The COVID-19 vaccine communication handbook. A practical guide for improving vaccine communication and fighting misinformation

This handbook is for journalists, doctors, nurses, policy makers, researchers, teachers, students, parents – in short, it’s for everyone who wants to know more: About the COVID-19 vaccines; How to talk to others about them; How to challenge misinformation about the vaccines.Published versio

Modelling the time-varying cell capacity in LTE networks

In wireless orthogonal frequency-division multiple access (OFDMA) based networks like Long Term Evolution (LTE) or Worldwide Interoperability for Microwave Access (WiMAX) a technique called adaptive modulation and coding (AMC) is applied. With AMC, different modulation and coding schemes (MCSs) are used to serve different users in order to maximise the throughput and range. The used MCS depends on the quality of the radio link between the base station and the user. Data is sent towards users with a good radio link with a high MCS in order to utilise the radio resources more efficiently while a low MCS is used for users with a bad radio link. Using AMC however has an impact on the cell capacity as the quality of a radio link varies when users move around; this can even lead to situations where the cell capacity drops to a point where there are too little radio resources to serve all users. AMC and the resulting varying cell capacity notably has an influence on admission control (AC). AC is the algorithm that decides whether new sessions are allowed to a cell or not and bases its decisions on, amongst others, the cell capacity. The analytical model that is developed in this paper models a cell with varying capacity caused by user mobility using a continuous -time Markov chain (CTMC). The cell is divided into multiple zones, each corresponding to the area in which data is sent towards users using a certain MCS and transitions of users between these zones are considered. The accuracy of the analytical model is verified by comparing the results obtained with it to results obtained from simulations that model the user mobility more realistically. This comparison shows that the analytical model models the varying cell capacity very accurately; only under extreme conditions differences between the results are noticed. The developed analytical and simulation models are then used to investigate the effects of a varying cell capacity on AC. Also, an optimisation algorithm that adapts the parameter of the AC algorithm which determines the amount of resources that are reserved in order to mitigate the effects of the varying cell capacity is studied using the models. Updating the parameter of the AC algorithm is done by reacting to certain triggers that indicate good or bad performance and adapt the parameters of the AC algorithm accordingly. Results show that using this optimisation algorithm improves the quality of service (QoS) that is experienced by the users.This work was partially supported by the Spanish Government through project TIN2010-21378-C02-02 and contract BES-2007-15030.Sas, B.; Bernal Mor, E.; Spaey, K.; Pla, V.; Blondia, C.; Martínez Bauset, J. (2014). Modelling the time-varying cell capacity in LTE networks. Telecommunication Systems. 55(2):299-313. https://doi.org/10.1007/s11235-013-9782-2S2993135523GPP (2010). 3GPP TR 36.213: Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Physical layer procedures, June 2010.3GPP (2010). 3GPP TR 36.942: Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Radio Frequency (RF) system scenarios, September 2010.Al-Rawi, M., & Jäntti, R. (2009). Call admission control with active link protection for opportunistic wireless networks. Telecommunications Systems, 41(1), 13–23.Bhatnagar, S., & Reddy, B.B.I. (2005). Optimal threshold policies for admission control in communication networks via discrete parameter stochastic approximation. Telecommunications Systems, 29(1), 9–31.Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing, 2(5), 483–502.E3. ict-e3.eu.Elayoubi, S.-E., & Chahed, T. (2005). Admission control in the downlink of WCDMA/UMTS. In LNCS: Vol. 3427. Mobile and wireless systems (pp. 136–151).Garcia, D., Martinez, J., & Pla, V. (2005). Admission control policies in multiservice cellular networks: optimum configuration and sensitivity. In G. Kotsis, & O. Spaniol (Eds.), Lecture notes in computer science: Vol. 3427. Wireless systems and mobility in next generation Internet (pp. 121–135).Guo, J., Liu, F., & Zhu, Z. (2007). Estimate the call duration distribution parameters in GSM system based on K-L divergence method. In International conference on wireless communications, networking and mobile computing (pp. 2988–2991), Shanghai, China, September 2007.Hossain, M., Hassan, M., & Sirisena, H. R. (2004). Adaptive resource management in mobile wireless networks using feedback control theory. Telecommunications Systems, 24(3–4), 401–415.Jeong, S.S., Han, J.A., & Jeon, W.S. (2005). Adaptive connection admission control scheme for high data rate mobile networks. In IEEE 62nd Vehicular technology conference, 2005. VTC-2005-Fall (Vol. 4, pp. 2607–2611).Kim, D.K., Griffith, D., & Golmie, N. (2010). A novel ring-based performance analysis for call admission control in wireless networks. IEEE Communications Letters, 14(4), 324–326.Latouche, G., & Ramaswami, V. (1999). Introduction to matrix analytic methods in stochastic modeling. ASA-SIAM. Baltimore: Philadelphia.MONOTAS. http://www.macltd.com/monotas .Neuts, M. (1981). Matrix-geometric solutions in stochastic models: an algorithmic approach. Baltimore: The Johns Hopkins University Press.NGMN. NGMN Radio Access Performance Evaluation Methodology, January 2008.NGMN. www.ngmn.org .Prehofer, C., & Bettstetter, C. (2005). Self-organization in communication networks: principles and design paradigms. IEEE Communications Magazine, 43(7), 78–85.Ramjee, R., Nagarajan, R., & Towsley, D. (1997). On optimal call admission control in cellular networks. Wireless Networks, 3(1), 29–41.Siwko, J., & Rubin, I. (2001). Call admission control for capacity-varying networks. Telecommunications Systems, 16(1–2), 15–40.SOCRATES. www.fp7-socrates.eu .Spaey, K., Sas, B., & Blondia, C. (2010). Self-optimising call admission control for LTE downlink. In COST 2100 TD(10)10056, Joint Workshop COST 2100 SWG 3.1 & FP7-ICT-SOCRATES, Athens, Greece.Spilling, A. G., Nix, A. R., Beach, M. A., & Harrold, T. J. (2000). Self-organisation in future mobile communications. Electronics & Communication Engineering Journal, 3, 133

Comparative survival analysis of breast cancer microarray studies identifies important prognostic genetic pathways

<p>Abstract</p> <p>Background</p> <p>An estimated 12% of females in the United States will develop breast cancer in their lifetime. Although, there are advances in treatment options including surgery and chemotherapy, breast cancer is still the second most lethal cancer in women. Thus, there is a clear need for better methods to predict prognosis for each breast cancer patient. With the advent of large genetic databases and the reduction in cost for the experiments, researchers are faced with choosing from a large pool of potential prognostic markers from numerous breast cancer gene expression profile studies.</p> <p>Methods</p> <p>Five microarray datasets related to breast cancer were examined using gene set analysis and the cancers were categorized into different subtypes using a scoring system based on genetic pathway activity.</p> <p>Results</p> <p>We have observed that significant genes in the individual studies show little reproducibility across the datasets. From our comparative analysis, using gene pathways with clinical variables is more reliable across studies and shows promise in assessing a patient's prognosis.</p> <p>Conclusions</p> <p>This study concludes that, in light of clinical variables, there are significant gene pathways in common across the datasets. Specifically, several pathways can further significantly stratify patients for survival. These candidate pathways should help to develop a panel of significant biomarkers for the prognosis of breast cancer patients in a clinical setting.</p

Altered regulation of Prox1-gene-expression in liver tumors

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

A Model of Brain Circulation and Metabolism: NIRS Signal Changes during Physiological Challenges

We construct a model of brain circulation and energy metabolism. The model is designed to explain experimental data and predict the response of the circulation and metabolism to a variety of stimuli, in particular, changes in arterial blood pressure, CO2 levels, O2 levels, and functional activation. Significant model outputs are predictions about blood flow, metabolic rate, and quantities measurable noninvasively using near-infrared spectroscopy (NIRS), including cerebral blood volume and oxygenation and the redox state of the CuA centre in cytochrome c oxidase. These quantities are now frequently measured in clinical settings; however the relationship between the measurements and the underlying physiological events is in general complex. We anticipate that the model will play an important role in helping to understand the NIRS signals, in particular, the cytochrome signal, which has been hard to interpret. A range of model simulations are presented, and model outputs are compared to published data obtained from both in vivo and in vitro settings. The comparisons are encouraging, showing that the model is able to reproduce observed behaviour in response to various stimuli

General anaesthetic and airway management practice for obstetric surgery in England: a prospective, multicentre observational study

There are no current descriptions of general anaesthesia characteristics for obstetric surgery, despite recent changes to patient baseline characteristics and airway management guidelines. This analysis of data from the direct reporting of awareness in maternity patients’ (DREAMY) study of accidental awareness during obstetric anaesthesia aimed to describe practice for obstetric general anaesthesia in England and compare with earlier surveys and best-practice recommendations. Consenting patients who received general anaesthesia for obstetric surgery in 72 hospitals from May 2017 to August 2018 were included. Baseline characteristics, airway management, anaesthetic techniques and major complications were collected. Descriptive analysis, binary logistic regression modelling and comparisons with earlier data were conducted. Data were collected from 3117 procedures, including 2554 (81.9%) caesarean deliveries. Thiopental was the induction drug in 1649 (52.9%) patients, compared with propofol in 1419 (45.5%). Suxamethonium was the neuromuscular blocking drug for tracheal intubation in 2631 (86.1%), compared with rocuronium in 367 (11.8%). Difficult tracheal intubation was reported in 1 in 19 (95%CI 1 in 16–22) and failed intubation in 1 in 312 (95%CI 1 in 169–667). Obese patients were over-represented compared with national baselines and associated with difficult, but not failed intubation. There was more evidence of change in practice for induction drugs (increased use of propofol) than neuromuscular blocking drugs (suxamethonium remains the most popular). There was evidence of improvement in practice, with increased monitoring and reversal of neuromuscular blockade (although this remains suboptimal). Despite a high risk of difficult intubation in this population, videolaryngoscopy was rarely used (1.9%)