You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["HealthCareActivity", "InjuryOrPoisoning", "BodySubstance", "IntellectualProduct", "AnatomicalStructure", "SpatialConcept", "Chemical", "Bacterium", "MedicalDevice", "Organization", "BiomedicalOccupationOrDiscipline", "Finding", "BiologicFunction", "Virus", "ResearchActivity", "ClinicalAttribute", "PopulationGroup", "Eukaryote", "BodySystem", "Food", "ProfessionalOrOccupationalGroup"].

Please return the output as a JSON object of the format: {"Virus": ["HIV", ...], "Bacterium": ["MRSA", ...], "AnatomicalStructure": ["Lung", ...], "BodySystem": ["CNS", ...], "BodySubstance": ["Serum", ...], "Finding": ["Headache", ...], "InjuryOrPoisoning": ["Fracture", ...], "BiologicFunction": ["Death", ...], "HealthCareActivity": ["Biopsy", ...], "ResearchActivity": ["Clinical trial", ...], "MedicalDevice": ["Lenses", ...], "SpatialConcept": ["Camps", ...], "BiomedicalOccupationOrDiscipline": ["Forensic medicine", ...], "Organization": ["WHO", ...], "ProfessionalOrOccupationalGroup": ["Provider", ...], "PopulationGroup": ["Swimmers", ...], "Chemical": ["Gold", ...], "Food": ["Rice", ...], "IntellectualProduct": ["RPAM", ...], "ClinicalAttribute": ["Biomarker", ...], "Eukaryote": ["Dogs", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Selection of a marker gene to construct a reference library for wetland plants, and the application of metabarcoding to analyze the diet of wintering herbivorous waterbirds Food availability and diet selection are important factors influencing the abundance and distribution of wild waterbirds. In order to better understand changes in waterbird population, it is essential to figure out what they feed on. However, analyzing their diet could be difficult and inefficient using traditional methods such as microhistologic observation. Here, we addressed this gap of knowledge by investigating the diet of greater white-fronted goose Anser albifrons and bean goose Anser fabalis, which are obligate herbivores wintering in China, mostly in the Middle and Lower Yangtze River floodplain. First, we selected a suitable and high-resolution marker gene for wetland plants that these geese would consume during the wintering period. Eight candidate genes were included: rbcL, rpoC1, rpoB, matK, trnH-psbA, trnL (UAA), atpF-atpH, and psbK-psbI. The selection was performed via analysis of representative sequences from NCBI and comparison of amplification efficiency and resolution power of plant samples collected from the wintering area. The trnL gene was chosen at last with c/h primers, and a local plant reference library was constructed with this gene. Then, utilizing DNA metabarcoding, we discovered 15 food items in total from the feces of these birds. Of the 15 unique dietary sequences, 10 could be identified at specie level. As for greater white-fronted goose, 73% of sequences belonged to Poaceae spp., and 26% belonged to Carex spp. In contrast, almost all sequences of bean goose belonged to Carex spp. (99%). Using the same samples, microhistology provided consistent food composition with metabarcoding results for greater white-fronted goose, while 13% of Poaceae was recovered for bean goose. In addition, two other taxa were discovered only through microhistologic analysis. Although most of the identified taxa matched relatively well between the two methods, DNA metabarcoding gave taxonomically more detailed information. Discrepancies were likely due to biased PCR amplification in metabarcoding, low discriminating power of current marker genes for monocots, and biases in microhistologic analysis. The diet differences between two geese species might indicate deeper ecological significance beyond the scope of this study. We concluded that DNA metabarcoding provides new perspectives for studies of herbivorous waterbird diets and inter-specific interactions, as well as new possibilities to investigate interactions between herbivores and plants. In addition, microhistologic analysis should be used together with metabarcoding methods to integrate this information.
{"AnatomicalStructure": ["rbcL", "candidate genes", "rpoB", "trnL", "rpoC1", "matK", "trnL gene", "marker genes", "gene", "marker gene"], "Bacterium": [], "BiologicFunction": ["amplification"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": ["feces"], "BodySystem": [], "Chemical": ["c/h primers"], "ClinicalAttribute": [], "Eukaryote": ["greater white-fronted goose Anser albifrons", "waterbirds", "greater white-fronted goose", "plants", "geese", "Carex spp", "waterbird", "herbivorous", "obligate herbivores", "birds", "Poaceae", "herbivores", "bean goose Anser fabalis", "monocots", "plant", "Poaceae spp.", "bean goose"], "Finding": ["Discrepancies"], "Food": ["food composition", "dietary", "food items", "diets", "Food", "diet"], "HealthCareActivity": ["microhistologic observation", "microhistologic analysis", "microhistology"], "InjuryOrPoisoning": [], "IntellectualProduct": ["constructed", "specie", "wild", "species"], "MedicalDevice": [], "Organization": ["reference library", "NCBI"], "PopulationGroup": [], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["metabarcoding", "study", "analyzing", "studies", "PCR", "analysis", "analyze", "metabarcoding methods", "DNA metabarcoding"], "SpatialConcept": ["Lower Yangtze River floodplain", "trnH-psbA", "Middle", "atpF-atpH", "psbK-psbI", "sequences", "wintering area", "local", "China", "UAA"], "Virus": []}