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.
--------
Question: The Sec7 domain ADP-ribosylation factor (Arf) guanine nucleotide exchange factors (GEFs) are found in all eukaryotes, and are involved in membrane remodeling processes throughout the cell. This review is focused on members of the GBF/Gea and BIG/Sec7 subfamilies of Arf GEFs, all of which use the class I Arf proteins (Arf1-3) as substrates, and play a fundamental role in traYcking in the endoplasmic reticulum (ER)-Golgi and endosomal membrane systems. Members of the GBF/Gea and BIG/Sec7 subfamilies are large proteins on the order of 200 kDa, and they possess multiple homology domains. Phylogenetic analyses indicate that both of these subfamilies of Arf GEFs have members in at least Wve out of the six eukaryotic supergroups, and hence were likely present very early in eukaryotic evolution. The homology domains of the large Arf1 GEFs play important functional roles, and are involved in interactions with numerous protein partners. The large Arf1 GEFs have been implicated in several human diseases. They are crucial host factors for the replication of several viral pathogens, including poliovirus, coxsackievirus, mouse hepatitis coronavirus, and hepatitis C virus. Mutations in the BIG2 Arf1 GEF have been linked to autosomal recessive periventricular heterotopia, a disorder of neuronal migration that leads to severe malformation of the cerebral cortex. Understanding the roles of the Arf1 GEFs in membrane dynamics is crucial to a full understanding of traYcking in the secretory and endosomal pathways, which in turn will provide essential insights into human diseases that arise from misregulation of these pathways.

Answer: Large Arf1 guanine nucleotide exchange factors: evolution, domain structure, and roles in membrane traYcking and human disease


Question: This research reports the design, analysis, integration, and test of a prototype of a real-time convective polymerase chain reaction (RT-cPCR) machine that uses a color charged coupled device (CCD) for detecting the emission of fluorescence intensity from an RT-cPCR mix in a microliter volume glass capillary. Because of its simple mechanism, DNA amplification involves employing the cPCR technique with no need for thermocycling control. The flow pattern and temperature distribution can greatly affect the cPCR process in the capillary tube, a computational fluid dynamics (CFD) simulation was conducted in this study for the first time to estimate the required period of an RT-cPCR cycle. This study also tested the PCR mix containing hepatitis B virus (HBV) plasmid samples by using SYBR Green I fluorescence labeling dye to assess the prototype performance. The measured results from the image-processing scheme indicate that the RT-cPCR prototype with a CCD-based fluorometer can achieve similar DNA quantification reproducibility compared to commercial machines, even when the initial DNA concentration in the test PCR mix is reduced to 10 copies/L

Answer: A real-time convective PCR machine in a capillary tube instrumented with a CCD-based fluorometer


Question: The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies. Citation: Salathé M, Jones JH (2010) Dynamics and Control of Diseases in Networks with Community Structure. PLoS Comput Biol 6(4): e1000736.

Answer:
Dynamics and Control of Diseases in Networks with Community Structure