In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Input: Consider Input: Being madly in love with all the advances in minimally invasive medicine and technology I often tell my friends the stories of how interventional radiology (IR) changes lives. One of these unintended stories led to a much bigger audience of TED community and spread the word about IR far beyond the circle of my friends. As the TED slogan promised, participating in the event 1 made me realize that our ideas of better medicine are worth sharing. It also made me think that better communication with the public is possible, and it does not take genius. It takes a willingness to try. We know that IR with its broad procedural spectrum, safety of minimally invasive approach, lower requirement for anesthesia and hospitalization, and cost-effectiveness has a strong stand in the evolving universal healthcare practice model. But, as Goethe said, knowing is not enough; we must apply. 2 Then, how can we as medical community increase the exercise of IR procedures? The starting point is to accept that many people, including medical professionals, do not know what diseases we can treat and what diagnostic or therapeutic services we can provide. Either it is a palliative treatment of metastases, or elective embolization of uterine fibroids, or emergent lysis of pulmonary embolism-it is our responsibility to find the appropriate patients and let both patients and primary physicians know what options we have to offer.

Output: Journal Pre-proof Guest Editorial: Radiology Nurses Can Raise the Impact on Promoting IR


Input: Consider Input: Microbes have made a phenomenal contribution to the health and well-being of people throughout the world. In addition to producing many primary metabolites, such as amino acids, vitamins and nucleotides, they are capable of making secondary metabolites, which constitute half of the pharmaceuticals on the market today and provide agriculture with many essential products. This review centers on these beneficial secondary metabolites, the discovery of which goes back 80 years to the time when penicillin was discovered by Alexander Fleming.

Output: Microbial drug discovery: 80 years of progress


Input: Consider Input: The spread of disease through human populations is complex. The characteristics of disease propagation evolve with time, as a result of a multitude of environmental and anthropic factors, this non-stationarity is a key factor in this huge complexity. In the absence of appropriate external data sources, to correctly describe the disease propagation, we explore a flexible approach, based on stochastic models for the disease dynamics, and on diffusion processes for the parameter dynamics. Using such a diffusion process has the advantage of not requiring a specific mathematical function for the parameter dynamics. Coupled with particle MCMC, this approach allows us to reconstruct the time evolution of some key parameters (average transmission rate for instance). Thus, by capturing the time-varying nature of the different mechanisms involved in disease propagation, the epidemic can be described. Firstly we demonstrate the efficiency of this methodology on a toy model, where the parameters and the observation process are known. Applied then to real datasets, our methodology is able, based solely on simple stochastic models, to reconstruct complex epidemics, such as flu or dengue, over long time periods. Hence we demonstrate that timevarying parameters can improve the accuracy of model performances, and we suggest that our methodology can be used as a first step towards a better understanding of a complex epidemic, in situation where data is limited and/or uncertain. Citation: Cazelles B, Champagne C, Dureau J (2018) Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models. PLoS Comput Biol 14(8): e1006211. https://doi.org/10.


Output: Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models