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.

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.
Microbial drug discovery: 80 years of progress

This study uses panel regression tests to examine the response of hotel performance to international tourism development and crisis events in Taiwan. Hotel performance measures are revenue (revenue per available room and occupancy rate), profitability (return on assets and return on equity) and stock performance. The crises were the earthquake on September 21, 1999 (the 9/21 earthquake), the terrorist attacks of September 11, 2001 in the US (the 9/11 terrorist attacks) and the outbreak of Severe Acute Respiratory Syndrome on April 22, 2003 (the SARS outbreak). This study makes four major contributions. First, test results confirm that international tourism development (ITD), proxied by the growth of total inbound tourist arrivals, has a more direct influence on hotel sales and profitability than it does on hotel stock performance. Second, this study identifies that the absence of a strong tie between ITD and hotel stock returns that was found in previous studies is due to the time-varying discount rate caused by investors' changing expectations for the prospect of future cash flows from holding hotel stocks. Third, this study finds new evidence that while the poor performance of hotel stocks caused by the 9/21 earthquake and the 9/11 terrorist attacks was attributed to the loss of hotel sales revenue, the adverse effect of the SARS outbreak on hotel stock returns is attributed not only to decreased hotel sales revenue but also to the increased discount rate. Lastly, this study is the first to investigate whether the response of hotel stock returns to ITD depends on the state of economy and concludes that the response of hotel stock performance to ITD in business cycle contraction is statistically different from that in business cycle expansion. Further, although the influence of ITD on hotel stock performance is still irrelevant during expansion periods, ITD can significantly enhance hotel stock returns during contraction periods.
The response of hotel performance to international tourism development and crisis events

Introduction: The research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques. Methods: Here, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor's diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis. Results: It achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria. Discussion: The auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level.

Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically