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title: 'Emergency department use and Artificial Intelligence in Pelotas: design and
baseline results'
authors:
- Felipe Mendes Delpino
- Lílian Munhoz Figueiredo
- Ândria Krolow Costa
- Ioná Carreno
- Luan Nascimento da Silva
- Alana Duarte Flores
- Milena Afonso Pinheiro
- Eloisa Porciúncula da Silva
- Gabriela Ávila Marques
- Mirelle de Oliveira Saes
- Suele Manjourany Silva Duro
- Luiz Augusto Facchini
- João Ricardo Nickenig Vissoci
- Thaynã Ramos Flores
- Flávio Fernando Demarco
- Cauane Blumenberg
- Alexandre Dias Porto Chiavegatto
- Inácio Crochemore da Silva
- Sandro Rodrigues Batista
- Ricardo Alexandre Arcêncio
- Bruno Pereira Nunes
journal: Revista Brasileira de Epidemiologia (Brazilian Journal of Epidemiology)
year: 2023
pmcid: PMC10000014
doi: 10.1590/1980-549720230021
license: CC BY 4.0
---
# Emergency department use and Artificial Intelligence in Pelotas: design and baseline results
## RESUMO
### Objetivo:
To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil.
### Methods:
The study is entitled “Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year.
### Results:
In total, 5,722 participants answered the survey, mostly females ($66.8\%$), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around $30\%$ of the sample has obesity, $14\%$ diabetes, and $39\%$ hypertension.
### Conclusion:
The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state.
## Objetivo:
Descrever os resultados iniciais da linha de base de um estudo de base populacional, bem como um protocolo para avaliar o desempenho de diferentes algoritmos de aprendizado de máquina, com o objetivo de predizer a demanda de serviços de urgência e emergência em uma amostra representativa de adultos da zona urbana de Pelotas, no Sul do Brasil.
## Métodos:
O estudo intitula-se “Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). Entre setembro e dezembro de 2021, foi realizada uma linha de base com os participantes. Está previsto um acompanhamento após 12 meses para avaliar a utilização de serviços de urgência e emergência no último ano. Em seguida, serão testados algoritmos de machine learning para predizer a utilização de serviços de urgência e emergência no período de um ano.
## Resultados:
No total, 5.722 participantes responderam à pesquisa, a maioria do sexo feminino (66,$8\%$), com idade média de 50,3 anos. O número médio de pessoas no domicílio foi de 2,6. A maioria da amostra tem cor da pele branca e ensino fundamental incompleto ou menos. Cerca de $30\%$ da amostra estava com obesidade, $14\%$ com diabetes e $39\%$ eram hipertensos.
## Conclusão:
O presente trabalho apresentou um protocolo descrevendo as etapas que foram e serão tomadas para a produção de um modelo capaz de prever a demanda por serviços de urgência e emergência em um ano entre moradores de Pelotas, no estado do Rio Grande do Sul.
## INTRODUCTION
Chronic diseases affect a large part of the population of adults and older adults, leading these individuals to seek urgent and emergency care. The implementation in 1988 of the Unified Health System (SUS) resulted in a model aimed at prevention and health promotion actions based on collective activities 1 – starting at Basic Health Units (UBS). There is also the National Emergency Care Policy, which advanced in the construction of the SUS, and has as guidelines universality, integrity, decentralization, and social participation, alongside humanization, the right of every citizen 2.
In a study that evaluated the characteristics of users of primary health care services in a Brazilian urban-representative sample, it was found that the vast majority were women and part of poorer individuals, in addition to almost $\frac{1}{4}$ of the sample receiving the national income distribution program (family allowance) 3. Brazil is a country highly unequal in socioeconomic terms; approximately $75\%$ of the Brazilian population uses the SUS and depends exclusively on it, and do not have private health insurance 4,5.
Individuals with multimorbidity are part of the vast majority who seek urgent and emergency services 6. Multimorbidity is a condition that affects a large part of the population 7, especially older adults 7. In addition, the association of multimorbidity with higher demand for emergency services is a challenge to appropriately manage and prevent these problems 8,9.
Innovative approaches may allow health professionals to provide direct care to individuals who are more likely to seek urgent and emergency services. The use of artificial intelligence can make it possible to identify and monitor a group of individuals with a higher probability of developing multimorbidity. In this context, machine learning (ML), an application of artificial intelligence, is a promising and feasible tool to be used on large scale to identify these population subgroups. Some previous studies have demonstrated that ML models can predict the demand for urgent and emergency services 10,11. Besides, a systematic review showed that ML could accurately predict the triage of patients entering emergency care 12. However, in a search for studies in Brazil, we found no published article on the subject.
In Brazil, urgent and emergency services are a fundamental part of the health care network, ensuring timely care in cases of risk to individuals’ lives 9. Urgent and emergency services are characterized by overcrowding and high demand. In addition, with the current pandemic of COVID-19, updated evidence on the characteristics of the users seeking these services is timely and necessary. The objective of this article was to describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different ML algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas.
## METHODS
The present cohort study is entitled “Emergency department use and Artificial Intelligence in PELOTAS-RS (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). The baseline was conducted between September and December 2021, and a follow-up was planned to be conducted 12 months later. We utilized the cross-sectional study to measure the prevalence of urgent and emergency care and the prevalence of multimorbidity, in addition to other variables and instruments of interest. The prospective cohort design intends to estimate the risk of using and reusing urgent emergency services after 12 months. Contact information, collected to ensure follow-up, included telephone, social networks, and full address. In addition, we also collected the latitude and longitude of households for control of the interviews.
## Study location and target population
The present study was conducted in adult households in the Pelotas, Rio Grande do Sul (RS), Southern Brazil. According to estimates by the Brazilian Institute of Geography and Statistics (IBGE) in 2020, Pelotas had an estimated population of 343,132 individuals (https://cidades.ibge.gov.br/brasil/rs/pelotas/panorama). Figure 1 shows the location of the city of Pelotas in Brazil.
**Figura 1.:** *Map of Brazil highlighting the city of Pelotas (RS).*
Pelotas has a human development index (HDI) of 0.739 and a gross domestic product per capita (GDP) of BRL 27,586.96 (https://www.ibge.gov.br/cidades-e-estados/rs/pelotas.html). The municipality has a Municipal Emergency Room that operates 24 hours a day, seven days a week, and serves about 300 patients a day, according to data provided by the unit.
## Criteria for inclusion and exclusion of study participants
We included adults aged 18 years or older residing in the urban area of Pelotas. Children and individuals who were mentally unable to answer the questionnaire were not included in the sample.
## Sample calculation, sampling process, and data collection
The sample size was calculated considering three objectives. First, to determine the sample size required to assess the prevalence of urgent and emergency services use, it was considered an estimated prevalence of $9\%$, with±two percentage points as a margin of error and a $95\%$ confidence level 13, concluding that 785 individuals would be necessary. Second, for multimorbidity prevalence, an estimated prevalence of $25\%$, with ± three percentage points as a margin of error and a confidence level of $95\%$ was used 14,15; reaching again, a total of 785 individuals needed. Finally, for the association calculations, similar studies in Brazil were assessed, and the following parameters were considered: significance level of $95\%$, power of $80\%$, exposed/unexposed ratio of 0.1, percentage of the outcome in the unexposed $20\%$, and a minimum prevalence ratio of 1.3. With these parameters, 5,104 individuals would be necessary to study the proposed associations. Adding 10 to $20\%$ for losses and/or refusals, the final sample size would be composed of 5,615–5,890 participants.
The process to provide a population-based sample was carried out in multiple stages. The city of Pelotas has approximately 550 census tracts, according to the last update estimates provided by IBGE in 2019. From there, we randomly selected 100 sectors. Since the sectors vary in size, we defined a proportional number of households for each.
Thus, it was estimated that, in total, the 100 sectors had approximately 24,345 eligible households. To interview one resident per household, we divided the total number of households by the sample size required, which resulted in 4.3. Based on this information, we divided each of the 100 sectors by 4.3 to reach the necessary number of households for each sector. One resident per household was interviewed, resulting in a total of 5,615 households. If there was more than one eligible resident, the choice was made by a random number generator application. Residents were placed in order, a number was assigned for each one, and one of them was selected according to the result of the draw. The first household interviewed in each sector was selected through a draw, considering the selected jump (4.3 households). Trades and empty squares were considered ineligible, and thus, the next square was chosen. Due to a large number of empty houses, it was necessary to select another 50 sectors to complete the required sample size. The additional households were drawn according to the same methodological criteria as the first draw to ensure equiprobability.
## Data collection instrument
We collected the data with the Research Electronic Data Capture (REDCap), a data collection program using smartphones 16,17. Experienced and trained research assistants collected the data. The questionnaire from EAI PELOTAS was prepared, when possible, based on standardized instruments, including questions about chronic diseases, physical activity, food security, use of urgent and emergency services, functional disability, frailty syndrome, self-perception of health, COVID-19, in addition to sociodemographic and behavioral questions. Supplementary Table 1 shows the instruments utilized in the present study.
**Table 1.**
| Characteristics | EAI PELOTAS* | EAI PELOTAS*.1 | PNS 2019† |
| --- | --- | --- | --- |
| Characteristics | Crude % (95%CI) | Survey design % (95%CI) | % (95%CI) |
| Mean age, years | 50.3 (49.9–50.8) | 46.2 (45.5–47.0) | 46.7 (45.9–47.5) |
| Mean number of household people | 2.6 (2.5–2.7) | 2.7 (2.6–2.8) | 3.0 (2.9–3.1) |
| Female (%) | 66.8 (65.6–68.0) | 54.2 (52.4–55.6) | 54.1 (51.7–56.4) |
| Skin color (%) | Skin color (%) | Skin color (%) | Skin color (%) |
| White | 78.2 (77.1–79.2) | 77.3 (74.9–79.5) | 76.8 (74.6–78.7) |
| Black | 15.0 (14.1–16.0) | 15.3 (13.5–17.3) | 8.3 (7.0–9.8) |
| Brown | 6.1 (5.5–6.7) | 6.7 (5.7–7.9) | 14.5 (12.9–16.3) |
| Other | 0.7 (0.5–1.0) | 0.7 (0.4–1.1) | 0.4 (0.2–0.8) |
| Schooling (%) | Schooling (%) | Schooling (%) | Schooling (%) |
| Incomplete elementary school or less | 35.7 (34.5–37.0) | 31.3 (28.6–34.2) | 30.2 (28.1–32.4) |
| Complete elementary school/incomplete high school | 16.2 (15.3–17.2) | 16.4 (15.1–17.7) | 15.7 (14.0–17.5) |
| Complete high school/incomplete higher education | 33.5 (32.3–34.7) | 37.6 (35.6–39.6) | 36.9 (34.6–39.2) |
| Complete higher education or more | 14.6 (13.7–15.5) | 14.7 (12.4–17.4) | 17.2 (15.7–18.9) |
## Dependent variables
The use of urgent and emergency services was assessed on a baseline using the following question: “In the last 12 months, how many times have you sought urgent and emergency services, such as an emergency room?”. This was followed by the characterization of the service used, city of service, frequency of use, and referral after use. One year after the study baseline, we will contact again the respondents to inquire about the use of urgent and emergency care services (number of times and type of service used).
## Independent variables
We assessed multimorbidity as the main exposure using a list of 22 chronic diseases and others (asthma/bronchitis, osteoporosis, arthritis/arthrosis/rheumatism, hypertension, diabetes, cardiac insufficiency, pulmonary emphysema/chronic obstructive pulmonary disease, acute kidney failure, Parkinson’s disease, prostate disease, hypo/hyperthyroidism, glaucoma, cataract, Alzheimer’s disease, urinary/fecal incontinence, angina, stroke, dyslipidemia, epileptic fit/seizures, depression, gastric ulcer, urinary infection, pneumonia, and the flu). The association with urgent and emergency services will be performed with different cutoff points, including total number, ≥2, ≥3, and combinations of morbidities. We will also perform network analyzes to assess the pattern of morbidities.
Other independent variables were selected from previous studies in the literature 18-21, including demographic, socioeconomic information, behavioral characteristics, health status, access, use and quality of health services.
## Data analysis
We will test artificial intelligence algorithms, ML, to predict the use of urgent and emergency services after 12 months. The purpose of ML is to predict health outcomes through the basic characteristics of the individuals, such as sex, education, and lifestyle. The algorithms will be trained to predict the occurrence of health outcomes, which will contribute to decision-making. With a good amount of data and the right algorithms, ML may be able to predict health outcomes with satisfactory performance.
The area of ML in healthcare has shown rapid growth in recent years, having been used in significant public health problems such as diagnosing diseases and predicting the risk of adverse health events and deaths 22-24. The use of predictive algorithms aims to improve health care and support decision-making by health professionals and managers. For the present study, individuals’ baseline characteristics will be used to train popular ML algorithms such as Support Vector Machine (SVM), Neural Networks (ANNs), Random Forests, Penalized Regressions, Gradient Boosted Trees, and Extreme Gradient Boosting (XGBoost). These models were chosen based on a previous review in which the authors identified the most used models in healthcare studies 25. We will use the Python programming language to perform the analyzes.
To test the predictive performance of the algorithms in new unseen data, individuals will be divided into training ($70\%$ of patients, which will be used to define the parameters and hyperparameters of each algorithm) and testing ($30\%$, which will be used to test the predictive ability of models in new data).
We will also perform all the preliminary steps to ensure a good performance of the algorithms, especially those related to the pre-processing of predictor variables, such as the standardization of continuous variables, separation of categorical predictors with one-hot encoding, exclusion of strongly correlated variables, dimension reduction using principal component analysis and selection of hyperparameters with 10-fold cross-validation. Different metrics will evaluate the predictive capacity of the models, the main one being the area under the receiver operating characteristic (ROC) curve (AUC). In a simplified way, the AUC is a value that varies from 0 to 1, and the closer to 1 the better the model’s predictive capacity 26. The other metrics will be F1-score, sensitivity, specificity, and accuracy. As measures of model fit, we will perform hyperparameters and balancing fit, as well as K-fold (cross-validation).
## COVID-19
The current pandemic, caused by the SARS-CoV-2 virus, has brought uncertainty to the world population. Although vaccination coverage is already high in large parts of the population, the arrival of new variants and the lack of other essential measures to face the pandemic still create uncertainty about the effects of the pandemic on people. General questions about symptoms, tests, and possible effects caused by coronavirus contamination were included in our baseline survey. We will also use SARS-CoV-2-related questions to evaluate the performance of ML algorithms. In September 2021, restrictive measures were relaxed due to a decrease in COVID-19 cases in Pelotas, allowing the study to begin. A vaccination passport was required from the interviewers to ensure the safety of both participants and interviewers. In addition, all interviewers received protective equipment against COVID-19, including masks, face shields, and alcohol gel. Finally, the interviewers were instructed to conduct the research in an open and airy area, ensuring the protection of the participants.
## Quality assurance and control
The activities to allow for control and data quality were characterized by a series of measures aimed at ensuring results without the risk of bias. Initially, we developed a research protocol, followed by an instruction manual for each interviewer. Thereafter, interviewers were trained and standardized in all necessary aspects.
REDCap was also important to garanteee the control and quality of responses as the questions were designed using validation checks according to what was expected for each answer. Another measure that ensured the control of interviews was the collection of latitude and longitude of households, which was plotted by two members of the study coordination weekly on maps, to ensure that the data collection was performed according to the study sample. With latitude and longitude data, it is also intended to carry out spatial analysis articles with techniques such as sweep statistics and Kernel.
The database of the questions was checked daily to find possible inconsistencies. Finally, two members of the study coordination made random phone calls to $10\%$ of the sample, in which a reduced questionnaire was applied, with the objective of comparing the answers with the main questionnaire.
## Ethical principles
We carried out this study using free and informed consent, as determined by the ethical aspects of Resolution No. $\frac{466}{2012}$ of the National Council of the Ministry of Health and the Code of Ethics for Nursing Professionals, of the duties in Chapter IV, Article 35, 36 and 37, and the prohibitions in chapter V, article 53 and 54. After identifying and selecting the study participants, they were informed about the research objectives and signed the Informed Consent Form (ICF). The project was referred to the Research Ethics *Committee via* the Brazilian platform and approved under the CAAE 39096720.0.0000.5317.
## Schedule
Initially, we conducted a stage for the preparation of an electronic questionnaire at the beginning of 2021. In February 2021, we initiated data collection after preparing the online questionnaire. The database verification and cleaning steps occurred simultaneously with the collection, and continued until March 2022. After this step, data analysis and writing of scientific articles began.
## First descriptive results and comparison with a population-based study
Of approximately 15,526 households approached, 8,196 were excluded — 4,761 residents were absent at the visit, 1,735 were ineligible, and 1,700 were empty (see Figure 2). We identified 7,330 eligible participants, of which 1,607 refused to participate in the study, totalizing 5,722 residents. Comparing the female gender percentage of the refusals with the completed interviews, we observed a slightly lower prevalence with $63.2\%$ ($95\%$CI 60.7–65.5) among the refusals, and $66.8\%$ ($95\%$CI 65.6–68.0) among the complete interviews. The mean age was similar between participants who agreed to participate (50.3; $95\%$CI 49.9–50.8) and those who refused (50.4; $95\%$CI 49.0–51.9).
**Figura 2.:** *Flowchart describing the sampling process.*
To evaluate the first descriptive results of our sample, we compared our results with the 2019 Brazilian National Health Survey (PNS) database. The PNS 2019 was collected by the IBGE in partnership with the Ministry of Health. The data are in the public domain and are available in the IBGE website (https://www.ibge.gov.br/). To ensure the greatest possible comparability between studies, we used only residents of the urban area of the state of Rio Grande do Sul, aged using the command svy from Stata, resulting in 3,002 individuals (residents selected to interview).
We developed two models to compare our data with the PNS 2019 survey: Crude model (crude results from the EAI PELOTAS study, without considering survey design estimates); Model 1 using survey design: primary sampling units (PSUs) using census tracts as variables and post-weight variables based on estimates of Pelotas population projection for 2020 (Table 1). We evaluated another model using individual sampling weight (i.e., the inverse of the probability of being interviewed in each census tract). These models are virtually equal to the above estimates (data not shown).
The mean age of our sample was 50.3 years (Table 1), 46.2 for model 1, which was similar to PNS 2019 (46.7 years). Our weighted estimates presented a similar proportion of females compared to the PNS 2019 sample. The proportions of skin colors were similar in all categories and models. Our crude model presented a higher proportion of participants with incomplete elementary school or less compared to model 1 and PNS 2019.
Table 2 describes the prevalence of chronic diseases and lifestyle factors in our study and the PNS 2019 sample. Our prevalence of diabetes was higher in the crude model compared to weighted estimates and PNS 2019 sample. In both models, we had a higher proportion of individuals with obesity and hypertension than in PNS 2019. Asthma and/or bronchitis presented similar proportions in our results compared to PNS 2019; the same occurred for cancer. Our study presented a higher proportion of smoking participants in both models than in the PNS 2019 sample.
**Table 2.**
| Chronic diseases and lifestyle factors | EAI PELOTAS* | EAI PELOTAS*.1 | PNS 2019† |
| --- | --- | --- | --- |
| Chronic diseases and lifestyle factors | Crude | Survey design 1 | PNS 2019† |
| Chronic diseases and lifestyle factors | % (95%CI) | % (95%CI) | % (95%CI) |
| Diabetes | 14.2 (13.3–15.1) | 11.5 (10.6–12.4) | 9.0 (8.9–11.1) |
| Obesity | 30.4 (29.2–31.7) | 29.2 (27.7–30.8) | 24.8 (22.6–27.1) |
| Hypertension | 39.0 (37.7–40.3) | 32.4 (31.0–33.9) | 28.1 (25.9–30.5) |
| Asthma or chronic bronchitis | 9.3 (8.6–10.1) | 9.3 (8.4–10.4) | 8.7 (7.3–10.3) |
| Cancer | 4.2 (3.7–4.7) | 3.4 (2.9–4.0) | 3.8 (2.9–4.9) |
| Current smoking | 20.6 (19.6–21.7) | 20.4 (18.9–22.0) | 16.3 (14.6–18.1) |
## DISCUSSION
We described the initial descriptive results, methodology, protocol, and the steps required to perform the ML analysis for predicting the use of urgent and emergency services among the residents of Pelotas, Southern Brazil. We expect to provide subsidies to health professionals and managers for decision-making, helping to identify interventions targeted at patients more likely to use urgent and emergency services, as well as those more likely to develop multimorbidity and mortality. We also expect to help health systems optimize their space and resources by directing human and physical capital to those at greater risk of developing multiple chronic diseases and dying. Recent studies in developed countries have found this a feasible challenge with ML 21,27. If our study presents satisfactory results, we intend to test its practical applicability and acceptance to assist health professionals and managers in decision-making in emergency services among residents of Pelotas.
The baseline and methods used to select households resemble the main population-based studies conducted in Brazil, such as the Brazilian Longitudinal Study of Aging (ELSI-Brazil) 28, the EPICOVID 29, and the PNS. The applicability of ML requires suitable predictive variables. Our study included sociodemographic and behavioral variables related to urgent and emergency services, and chronic diseases. EAI PELOTAS study also includes essential topics that deserve particular importance during the COVID-19 pandemic, such as food insecurity, decreased income, physical activity, access to health services, and social support.
We also presented one weighting option in order to obtain sample estimates considering the complex study design. All estimates have their strength and limitation. Each research question answered through this study may consider these possibilities and choose the most suitable one. The estimates were similar without weighting and those considering the primary sampling unit (PSU) and sampling weight. Using the census tract in the PSU is fundamental to consider the sampling design in the estimates of variability (standard error, variance, $95\%$CI, among others). In addition, due to the possible selection bias in the sample, which contains more women and older people than expected, the use of a post-weighting strategy becomes necessary to obtain estimates adjusted for the sex and age distributions of the target population (due to the lack of census data, we used population projections). However, it should be noted that this strategy can produce estimates simulating the expected distribution only by sex and age. Still, we do not know how much this strategy can distort the estimates since the demographic adjustment cannot reproduce adjustment in all sample characteristics, especially for non-measured variables that may have influenced the selection of participants. Thus, we recommend defining the use of each strategy on a case-by-case basis, depending on the objective of the scientific product. Finally, we suggest reporting the different estimates according to the sample design for specific outcomes (e.g., the prevalence of a specific condition) that aim to extrapolate the data to the target population (adults of the city of Pelotas).
In conclusion, the present article presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents in Pelotas (RS), Southern Brazil.
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---
title: Alterations in Fecal Microbiota Linked to Environment and Sex in Red Deer (Cervus
elaphus)
authors:
- Yue Sun
- Yanze Yu
- Jinhao Guo
- Linqiang Zhong
- Minghai Zhang
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000040
doi: 10.3390/ani13050929
license: CC BY 4.0
---
# Alterations in Fecal Microbiota Linked to Environment and Sex in Red Deer (Cervus elaphus)
## Abstract
### Simple Summary
The gut microbiota forms a complex microecosystem in vertebrates and is affected by various factors. Wild and captive red deer currently live in the same region but have vastly different diets. In this study, the 16S rRNA sequencing technology was performed to evaluate variations in the fecal microbiota of wild and captive individuals of both sexes of red deer. It was found that the composition and function of fecal microbiota in wild and captive environments were significantly different. As a key intrinsic factor, sex has a persistent impact on the formation and development of gut microbiota. Overall, this study reveals differences in the in the fecal microbiota of red deer based on environment and sex. These data could guide future applications of population management in red deer conservation.
### Abstract
Gut microbiota play an important role in impacting the host’s metabolism, immunity, speciation, and many other functions. How sex and environment affect the structure and function of fecal microbiota in red deer (Cervus elaphus) is still unclear, particularly with regard to the intake of different diets. In this study, non-invasive molecular sexing techniques were used to determine the sex of fecal samples from both wild and captive red deer during the overwintering period. Fecal microbiota composition and diversity analyses were performed using amplicons from the V4–V5 region of the 16S rRNA gene sequenced on the Illumina HiSeq platform. Based on Picrust2 prediction software, potential function distribution information was evaluated by comparing the Kyoto Encyclopedia of Genes and Genome (KEGG). The results showed that the fecal microbiota of the wild deer (WF, $$n = 10$$; WM, $$n = 12$$) was significantly enriched in Firmicutes and decreased in Bacteroidetes, while the captive deer (CF, $$n = 8$$; CM, $$n = 3$$) had a significantly higher number of Bacteroidetes. The dominant species of fecal microbiota in the wild and captive red deer were similar at the genus level. The alpha diversity index shows significant difference in fecal microbiota diversity between the males and females in wild deer ($p \leq 0.05$). Beta diversity shows significant inter-group differences between wild and captive deer ($p \leq 0.05$) but no significant differences between female and male in wild or captive deer. The metabolism was the most important pathway at the first level of KEGG pathway analysis. In the secondary pathway of metabolism, glycan biosynthesis and metabolism, energy metabolism, and the metabolism of other amino acids were significantly different. In summary, these compositional and functional variations in the fecal microbiota of red deer may be helpful for guiding conservation management and policy decision-making, providing important information for future applications of population management and conservation.
## 1. Introduction
Red deer (Cervus elaphus), which belong to the family Cervidae, order Artiodactyla, distributed in Asia, Europe, North America, and North Africa [1]. The red deer is a typical forest-inhabiting mammal in northeast China and has an important ecological status in the forest ecosystem [2]. Owing to habitat fragmentation, the populations of red deer in the wild are currently in sharp decline [2]. Using captive populations as reintroduction resources is an effective strategy to restore the populations of wild red deer [3].
The complex gut microbiota systems in the mammalian gut are composed of large fractions of microbes [4]. The gut microbiota are a complex product of the long-term evolution of hosts and microbes [4]. Recent studies have shown that not only are gut microbiota a part of the host, but they also have a significant impact on the health of the host, such as promoting immunity, digestion, metabolism, and intestinal endocrine hormones, among others [5,6,7]. Simultaneously, the complex and flexible gut microbiota can be affected by multiple environmental and host genotypes [8]. Many studies have shown that diet is an important factor that affects the structure and function of the fecal microbiota [9,10,11]. For example, changes in diet alter the function and diversity of fecal microbiota as well as the relative abundance of some microorganisms [12]. Moreover, diet-induced loss of microbial function and diversity will increase the risk of diversity loss and extinction through generational amplification [13]. It was necessary to investigate the gut microbiome by comparing differences between wild and captive red deer. However, to date, there has been a lack of studies comparing the gut microbiota between wild and captive red deer [11]. Because of sex differences in behavior and physiology, sex as an important intrinsic factor leads to differences in gut microbiota among individuals within species [14,15,16]. Although the results are inconsistent, animal species with significant sexual dimorphism and human studies have shown sex-related differences in gut microbiota. In mice (Mus musculus), poultry, and forest musk deer (Moschus berezovskii), the composition of the gut or fecal microbiota shows sex differences [17,18,19]. At present, few studies have analyzed the sexual dimorphism of fecal microbiota in red deer.
In order to save endangered populations, artificial breeding of wild populations is carried out. The food types and nutrient intake ratios obtained in captivity and wild environments are very different, especially for endangered cervidae [20]. Therefore, monitoring the digestive system of captive animals and identifying standardized levels of nutritional requirements and fiber composition is critical for captive wild animals to determine whether they have acclimated to artificially provided food and new environments—a part of wildlife conservation’s main problem [21]. Using captive populations as reintroduction resources is an effective strategy to restore the populations of wild red deer. The composition of gut microbiota in wild populations can be a good indicator of the breeding direction of the captive population [9]. Therefore, understanding the impact of dietary differences between wild and captive red deer on the fecal microbiota can help to assess and ensure the long-term viability of this species [9]. At present, the research methods for fecal microbiota have also shifted from traditional methods to 16S rRNA gene sequencing technology, from simple microbial composition, community structure, and core microbiota research to microbial function research, which has become a hot frontier in ungulate research today [22].
The main goal of this study was to characterize the composition of the fecal microbiota of red deer of different sex and feeding plus environment. We used high-throughput 16S rRNA sequencing technology to comprehensively analyze. Thus, we hypothesized that: [1] the fecal microbiota composition and function are different between wild and captive deer; and [2] under the wild or captive environment, the microbiota diversity and evenness are different between females and males.
## 2.1. Study Site, Subjects, and Sample Collection
This study was conducted at the Gaogestai National Nature Reserve in Chifeng, Inner Mongolia (119°02′30″, 119°39′08″ E; 44°41′03″, 45°08′44″ N). The total area is 106,284 hm2. It is a typical transition zone forest-steppe ecosystem in the southern foothills of Greater Khingan Mountains, including forests, shrubs, grasslands, wetlands, and other diverse ecosystems. In February 2019, 75 line transects were randomly laid in the Gogestai protection area. Positive and reverse footprint chain tracking was carried out after the foodprints of red deer were found through line transect investigation. Disposable PE gloves were worn to collect red deer feces. While tracking the footprint chain, set 2 m × 2 m plant quadrate every 200 m to 250 m along the footprint chain, and collect all kinds of plant branches eaten by deer in the quadrate as far as possible [23]. A total of 162 fecal samples were collected and stored at −20 °C within 2 h. The feces of red deer from different areas of the Reserve were identified as coming from different individuals, and 43 feces were identified individually in the laboratory.
In February 2019, the HanShan Forest Farm in Chifeng City, Inner Mongolia, China (adjacent to the Gaogestai Nature Reserve) had a total of 11 healthy adult red deer of similar age and size. Ear tags were used to differentiate each individual red deer. Through continuous observation, feces were collected immediately after excretion by different red deer individuals and stored at −20 °C. We measured crude protein, energy, neutral detergent fiber (NDF), and total non-structural carbohydrates in red deer diets.
## 2.2. Individual Recognition and Sex Identification
We used a qiaamp DNA Fecal Mini-kit (QIAGEN, Hilden, Germany) to extract host deoxyribonucleic acid (DNA) from the fecal samples of red deer as previously described [24]. Microsatellite PCR technology was used with nine pairs of microsatellite primers (BM848, BMC1009, BM757, T108, T507, T530, DarAE129, BM1706, and ILST0S058) [25,26] with good polymorphism that were selected based on the research results of previous studies. These nine pairs of primers can amplify fecal DNA stably and efficiently. A fluorescence marker (TAMRA, HEX, or FAM) was added to the 5′ end of upstream primers at each site (Supplementary Table S1). Primer information, PCR amplification, and genotype identification procedures are described in the literature [27]. Multi-tube PCR amplification was used for genotyping [28], and 3–4 positive amplifications were performed for each locus to determine the final genotype [29]. The excel microsatellite toolkit [30] was used to search for matching genotypes from the data. Samples are judged to be from the same individual if all loci have the same genotype or if only one allele differs at a locus. The microsatellite data were analyzed by Cervus 3.0 software, and the genotyping was completed [31].
Male and female individuals were identified by detecting the existence of genes after the individual identification of red deer was completed. *Sry* gene primers (F:5′-3′ TGAACGCTTTCATTGTGTGGTC; R:5′-3′ GCCAGTAGTCTCTGTGCCTCCT) were designed, and the amplification system was determined. To minimize the occurrence of false positives or false negatives that could affect results, the *Sry* gene was repeated three times to expand and increase during the experiment, and samples with target bands that appeared on the second and third occasions were determined to be male [32].
## 2.3. Fecal Microbiota DNA Extraction, Amplification, and Sequencing
The total microbial DNA of fecal samples was extracted using an E.Z.N.A® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). The DNA integrity of the extracted samples was determined by $1\%$ agarose gel electrophoresis. Targeting a 420 bp fragment encompassing the V4-V5 region of the bacterial 16S ribosomal RNA gene was amplified by PCR using primers 515F (5′-GTG CCA GCM GCC GCG GTA A-3′) and 907R (5′-CCG TCA ATT CMT TTR AGT TT-3′). NEB 154 Q5 DNA high-fidelity polymerase (NEB, Ipswich, MA, USA) was used in PCR amplifications (Supplementary Table S1). A 1:1 mixture containing the same volume of 1XTAE buffer and the PCR products were loaded on a $2\%$ agarose gel for electrophoretic detection. PCR products were mixed in equidensity ratios. Then, the mixture of PCR products was purified using the Quant-iTPicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). Sequencing libraries were generated using the TruSeq Nano DNA LT Library Prep kit (Illumina, San Diego, CA, USA) following the manufacturer’s recommendations, and index codes were added. The library’s quality was assessed on the Agilent 5400 (Agilent Technologies Co. Ltd., Santa Clara, CA, USA). At last, the library was sequenced on an Illumina NovaSeq 6000 platform, and 250 bp paired-end reads were generated.
Microbiome bioinformatics were performed with QIIME2 2019.4 [33] with slight modification according to the official tutorials (https://docs.qiime2.org/2019.4/tutorials/ (accessed on 30 September 2022)). Briefly, raw data FASTQ files were imported into the format that could be operated by the QIIME2 system using the qiime tools import program. The DADA2 [34] process is to obtain amplified variant sequences through de-duplication. In the process, clustering is not carried out based on similarity, but only de-duplication is carried out. Demultiplexed sequences from each sample were quality filtered and trimmed, de-noised, merged, and then the chimeric sequences were identified and removed using the QIIME2 DADA2 plugin to obtain the feature table of amplicon sequence variants (ASV) [34]. The QIIME2 feature-classifier plugin was then used to align ASV sequences to a pre-trained GREENGENES 13_8 $99\%$ database (trimmed to the V4V5 around a 420bp region bound by the 515F/907R primer pair) to generate the taxonomy table [35]. In order to unify the sequence effort, samples were rarefied at a depth of 25,318 sequences per sample before alpha and beta diversity analysis. Rarefaction allows one to randomly select a similar number of sequences from each sample to reach a unified depth.
## 2.4. Bioinformatics and Statistical Analyses
Sequence data analyses were mainly performed using QIIME2 and R software (v3.2.0). ASV-level alpha diversity indices, such as the Chao1 richness estimator and Pielou’s evenness, were calculated using the ASV table in QIIME2 [36,37], and visualized as box plots (R software, package “ggplot2”). Beta diversity analysis was performed to investigate the structural variation of microbial communities across samples using weighted or unweighted UniFrac distance metrics [38,39] and visualized via principal coordinate analysis (PCoA) (R software, package “ape”). The significance of differentiation of microbiota structure among groups was assessed by PERMANOVA (permutational multivariate analysis of variance) [40]. Random forest analysis (R software, package “randomForest”) was applied to sort the importance of microbiota with differences in abundance between groups and screen the most critical phyla and genera that lead to microbial structural differences between groups using QIIME2 with default settings [41,42]. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (Picrust2) [43] is software that predicts the functional abundance from the sequencing data of marker genes (typically 16S rRNA). An ASV’s abundance table is used for standardization, and the corresponding relationship of each ASV is compared with the Kyoto Encyclopedia of Genes and Genomes (KEGG) library to obtain the functional information and functional abundance spectrum.
## 3.1. Identification of Individuals and Sex
A total of 22 red deer individuals were identified from 43 fecal samples, including 12 males and 10 females (Supplementary Table S2). The female captive deer were CF1, CF2, CF3, CF4, CF5, CF6, CF7, and CF8. The male captive deer were CM1, CM2, and CM3. We divided all the red deer (22 wild and 11 captive) into four groups: wild females (WF) ($$n = 10$$), wild males (WM) ($$n = 12$$), captive females (CF) ($$n = 8$$), and captive males (CM) ($$n = 3$$). The information about identification, location, sex, and diet is summarized in Supplementary Table S2.
## 3.2. Diet Composition and Nutritional Composition of Wild and Captive Red Deer Winter Diets
The wild red deer were fed on 16 species of plants in the winter. The edible plants belonged to 16 species of 16 genera and 9 families. Since the frequency of occurrence of other edible plants in red deer, such as Mongolian oak (Quercus mongolica) and Chinese maple (Acer sinensis), was less than $7\%$, the nutrient content of these plants was not measured. In addition, we hypothesized that they had little influence on the nutritional strategy of red deer. Therefore, the primary nutrient contents of 14 types of edible plants were determined. The food and nutritional composition of wild red deer are shown in Supplementary Table S3. When the captive red deer were fed, each type of food was fed separately at different times. The nutritional content of the primary food of captive red deer from the farm (adjacent to the Gaogestai Nature Reserve) in winter is shown in Supplementary Table S4. Only one kind of diet were provided to captive deer at each feeding time with all captive deer feeding together. Captive red deer feed on leaves and high protein given by artificial feeding. Compared with captive red deer, wild deer have a wider feeding range and no dietary limitations. Substantial differences exist between these two feeding methods.
## 3.3. Sequencing Analysis and Clustering
A total of 1,561,654 high-quality sequences were obtained from the fresh winter feces of 22 wild deer and 11 captive deer. Rarefaction curves based on the Chao1 diversity index reached asymptotes at 22,500. The results showed that with the increase in amount of sequencing, the curve tended to be flat and no longer changed, indicating that the amount of sequencing in this study basically reflected the diversity of red deer fecal microbiota in this study (Supplementary Figure S1). A total of 15,228 ASVs were obtained using a $100\%$ similarity clustering method. The WF, WM, CF, and CM groups included 3056 ASVs, 3924 ASVs, 6661 ASVs, and 1587 ASVs, respectively.
## 3.4. Microbial Composition and Diversity by Environment and Sex
We found significant differences in fecal microbial composition between wild and captive red deer based on environment. The fecal microbial communities of four groups (WF, WM, CF, and CM) were dominated by the phyla Firmicutes and Bacteroidetes (Figure 1A). The phylum Firmicutes was the most abundant in WF (81.12 ± $2.87\%$), followed by WM (79.03 ± $2.19\%$), CF (58.24 ± $3.17\%$), and CM (59.66 ± $0.47\%$). Secondly, Bacteroidetes was abundant in WF (15.19 ± 2.09), WM (16.89 ± $2.08\%$), CF (33.02 ± 5.48), and CM (31.55 ± $1.61\%$). At the genus level, the genera from the four groups with abundance > $1\%$ were Oscillospira, a candidate genus 5-7N15 from the family Bacteroidaceae, Ruminococcus, Roseburia, Clostridium, and Prevotella (Figure 1B and Table 1).
The chao1 diversity indices demonstrate a significant difference between the WF and WM groups ($p \leq 0.01$). There was no statistically significant difference between the CF and CM groups ($p \leq 0.05$). Pieluo’s diversity index showed that no significant differences occurred between WF and WM groups ($p \leq 0.05$) or CF and CM groups ($p \leq 0.05$) (Figure 2).
Wild and captive red deer also differed in beta-diversity. An PCoA plot based on the Unweighted Unifrac and Weighted Unifrac distance matrix revealed clear separation of the fecal microbiota between wild and captive red deer (Figure 3A). The results of a PCoA analysis showed that the fecal microbial structures of the CF and CM groups were more similar than those of the WF and WM communities ($F = 13.82$, $$p \leq 0.001$$; and unweighted: $F = 5.983939$, $$p \leq 0.001$$; Figure 3A; Supplementary Table S5).
A random forest analysis showed that Firmicutes and Bacteroidetes were the primary microorganisms that had differences between the wild and captive populations by (an importance > 0.1) (Figure 3C, D). This analysis indicated that there were significant differences in the abundances of Firmicute and Bacteroidetes between the four groups (an importance > 0.1), which were the primary phyla that caused differences in the microbial communities between groups (Figure 3C). Ruminococcus, Treponema, Akkermansia, a candidate genus 5-7N15 belonging to family Bacteroidaceae, and a candidate genus rc4-4 belonging to family Peptococcaceae were the main genera that caused differences in microbial communities between sex and environment (importance > 0.04; Figure 3D).
## 3.5. Functional Modules of Fecal Microbial Communities
Metabolism was found to be the most common function prediction performed on fecal microbial communities and included the most important pathways for microbial clustering ($76.67\%$). The second pathway of metabolism included amino acid metabolism ($17.26\%$), carbohydrate metabolism ($17.85\%$), metabolism of cofactors and vitamins ($16.57\%$), and metabolism of terpenoids and polyketides ($12.66\%$) (Figure 4A). A PCoA analysis showed that the WF and WM groups had more similar microbial function clusters (Figure 4B).
It was found that there were significant differences in the three metabolic pathways of glycan biosynthesis and metabolism (GBM), energy metabolism (EM), and metabolism of other amino acids (MAA) ($p \leq 0.05$) (Figure 5).
## 4. Discussion
This is the first study to apply high-throughput sequencing to describe the fecal bacterial microbiota of wild and captive red deer by sex. Analysis of the differences in fecal microbiota is a key step in releasing captive red deer to help expand the wild population. *In* general, the fecal bacterial microbiota of red deer was similar to that of other cervidae, such as elk (Cervus canadensis), white tailed deer (Odocoileus virginianus) [38], and white-lipped deer (Cervus albirostris) [39], at least at the bacterial phylum level, with high proportions of the phyla Firmicutes and Bacteroidetes. In the digestive tract of herbivores, the role of *Firmicutes is* mainly to decompose cellulose and convert it into volatile fatty acids, thereby promoting food digestion and host growth and development. The enrichment of Firmicutes plays an important role in promoting the ability of red deer to obtain abundant nutrients from food and, at the same time, affects the metabolic function of the fecal microbiota. Bacteroidetes can improve the metabolism of organisms, promote the development of the gastrointestinal immune system, participate in the body’s bile acid, protein, and fat metabolisms, and also have a certain regulatory effect on carbohydrate metabolism. It can also produce special glycans and polysaccharides, which have a strong inhibitory effect on inflammation [43]. Differences in microbiota may be explained by changes in diet. Data from previous local and overseas studies have shown that diet is the main factor affecting the gut microbiota in mammals [40]. It is likely that wild deer have a more varied diet, more than captive deer. These phyla, Firmicutes and Bacteroidetes, are involved in important processes such as food digestion, nutrient regulation and absorption, energy metabolism, and host intestinal defense against foreign pathogens [40,41,42].
Alpha diversity alterations may be attributed to differential diet or hormonal influences on the gut microbiota. Fecal microbiota richness in wild populations is higher than that in captive animals, such as the Tibetan wild ass (Equus kiang), bharal (Pseudois nayaur), Tibetan sheep (Ovis arise), and yak (Bos mutus) [44,45,46,47,48]. Nevertheless, other studies also found that captivity might increase the alpha diversity of fecal microbiota in most Cervidae compared with other animals, for example, sika deer (Genus Cervus), Père David’s (Elaphurus davidianus), and white-tailed deer (Odocoileus virginianus) [49,50]. It may be that some environmental stresses in the wild or the special structure of the stomach and intestines in these deer lead to decreased alpha diversity of fecal microbiota in wild deer [50]. This phenomenon needs further research to determine its cause. Our results showed that the richness of the fecal microbial community in wild red deer differed by sex (Figure 2). In wild deer, the microbiota diversity was higher for females than males. Microbial community alterations by sex could be attributed to hormonal [51]. The sampling time was during the gestation period of red deer. Levels of female growth hormone during pregnancy may affect the fecal microbiota. Reproductive hormones have also been associated with sex and gut microbial changes in wild animals [17,52,53]. Increased evidence indicates that sex steroid hormone levels are associated with the human gut microbiota [54,55]. Futher, Edwards et al. reported that estrogen and progesterone had an impact on gut function [56]. The captive deer also had the smallest sample size ($$n = 3$$ males and 8 females), which limited our ability to detect these differences.
In this study, the functional pathway composition of wild red deer is more similar (Figure 5B), which is completely opposite to the microbial structure (Figure 3A). The change in microbial structure does not necessarily lead to the change in function, which may be due to the same function in different microbial communities [57]. In recent years, studies have shown that gut microbiota are involved in various metabolic processes such as amino acids, carbohydrates, and energy, confirming their primary role in assisting host digestion and absorption [58]. It has also been found to be involved in environmental information processing, suggesting that the gut microbiota plays an important role in facilitating acclimation to changing environments [59]. The metabolism of gut microbiota is closely related to the feeding habits of the host. In the long-term evolution process, the gut microbiota will respond to changes in diet types or specific diets by adjusting the content of certain digestive enzymes [4,60]. Studies have shown that the decrease of fecal microbial diversity can lead to a reduction in the functional microbiota, in the efficiency of the microbiota, and in the resistance to pathogen invasion [61]. The decrease in fecal microbial diversity in captive populations resulted in a decrease in functional microbiota [61]. Ruminococcaceae and Lachnospiraceae are two of the most common bacterial families within the *Firmicutes phylum* [62]. It has been hypothesized that they have an important role as active plant degraders [63,64]. According to our results, the level of Ruminococcaceae in the captive groups is significantly lower than that in the wild group, which could suggest that the fiber-reduced diet in captivity is modifying the ability of the fecal microbiota to degrade recalcitrant substrates such as cellulose, hemicellulose, and lignocellulose, among others, that are commonly found on the main resources of the wild red deer diet. The captive deer’s consequent reduction of diet resources might trigger the decline of important metabolic pathways associated with nutrient use [64].
16S rRNA analysis constitutes a valuable and cost-efficient approach for surveillance and monitoring wild populations as well as captive individuals. Picrust2 prediction accuracy is dependent on the availability of closely related annotated bacterial genomes in the database and the phylogenetic distance from the reference genome. However, the prediction results are still uncertain, which does not mean that the correlation between the predicted genes and the real metagenome of the microbiota is $100\%$ [65]. At present, due to the difficulty of cultivation, the mechanism by which some functional bacteria exert their effects remain unclear. Therefore, in the follow-up work, it is necessary to repeatedly cultivate the conditions of some intestinal anaerobic bacteria, the most extensive of which are Firmicutes and some Bacteroidetes. The microbiota was cultured in vitro by simulating the gut environment, and its functions were speculated and further verified in combination with multiple groups of studies (metagenomics, meta transcriptome, and proteome, etc.). At the same time, the unknown functional microbiota and its genome sequence information can be explored and studied. These works will help to understand the metabolic activities of the complex microbiota and further explore the host physiological processes involved in gut microbiota.
## 5. Conclusions
In conclusion, our study provided information on the structure and function of the fecal microbiome of red deer through the 16S rRNA gene of fecal samples. Comparing analyses identified significant variations of fecal microbiota composition and functions between captive and wild populations and also indicated that environment and sex have a great influence on these variations. These findings were of great significance for the reintroduction of captive red deer, given that the differences in fecal microbiota composition and functions between captive and wild red deer would greatly impact the ability of captive red deer to adapt to the wild environment. For further study, incorporating novel methods (e.g., transcriptome) to study the functional annotation of gene content and the functional traits of the host would be essential for better understanding the physiology and immunology of red deer.
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|
"---\ntitle: Rumen-Protected Lysine and Methionine Supplementation Reduced Protein Requirement\n of(...TRUNCATED) | [-0.0005616334383375943,0.09884747862815857,-0.004461865406483412,-0.008237290196120739,-0.002589754(...TRUNCATED) |
"---\ntitle: Epithelial-to-Mesenchymal Transition and Phenotypic Marker Evaluation in Human,\n Cani(...TRUNCATED) | [-0.0005898652016185224,0.050666313618421555,-0.0011331620626151562,-0.0024525972548872232,-0.002607(...TRUNCATED) |
"---\ntitle: Effects of Dietary Alpha-Lipoic Acid on Growth Performance, Serum Biochemical\n Indexe(...TRUNCATED) | [-0.0006440303986892104,0.04401669651269913,-0.02925965189933777,0.0006122264312580228,-0.0030849850(...TRUNCATED) |
"---\ntitle: Relationships between the Content of Micro- and Macroelements in Animal Samples\n and (...TRUNCATED) | [-0.0006015186081640422,0.06954330950975418,-0.041477616876363754,-0.03042519837617874,-0.0023750283(...TRUNCATED) |
"---\ntitle: Effect of Maternal Gradient Nutritional Restriction during Pregnancy on Mammary\n Glan(...TRUNCATED) | [-0.0006227957783266902,0.06267127394676208,0.007301202975213528,0.016419872641563416,-0.00282020727(...TRUNCATED) |
"---\ntitle: Effects of Different Phospholipid Sources on Growth and Gill Health in Atlantic\n Salm(...TRUNCATED) | [-0.0007010953268036246,0.056474439799785614,-0.02114568091928959,0.027463596314191818,-0.0032556611(...TRUNCATED) |
"---\ntitle: Effects of Bacillus licheniformis and Combination of Probiotics and Enzymes\n as Suppl(...TRUNCATED) | [-0.0006216653273440897,0.059304527938365936,-0.018391910940408707,-0.007819689810276031,-0.00284636(...TRUNCATED) |
"---\ntitle: Growth Performance, Antioxidant and Immunity Capacity Were Significantly Affected\n by(...TRUNCATED) | [-0.0005725014489144087,0.09259050339460373,-0.014173279516398907,0.0089743472635746,-0.002382843988(...TRUNCATED) |
End of preview. Expand
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