0311/17

Can data from space address critical data gaps on earth? Investigating the extent to which holistic disaster risk can be estimated using remote sensing data

Disasters, caused by natural or human related hazards, claim tens of thousands of lives and cause over $300 billion in direct asset losses annually. While only 22% of disaster events occur in low-income countries, those events are disproportionately responsible for 67% of deaths, even when controlling for magnitude and scale. The discrepancy is largely due to differences in exposure and vulnerability. Despite growing recognition of these factors, disaster risk assessments often suffer from critical gaps in high-quality, location-based hazard, exposure, and vulnerability data. This thesis addresses these gaps by developing novel methods that integrate remote sensing and socio-economic data for multi-dimensional disaster risk assessment. First, a methodological framework is developed and implemented that combines satellite-derived flood hazard data (fluvial, pluvial, and coastal) with population and poverty data, resulting in global and subnational flood disaster risk data. The coarse survey-derived poverty data is the most significant limitation to the spatial resolution of the results, so the second part of the thesis explores whether satellite data, including nightlights, land cover, and NO2 emissions, can be used to estimate highly granular economic activity. The approach developed is novel in several ways, including its use of geographic weighted regression, which enables spatial heterogeneity to be captured and evaluated. The results find that 1.81 billion people are exposed to high flood risk globally, with 89% located in low- and middle-income countries. Additionally, the thesis demonstrates that high resolution economic activity can be estimated using satellite data, with the model achieving an overall predictive performance of 0.95 (R2) with variables displaying spatially heterogeneous relationships across the study area. The implications of this research are far-reaching: improving the quality and availability of disaster risk data enable policy-makers to target interventions to the most vulnerable populations. The methodologies developed extend beyond flood risk, offering replicable approaches for other disaster risk assessments. Ultimately, this thesis contributes to a more informed, data-driven approach to disaster risk reduction, with the potential to save lives and reduce economic losses globally