TASK DEFINITION: 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.
PROBLEM: Glycyrrhetinic acid monoglucuronide (GAMG) is a great value-added and has considerable commercial interest due to its strong pharmacological activities and functional low-calorie sweetener. However GAMG is quite rare in natural plants, and it must be prepared from glycyrrhizin (GL) by hydrolysing one terminal glucuronic acid. β-Glucuronidase is the key enzyme in the biotransformation of GL to GAMG, but its activities need to be enhanced to facilitate the industrial large-scale production of GAMG. In this study, we identified that isoliquiritigenin (ISL), as one of chemical compositions from the total flavonoids glycyrrhiza (TFG), can significantly enhance β-glucuronidase activity in vitro. Measurements using high-performance liquid chromatography (HPLC) showed that the activity of β-glucuronidase could be increased by 2.66-fold via the addition of ISL to a β-glucuronidase solution that contained GL at a 3:10 molar ratio of ISL to GL. ISL was concluded to be an activator because ISL could reduce the K m and E a of β-glucuronidase reacting with GL. This study sheds new light on the mechanism of βglucuronidase and helps to make industrial production of GAMG through fermentation feasible. Published: xx xx xxxx OPEN www.nature.com/scientificreports/ 2 Scientific RepoRts | 7: 12503 |

SOLUTION: Identification of isoliquiritigenin as an activator that stimulates the enzymatic production of glycyrrhetinic acid monoglucuronide OPEN

PROBLEM: Dendritic cells (DCs) are recognized as highly potent antigen-presenting cells that are able to stimulate cytotoxic T lymphocyte (CTL) responses with antitumor activity. Consequently, DCs have been explored as cellular vaccines in cancer immunotherapy. To that end, DCs are modified with tumor antigens to enable presentation of antigen-derived peptides to CTLs. In this review we discuss the use of viral vectors for in situ modification of DCs, focusing on their clinical applications as anticancer vaccines. Among the viral vectors discussed are those derived from viruses belonging to the families of the Poxviridae, Adenoviridae, Retroviridae, Togaviridae, Paramyxoviridae, and Rhabdoviridae. We will further shed light on how the combination of viral vector-based vaccination with T-cell supporting strategies will bring this strategy to the next level. Since their discovery in 1973, it was clear that dendritic cells (DCs) stood out above the immune cell pack (1, 2). They are morphologically distinct from all other immune cell types and are gifted with an unparalleled capacity to take up, process and present self and foreign antigens to both CD4 + and CD8 + T cells. DCs are critical intermediaries between the innate and adaptive immune systems, as they stimulate, regulate, and shape both immunity and tolerance in all its disguises. Ralph Steinmann, who discovered these cells, was awarded the Nobel Prize for Medicine in 2011, because the discovery of DCs changed medicine (3). Dendritic cells in both humans and mice represent a population of at least four different subtypes with distinct phenotypical and functional characteristics (4-7). These subsets are: plasmacytoid DCs (pDCs), two subsets of conventional DCs (cDC1 and cDC2), and inflammatory DCs. The latter represent a monocyte-derived subset that appears during inflammatory responses (Table 1) . Recently, additional types of human blood DCs, monocytes, and progenitors were revealed using single cell RNA-sequencing. The group of Prof. Nir Hacohen identified pDCs next to cDC progenitor-derived cDC1 (Clec9A + ) and two types of CD1c + cDC2, of which one can also be derived from CD14 + DCs. Furthermore they found a CD141 − CD1c − CD11c + DC subset derived from CD16 + monocytes and an AXL + Siglec6 + subset (8). Future research will have to unravel a possible murine representative for the human cDC2 and AXL + Siglec6 + DC subset. Also, Langerhans cells have been considered an important DC subset for vaccination as they are localized in the epidermis (HLA-DR + CD11c + CD1a + CD207 + ). However, recent evidence suggests that they are related to macrophages, another antigen-presenting cell (APC) type with potential antitumor activity (9).

SOLUTION: The Journey of in vivo Virus Engineered Dendritic Cells From Bench to Bedside: A Bumpy Road

PROBLEM: Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data-and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.

SOLUTION:
Spatial and temporal epidemiological analysis in the Big Data era