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.

Ex Input:
Protein chips have emerged as a promising approach for a wide variety of applications including the identification of protein-protein interactions, protein-phospholipid interactions, small molecule targets, and substrates of proteins kinases. They can also be used for clinical diagnostics and monitoring disease states. This article reviews current methods in the generation and applications of protein microarrays. Understanding complex cellular systems will require the identification and analysis of each of its components and determining how they function together and are regulated. A critical step in this process is to determine the biochemical activities of the proteins and how these activities themselves are controlled and modified by other proteins. Traditionally, the biochemical activities of proteins have been elucidated by studying single molecules, one experiment at a time. This process is not optimal, as it is slow and labor intensive. In contrast to this traditional approach, high-throughput scientific methods have been developed in the last decade to optimize the study of large numbers of molecules, including DNA, proteins and metabolites. DNA microarrays in particular have proved valuable in genomic research (Schena et al., 1995) . They have been used to study gene expression patterns, to locate transcription factor binding sites, and to detect sequence mutations and deletions on a grand scale. However, DNA microarrays tell us only about the genes themselves and provide little information regarding the functions of the proteins they encode. More recently high throughput approaches have been developed for the study of proteins, including profiling of proteins using mass spectrometry (Gavin et al.,

Ex Output:
Protein Microarray Technology Types of protein microarrays


Ex Input:
Several kinds of pressure can lead to the emergence of infectious diseases. In the case of zoonoses emerging from livestock, one of the most significant changes that has taken place since the mid twentieth century is what has been termed the "livestock revolution", whereby the stock of food animals, their productivity and their trade has increased rapidly to feed rising and increasingly wealthy and urbanized populations. Further increases are projected in the future in low and middle-income countries. Using avian influenza as an example, we discuss how the emergence of avian influenza H5N1 and H7N9 in China was linked to rapid intensification of the poultry sector taking place in landscapes rich in wetland agriculture and wild waterfowls habitats, providing an extensive interface with the wild reservoir of avian influenza viruses. Trade networks and live-poultry markets further exacerbated the spread and persistence of avian influenza as well as human exposure. However, as the history of emergence of highly pathogenic avian influenza (HPAI) demonstrates in high-income countries such as the USA, Canada, Australia, the United Kingdom or the Netherlands, this is by no way specific to low and middle-income countries. Many HPAI emergence events took place in countries with generally good biosecurity standards, and the majority of these in regions hosting intensive poultry production systems. Emerging zoonoses are only one of a number of externalities of intensive livestock production systems, alongside antimicrobial consumption, disruption of nutrient cycles and greenhouse gases emissions, with direct or indirect impacts on human health. In parallel, livestock production is essential to nutrition and livelihoods in many low-income countries. Deindustrialization of the most intensive production systems in high-income countries and sustainable intensifications in low-income countries may converge to a situation where the nutritional and livelihood benefits of livestock production would be less overshadowed by its negative impacts on human an ecosystem health.

Ex Output:
Intensifying poultry production systems and the emergence of avian influenza in China: a 'One Health/Ecohealth' epitome


Ex Input:
As infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decisionmaking in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population. misinformation from the "anti-vaxxer" movement [8, [11] [12] [13] . Recently, there have been calls for a special government commission on vaccine safety, despite overwhelming scientific consensus that vaccines are both safe and effective [12, [14] [15] [16] . As outbreaks unfold, people can take a variety precautionary measures to avoid infection, including immunization and social distancing [1, [17] [18] [19] . They often judge personal risk based on their impressions of overall disease prevalence and severity [2, [20] [21] [22] [23] . When infection risk appears low, small risks of adverse effects from the vaccine can seem relatively important and cause vaccine coverage to drop below levels required to control transmission. A variety of other factors can influence the perceived utility of disease prevention, including epidemiological news and rumors, costs of vaccination and other control measures, trust in health professionals, government agencies, media and non-official information sources, as well as societal pressure to ensure the health of one's children [24] [25] [26] [27] [28] [29] . Studies have shown that media reports about outbreaks that specify numbers of cases, hospitalizations or deaths, can influence avoidance behavior and contact patterns at both individual and community levels. In some cases, oversimplified or erroneous media reports regarding flawed vaccines can trigger panic and increases in vaccine hesitancy [30] [31] [32] [33] . For both seasonal and pandemic influenza, such interactions between vaccination decision-making and transmission dynamics can profoundly shape the course of epidemics [20, [34] [35] [36] . Individual intervention decisions can have far-reaching effects. For example, vaccination protects not only the immunized individual, but also social contacts who might have been infected by the individual. Social distancing decisions may break chains of transmission by protecting the decision-maker and more generally disrupting social dynamics. Following [37, 38] , we refer to this indirect protection as a herd effect. Although previously equated to the reduction in incidence in the unimmunized population [38], we quantify the herd effect of an intervention effort by estimating the number of infections averted per vaccine administered (or per individual social distancing action). The general phenomenon in which individual intervention actions reduce the risk of infection to others has also been called vaccination efficiency, vaccination effectiveness, herd immunity, and indirect protection [37] [38] [39] [40] . The magnitude of the herd effect will critically depend on contact patterns [41, 42]. Measures taken by gregarious individuals may have greater immediate benefits than those taken by solitary individuals, with downstream epidemiological consequences modulated by the full social network [43, 44] . Contact patterns may also influence the decision-making process itself, by constraining epidemiological perspectives. When gauging infection risk, individuals may consider global information (e.g., from news media) or local first-hand encounters with disease (e.g., infected acquaintances, friends or family members) [30, 31] . While traditional compartmental models assume homogeneity in both epidemiological risks and intervention benefits, network-based models provide a tractable framework for studying the complex interplay between contact networks, intervention decision making and disease transmission [34, [45] [46] [47] [48] [49] [50] [51] . Here, we investigate the epidemiological impacts of different decision paradigms using a network-based SIR epidemic model, in which individuals also make vaccination or social distancing choices based on their perceived epidemiological risks. Depending on the decision model, they estimate either overall disease prevalence, their number of infected social contacts, or their fraction of infected social contacts. When the perceived threat is sufficiently high, they take a measure that immediately affords full protection for the duration of the epidemic. We compare the efficacy of these three different paradigms across a range of diseases in a realistically heterogeneous network, and show that the most naive model-simply counting one's infected contacts-affords the most epidemiological protection using the least amount of resources (e.g., vaccinations or economic costs associated with social distancing). Local risk perception enhances epidemic control PLOS ONE | https://doi.

Ex Output:

Local risk perception enhances epidemic control