Given the task definition, example input & output, solve the new input case.
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.
Example: The severe acute respiratory syndrome (SARS) epidemic originating from China in 2002 was caused by a previously uncharacterized coronavirus that could be identified by specific RT-PCR amplification. Efforts to control future SARS outbreaks depend on the accurate and early identification of SARS-CoV infected patients. A real-time fluorogenic RT-PCR assay based on the 3 -noncoding region (3 -NCR) of SARS-CoV genome was developed as a quantitative SARS diagnostic tool. The ideal amplification efficiency of a sensitive SARS-CoV RT-PCR assay should yield an E value (PCR product concentration increase per amplification cycle) equal to 2.0. It was demonstrated that the 3 -NCR SARS-CoV based RT-PCR reactions could be formulated to reach excellent E values of 1.81, or 91% amplification efficacy. The SARS-CoV cDNA preparations derived from viral RNA extract and the cloned recombinant plasmid both exhibit the identical amplification characteristics, i.e. amplification efficacy using the same PCR formulation developed in this study. The viral genomic copy (or genomic equivalences, GE) per infectious unit (GE/pfu) of SARS-CoV used in this study was also established to be approximate 1200-1600:1. The assay's detection sensitivity could reach 0.005 pfu or 6-8 GE per assay. It was preliminarily demonstrated that the assay could efficiently detect SARS-CoV from clinical specimens of SARS probable and suspected patients identified in Taiwan. The 3 -NCR based SARS-CoV assay demonstrated 100% diagnostic specificity testing samples of patients with acute respiratory disease from a non-SARS epidemic region.
Output: Development and evaluation of an efficient 3 -noncoding region-based SARS coronavirus (SARS-CoV) RT-PCR assay for detection of SARS-CoV
It's a correct title because the paragraph described the development of 3 -noncoding region of SARS-CoV genome and assay efficiently detecting SARS-CoV from clinical specimens.

New input case for you: Ecological niche modeling (ENM) is widely employed in ecology to predict species' potential geographic distributions in relation to their environmental constraints and is rapidly becoming the gold-standard method for disease risk mapping. However, given the biological complexity of disease systems, the traditional ENM framework requires reevaluation. We provide an overview of the application of ENM to disease systems and propose a theoretical framework based on the biological properties of both hosts and parasites to produce reliable outputs resembling disease system distributions. Additionally, we discuss the differences between biological considerations when implementing ENM for distributional ecology and epidemiology. This new framework will help the field of disease ecology and applications of biogeography in the epidemiology of infectious diseases. The recent rise of emerging infectious diseases (EIDs) (see Glossary) [1] has increased the burden of infectious diseases and negatively impacted the global economy [2][3][4] [5] . Approximately 60% of emerging human diseases are caused by pathogenic parasites of animal origin (zoonoses), particularly wildlife [6] . As human activities intensify, contact with wildlife and exposure to novel parasites increase, potentially driving zoonotic disease emergence [1, 7] . Given the threat that EIDs pose to human populations, understanding the underlying drivers of parasite geographic distribution and their spillover to humans is particularly relevant for epidemiologists, public-health practitioners, and policy makers [9] . Ecological niche modeling (ENM) has proven useful to forecast the distribution of a vast number of organisms [10-13] and is increasingly employed to predict parasite distributions locally and globally [14] [15] [16] [17] . Despite great strides made in the implementation of ENM to forecast complex biological phenomena such as disease systems [18], traditional frameworks may render biologically unrealistic predictions and thus must be revised, as we show in this review. We provide an overview of the current state of disease ENM and propose a framework based on the biological properties of both parasites and hosts to produce reliable outputs resembling disease systems distributions. Specifically, our theoretical framework: (i) addresses the selection of an appropriate modeling approach and highlights the importance of including biologically sound predictor variables; (ii) proposes the concept of a microscale parasitic niche defined by host traits to identify relevant parasite-host associations; and (iii) integrates traditional parasite ENM with the proposed microscale niche to better understand geographic distributions and improve fine-scale predictions of disease transmission risk. ENM estimates the distributions of species by linking their geographic occurrence with their environmental constraints, often utilizing correlative approaches (detailed explanations in [18] ). Highlights
Output:
An Ecological Framework for Modeling the Geography of Disease Transmission