This article presents batch experimental data describing the main batch adsorption operation parameters. Also the adsorption models  of basic yellow dye on to the raw and treated kaolin adsorbents. Besides, instrumental analyses were recorded to characterize kaolin adsorbent. Such as, thermogravimetric analyzer, Fourier transformation infrared and scanning electroscope with energy dispersion spectroscopy were used. UV–Visible spectrometer was used throughout the experimental study for the determination of absorbance. The effect of adsorption temperature , PH , initial dye concentration , contact time  and adsorbent dosage  have been well determined. For adsorbent characteristics, we provide dataset regarding (i) thermogravimetery with differential scanning calorimetery, (ii) Fourier transform infrared spectral data before and after basic yellow dye adsorption process, (iii) scanning electroscope with energy dispersion spectroscopy image at ×500 resolution, (iv) X-ray diffraction and, (v) batch adsorption experimental parameters records. Regarding scanning electroscope with energy dispersion spectroscopy image, we provide data of three surface morphology image and three elemental distribution spectra for raw and treated kaolin adsorbent. Six kinds of data for basic yellow dye removal are reported: batch experiment datasets (sheet 1 from excel file), Optimization datasets (sheet 2 from excel file), Isotherm, kinetics and Thermodynamics dataset (sheet 3 from excel file), thermogravimetric with Differential Scanning Calorimetry (sheet 4 from excel file), Fourier transform infrared, before and after adsorption for basic yellow dye, dataset (sheet 5 from excel file), X-ray Diffraction (sheet 6 from excel file) and Scanning electron microscopy with energy dispersive spectroscopy dataset for beneficiated, calcined and raw kaolin adsorbents (images from three separate word file).22.1Natural kaolin was collected from local area, Debre Tabor Town, Amhara Region, Ethiopia. Synthetic solution was prepared from Basic yellow dye. All laboratory grade reagents, sodium hydroxide (NaOH), Hydrochloric acid (HCl), potassium bromide (KBr), distilled water and liquid nitrogen was used without any purification.2.2Collected kaolin was crushed, milled and screened using jaw crusher, disc mill and Standard sieves, respectively. The size of kaolin powder was sieved to 75 μm. The powder of kaolin was calcined at 700°C using Muffle Furnace. Additionally, the powder of kaolin was beneficiated using distilled water in the conical flask of 1L capacity. Finally, three types of kaolin adsorbents were prepared from natural kaolin i.e. raw, beneficiated and calcined kaolin adsorbent in order to investigate their efficiency for dye adsorption from aqueous solution.2.321H27N3O5S as well as its molecular weight and maximum wave length are 433.52 g/mol and 438 nm, respectively. The stock solution (500 mg/L) of the dye was prepared by dissolving 0.5 g of basic yellow dye in 1 L of distilled water and required concentrations were obtained by dilution of the stock solution. The maximum wave length (438 nm) is obtained after scanning of the sample of dye using UV/VIS spectrometer (Lamda 35 Ferkin Elmer).Basic yellow dye was taken from Bahir Dar Textile Share Company, Amhara Region, Ethiopia. The physical state of basic yellow dye is powdered solid. Basic yellow dye has the chemical formula; C2.4o is the initial dye concentration (mg/L), Ce is a solute (dye) concentration in the liquid phase at the equilibrium (mg/L), Ct is the dye concentration in liquid phase at any time (mg/L), m is the amount of adsorbent (g) and V is the volume of solution (L).A measure amount of distilled water (100ml) was taken in 250 ml of conical flask for batch experiment. In conical flask calculated amount of initial dye concentration and adsorbent were added and agitated with magnetic stirrer on digital hot plate at 200 rpm. The initial pH of solution was adjusted with 1M HCl or 1M NaOH before adding the adsorbent. The batch adsorption experiments were performed for a wide range of initial dye concentration , temperature , solution pH , adsorbent dosage  and contact time . At the end of each experiment, small amount of the solutions was withdrawn at predetermined time and filtered. The absorbance value of a solution was measured after adsorption experiment for each run. The finial dye concentration was calculated from calibration curve. The removal efficiency of the dye was calculated by Equation 2.5−1 to analyze the surface function group and their percent transmittance by using a PerkinElmer Frontier Spectrometer (ILC38B6PD7) before and after adsorption. The processed data is presented in The dye absorbance values were measured by UV/VIS spectrometer (Lamda 35 Ferkin Elmer) at a maximum wave length (438 nm). The thermogravimetric properties is important because metakaolin is not a simple mixture of amorphous silica and alumina, but rather a complex amorphous structure that maintains some longer-range order due to the stacking of its hexagonal layers 2.6m is sorption capacity (mg/g), KL is sorption energy (L/g), Kf and n are the Freundlich model constants and indicating the relationship between sorption capacity and sorption intensity, respectively. If n = 1, n > 1, and n < 1, then the sorption process would be the linear, physical, or chemical in its nature, respectively.The raw data of isotherm was presented in Mendeley Data of Microsoft Excel in worksheet 3. Langmuir and Freundlich isotherm models were used to determine the relationship between dye ions adsorbed on the kaolin adsorbents surfaces and dye ions remaining in the solution. The Langmuir isotherm model assumes that sorption occurs in certain places and within the sorbent. In addition, the model assumes that sorption of dye ions occurs within homogeneous monolayers without any interactions between dye ions at the sorbent surface. The Freundlich isotherm model is an experimental model and assumes that the sorption of dye ions occurs at heterogeneous surfaces and active sites with different energies. The Langmuir and Freundlich model linear equation were presented in Eqs. The calculated Langmuir and Freundlich constant parameters from raw data of Mendeley Data of Microsoft Excel in worksheet 3 were presented in 2.7The raw data of kinetic was present in Mendeley Data of Microsoft Excel in worksheet 3. Kinetic model was used to know the information about experimental data of adsorption mechanism as a function of mixing time. The raw data of effect of mixing time on adsorption process was presented Mendeley Data of Microsoft Excel in worksheet 2. Pseudo first-order and pseudo second-order were used to analyze kinetic model. The linear equations for both models were presented in Eqs. The calculated Pseudo first-order and pseudo second-order constant parameters from raw data of Mendeley Data of Microsoft Excel in worksheet 3 were presented in 2.80 = Standard change free Gibbs energy (kJ mol−1), −1), −1K−1) and R = Universal gas constant (8.314 J mol−1K−1). Kc, the equilibrium constant, represents the ability of the adsorbent to retain the adsorbate and extent of movement of the adsorbate within the solution. The Kc is the ratio of the equilibrium concentration of the dye (qe) attached to adsorbent compared to the Van't Hoff equation as equilibrium dye concentration in solution (Ce). The calculated values of standard change Gibbs free energy, enthalpy and entropy constant parameters from raw data of Mendeley Data of Microsoft Excel in worksheet 3 were presented in The raw data of thermodynamic was presented in Mendeley Data of Microsoft Excel in worksheet 3. The thermodynamic property was used to know the exothermic and endothermic of batch adsorption for the experimental data in terms of Gibb's free energy, enthalpy and entropy a function of temperature. The raw data of effect of temperature on adsorption process was presented Mendeley Data of Microsoft Excel in worksheet 1. The values of standard change Gibbs free energy, enthalpy and entropy were obtained from according to the equations


Microvirga, Blastococcus, RB41, Nitrospira, and four norank members of bacteria in connecting the different microbiomes. Collectively, the microbial communities in the Xilingol steppe grassland presented strong habitat preferences with a certain degree of dispersal and colonization potential to new habitats along the animal excrement- phyllosphere-soil gradient. This study provides the first detailed comparison of microbial communities in different habitats in a single grassland, and offers new insights into the biogeographic patterns of the microbial assemblages in grasslands.The spatial variability of microorganisms in grasslands can provide important insights regarding the biogeographic patterns of microbial communities. However, information regarding the degree of overlap and partitions of microbial communities across different habitats in grasslands is limited. This study investigated the microbial communities in three distinct habitats from Xilingol steppe grassland, i.e. animal excrement, phyllosphere, and soil samples, by Illumina MiSeq sequencing. All microbial community structures, i.e. for bacteria, archaea, and fungi, were significantly distinguished according to habitat. A high number of unique microorganisms but few coexisting microorganisms were detected, suggesting that the structure of microbial communities was mainly regulated by species selection and niche differentiation. However, the sequences of those limited coexisting microorganisms among the three different habitats accounted for over 60% of the total sequences, indicating their ability to adapt to variable environments. In addition, the biotic interactions among microorganisms based on a co-occurrence network analysis highlighted the importance of  In comparison to aquatic systems, soils are particularly heterogeneous, and numerous environmental factors are assumed to control the spatial patterns of microorganisms4. Grasslands, as a major components of the global ecosystem, cover 37% of the terrestrial area on Earth5. With an extraordinarily high diversity of plants and herbivores, grasslands provided a well-structured platform for exploring the interaction of a range of biotic and abiotic factors that alter the structure of soil microbial communities8. Illuminating the spatial patterns of microorganisms in a grassland ecosystem not only helps to predict the critical processes that occur within grasslands (e.g. grassland degradation and restoration) but also helps to build a more comprehensive microbial biogeographic pattern at a larger scale10.A central goal in both microbial ecology and biogeography is to explore the variation in microbial abundance and diversity in different ecosystems13. The large number of microorganisms that inhabit the aboveground and belowground niches in grasslands play vital roles in nutrient cycling and contribute greatly to grassland health15. Previous research suggests that plant growth-promoting microorganisms (PGPM), which commonly reside in the phyllosphere and rhizosphere, can exhibit beneficial effects on plant growth by facilitating nitrogen, phosphorus, and iron uptake from the soil via nitrogen fixation, inorganic phosphate solubilisation, and the production of iron chelators, respectively16. In addition, 30–50% of aboveground plant biomass is consumed annually by large herbivores18, and the decomposition of animal excrement is a significant pathway for nutrient cycling through the mutual effect of faecal and soil microorganisms20. Thus, in grassland ecosystems, microorganisms inhabited in animal excrement, plants, and soil constitute a complex distribution pattern which required in-depth investigation and understanding.In grasslands, aboveground and belowground communities are intrinsically linked and the feedbacks between these subsystems have important implications for biogeochemical cycles and grassland ecosystem functioning21, rock glacier-ponds22, peatland systems23, terrestrial- freshwater gradients24, and the human body25. Although the crossing of boundaries between differential habitats enables microorganisms to occupy novel niches and explore accessible nutrient resources, it is a large challenge for microorganisms26. Both deterministic  and stochastic processes  have been used to explain microbial community assemblages30. Previous studies have shown that different mechanisms occur simultaneously and are co-responsible for the structure of microbial assemblages32. For example, different ecological groups, such as habitat generalists and specialists, exhibit different organismal traits and exhibit different responses to varying environmental conditions33. Therefore, the microbial assemblages for habitat generalists and specialists may vary according to their tolerance to different environmental conditions34. In addition, habitat heterogeneity and connectivity also affect microbial assemblages on a local scale35. As a typical representative of a multicomponent terrestrial ecosystem, grasslands show complex interactions across the animal-grass-soil gradient38. However, compared with other ecosystems, information regarding the microbial assemblages across the distinct habitats of animal excrement-phyllosphere-soil in grasslands is still limited. Whether or not these different habitats have significant differential microbial community structure? What is the degree of overlap in microbial communities among the three habitats? And what are the keystone microorganisms in connecting among microbiomes?In fact, mountainous studies have focused on the microbial assemblages among different habitats in a range of systems including forestsIn the present study, we investigated the microbial communities in three different habitats in a grassland ecosystem, i.e. animal excrement, phyllosphere, and soil. The difference and similarity of microbial community structure among different habitats in grassland ecosystem was analysed using Illumina sequencing of amplicon libraries that targeted bacteria, archaea, and fungi. In addition, the keystone microorganisms in connecting among microbiomes in this animal excrement-plant-soil system were also identified. This study serves to advance our knowledge of the microbial ecology of different grassland habitats and to improve analysis regarding the impact of microbial communities on the health and functioning of grasslands.In total, 590,663 bacterial 16 S rRNA gene sequences were obtained from 12 samples from the 3 grassland habitats, which were then clustered into 4,382 operational taxonomic units (OTUs). Furthermore, 586,063 archaeal sequences (164 OTUs) and 638,002 ITS sequences  were retrieved from 16 S rRNA and ITS gene sequencing, respectively. To compare the samples with the same sequencing depth, normalization of the sequence number was conducted by extracting the minimum number of bacterial , archaeal , and fungal  sequences in each sample , but the microbial diversity in the phyllosphere varied among the samples, with a higher similarity to the fungal communities in the soil as well as to the bacterial and archaeal communities in the animal excrement. The trends of the Chao 1 estimator and Shannon index in all three habitats were all consistent with the results of OTU richness, except for the Shannon index in the bacterial communities  in this habitat. In the phyllosphere samples, Actinobacteria was the most abundant phylum, accounting for nearly half of the bacterial sequences in the phyllosphere samples. In addition, nearly 47% of the bacterial sequences of the soil samples  were derived from phyla Actinobacteria. Acidobacteria (15%) and Alphaproteobacteria (9%) also comprised the major bacterial components in the soil samples, and they were found in relatively high abundance in shallow soil samples Fig. .Figure 1In the animal excrement samples, the archaeal communities were mainly comprised of phylum Euryarchaeota, which accounted for 96% of the archaeal sequences, followed by unclassified archaea (3%) and Thaumarchaeota (1%). Specifically, classes Methanobacteria and Methanomicrobia were the dominant archaea with 62% and 31% of the archaeal sequences in excrement samples, respectively. Phyla Thaumarchaeota and Euryarchaeota accounted for > 90% of the archaeal sequences from the phyllosphere samples, and Soil Crenarchaeotic Group (78%) was the most dominant class in the phyllosphere, followed by Methanobacteria (13%) and unclassified archaea (9%). In the soil samples, Thaumarchaeota accounted for >90% of the archaeal sequence data, and the Soil Crenarchaeotic Group was the only class in this phylum Fig. .In the fungal communities, the phylum Ascomycota was clearly dominant in all samples, especially accounting for 99% in the animal excrement samples. Furthermore, classes Sordariomycetes (42%), Leotiomycetes (28%), and Dothideomycetes (27%), belonging to phylum Ascomycota, were found in high abundance in the animal excrement samples. In the phyllosphere samples, class Dothideomycetes was predominant (71%), followed by classes Sordariomycetes and Tremellomycetes . In the soil samples, Ascomycota accounted for 60% of the fungal sequences and was mainly composed of classes Sordariomycetes (27%) and Dothideomycetes (10%), and unclassified members of Ascomycota (12%) Fig. .Microbial comparison analysis were performed to determine the changes in microbial composition among three different habitats, i.e. animal excrement, phyllosphere, and soil. The principal coordinates analysis (PCoA) plot, based on Bray-Curtis distances, revealed the differentiation of the composition of the microbial communities across the three habitats Fig. . SamplesTo determine the microorganisms that most likely explained the observed differences among samples from the three habitats, we performed a linear discriminant analysis of the effect size (LEfSe). The LEfSe analysis revealed the representative microorganisms in the three habitats, including 9 taxa in the animal excrement samples, 7 taxa in the phyllopshere samples, and 18 taxa in the soil samples Fig. . Animal The network and Venn diagrams revealed the overlap and partitioning of the microbial OTUs across three habitats Fig. . Almost Although relatively few microbial OTUs  were shared among the three habitats Fig. , these cThe microbial co-occurrence patterns were explored using a network analysis Fig. . The micZi-Pi plots , more specifically the ammonia-oxidizing genus Candidatus Nitrososphaera and other unclassified members of the Soil Crenarchaeotic Group. Our results are fairly consistent with the information available on archaeal communities from a global soil investigation, i.e. a general dominance of Crenarchaeota (recently classified as Thaumarchaeota), along with minor fractions of Euryarchaeota44. Besides, members of the Soil Crenarchaeotic Group were also found to be dominant in savanna soils in South Africa45. However, in contrast, a large proportion of the taxa abundant in animal excrement or phyllosphere samples were rare in soil samples and vice versa  and, based on a meta-analysis in a global scale48, is reportedly the most abundant class in phyllosphere fungal communities. Moreover, Capnodiales, one of the major orders within class Dothideomycetes in the phyllosphere samples in the present study, reportedly have a high tolerance for stressful conditions, including high solar irradiation, extreme temperature shifts, and prolonged desiccation49; conditions that are typically characteristic of the phyllosphere environment50. Overall, the distinct distribution of these microorganisms among the different habitats indicated a significant degree of habitat preference of these microorganisms.The microbial community structures of the different habitats were observed to be differential Fig. . In soil52. In the present study, a high number of the microbial taxa were found to be habitat specialists , with soil having the highest unique OTU richness , they only accounted for 20.09% of the total sequences. This is consistent with the finding in previous studies, which showed that habitat specialists have lower population densities due to limited efficiencies in finding suitable habitats during the dispersal process34. Members of Actinobacteria, Proteobacteria, Thaumarchaeota, Dothideomycetes, and Sordariomycetes were detected among three distinct habitats in this study  in the Xilingol steppe grassland. A large number of microorganisms were detected in the individual habitat types, of which only 7.81% of the microbial OTUs were shared among the three habitats. Our findings suggest a strong niche differentiation and habitat filtering of microbial communities in the grassland ecosystem. However, the coexisting microorganisms comprised over 60% of the sequences, despite their limited population, which indicates that they have a high dispersal potential. Moreover, eight microbial OTUs were identified as the keystone taxa in this study, which indicates their significant role in the complex ecological interactions across the different microorganisms in the grassland ecosystem. Overall, this is the first report that directly compares the microbial compositions of different habitats in a grassland. Further studies are needed to elucidate how this habitat filter affects the ecological processes and functioning of the grassland ecosystem.88. The sampling sites were at an elevation of 1100 m. The dominant soil type of the study site was chestnut loam and the dominant vegetation species were Leymus chinensis, Agropyron cristatum, and Stipa grandis. The main large herbivores that inhabit the Xilingol steppe include Ujimqin sheep, Luxi cattle, and Arabian horses. A sampling transect was created (50 m × 50 m) and three different vegetation types as well as three animal species were selected. Animal excrement samples and grass samples were collected in triplicate in each sampling area. Corresponding soil samples were collected from two different depths, comprising the shallow soil samples  and deep soil samples , determined by a steel probe (1 m). Soil samples were sieved to 2 mm particle size for subsequent experiments. All samples were labelled and stored at –80 °C until use for DNA extraction.This study was conducted in the Xilingol steppe grassland , in the central-east of Inner Mongolia Autonomous Region, China. The mean annual precipitation and temperature of this region is 195 mm and 5.1 °C, respectivelyet al.89. Briefly, the phyllosphere microorganisms were detached from the grass surface by shaking the grass samples in sterile water, in a 1:10 ratio, at 25 °C; the treated grass samples were subjected to ultrasonic disruption for 3 min at 300 W; the same volume of sterile water was added to the grass samples and the ultrasonication was repeated four times. Microbial enrichment was then performed by passing the liquid through a 0.22 μm mixed cellulose ester membrane . Genomic DNA from the phyllosphere microorganisms in the enriched membrane was extracted using the MoBio PowerSoil kit after membrane shearing. The final DNA samples obtained were a mixture of triplicate extracted DNA in the same volume from the three replicates in the same samples, and were stored at −80 °C until further analysis.Genomic DNA from the all samples was extracted using a MoBio PowerSoil kit  according to the manufacturer’s instructions. Genomic DNA from the grass samples were extracted using a modified method of Zhao 90. Primers 524F-10-ext (5′-TGYCAGCCGCCGCGGT AA-3′) and arch958R (5′-YCCGGCGTTGAVTCCAATT-3′), specific to the V4–V5 region of the archaeal 16S rRNA gene, were used to create the archaeal libraries91. Primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCG ATGC-3′) were used to create fungal libraries92. Three replicates were conducted for each DNA sample and a negative control containing 1 μL sterile water was prepared to determine the PCR reagent contamination. PCR program conditions were as follows: initial denaturation at 95 °C for 3 min; 35 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s; and a final extension at 72 °C for 10 min. Three independent PCR products for each DNA sample were then mixed to prepare the PCR amplicon library to minimize the impact of potential early-round PCR errors93. The PCR products were quantified and homogenized with a QuantiFluorTM-ST  and were sent for sequencing on the Illumina MiSeq PE300 platform by the Genomic Research Center at Majorbio Bio-pharm Technology Co., Ltd. .For the Illumina MiSeq sequencing, universal bacterial primers 338F (5′-ACTC CTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), that target the V3–V4 region of the 16S rRNA gene, were used to create the bacterial libraries94. Barcode and primer sequences, as well as sequences shorter than 200 bp or longer than 550 bp were also removed. Pair-end sequences were merged into full-length sequences using FLASH software95. Chimeras were identified and discarded using the “chimera.uchime” command in Mothur package (v 1.30.1)96. Similar sequences were clustered into OTUs with a 97% similarity using USEARCH v7.097. For bacterial and archaeal libraries, the Silva database (release 128) was used for taxonomic assignment of OTUs using RDP classifier (v2.2)98. For fungal libraries, OTUs were classified with reference to the UNITE database (release 6.0). Non-target sequences, including mitochondria, chloroplasts, and non-fungal eukaryotic sequences were also removed from the final OTU dataset by QIIME99.Before analysis, raw sequences with a quality score below 30 and containing ambiguous base (“N”) were trimmed by using Trimmomatic96. The compositions of three microbial groups, i.e. bacteria, archaea, and fungi, were displayed using an average relative abundance for each taxa with R software100. To better convey the biological information in these samples, the bacterial communities were visualized at the phylum level, whereas the archaeal and fungal communities were displayed at the class level. The PCoA, based on the Bray Curtis distance matrix, was used to assess the beta diversity patterns to determine the dissimilarity of the microbial community compositions among the three habitats: animal excrement, phyllosphere, and soil. To determine the differences in the microbial community compositions of the different habitats, we used a permutational multivariate analysis of variance (PERMANOVA) in R software. The LEfSe, which uses a non-parametric factorial Kruskal-Wallis (KW) sum-rank test and performs a linear discriminant analysis (LDA) to evaluate the effect size of each taxa101, was performed using an online LEfSe program  to identify the microbes that differed significantly in abundance among the three habitats.Alpha diversity indices, including richness, Chao 1 estimator, Shannon index, and Good’s coverage of samples were calculated using Mothur104, to gain a comprehensive understanding of the microbial interactions among the different microbiomes. The OTUs with more than five sequences were used because they are more representative in the network analyses105. The OTUs that occurred in more than half of all samples were kept in the pipeline using default settings. A similarity matrix was constructed based on Pearson’s Correlation Coefficient between pairwise OTUs. A cutoff value  was chosen according to the P value of the similarity matrix (P > 0.05). After filtering the pairwise OTUs, i.e. those with abundances lower than the St, a new OTU table was generated for the subsequent network construction. After the “global network properties”, “individual nodes’ centrality”, and “module separation and modularity calculation” were calculated, the networks were visualized with the interactive platform Gephi106.A network analysis was performed using Molecular Ecological Network Analysis PipelineSupplementary materials


Parus major) and blue (Cyanistes caeruleus) tits in Belgium and examined if forest fragmentation affects these parameters. Analysis of 70 great tit and 34 blue tit eggshells revealed a similar microbiota composition , but higher bacterial loads in great tits. Forest fragmentation was not identified as an important explanatory variable. Although a significant negative correlation between hatching success and bacterial load on the eggshells in great tits corroborates microbial pressure to be a driver of embryonic mortality, the overall hatching success was only marginally lower than in blue tits. This may be explained by the significantly higher levels of lysozyme and IgY in the eggs of great tits, protecting the embryo from increased infection pressure. Our results show that immune investment in eggs is suggested to be a species-specific adaptive trait that serves to protect hatchlings from pathogen pressure, which is not directly linked to habitat fragmentation.The hatching success of a bird’s egg is one of the key determinants of avian reproductive success, which may be compromised by microbial infections causing embryonic death. During incubation, outer eggshell bacterial communities pose a constant threat of pathogen translocation and embryo infection. One of the parental strategies to mitigate this threat is the incorporation of maternal immune factors into the egg albumen and yolk. It has been suggested that habitat changes like forest fragmentation can affect environmental factors and life-history traits that are linked to egg contamination. This study aims at investigating relationships between microbial pressure, immune investment and hatching success in two abundant forest bird species and analyzing to what extent these are driven by extrinsic  factors. We here compared (1) the bacterial load and composition on eggshells, (2) the level of immune defenses in eggs, and (3) the reproductive success between great ( Embryonic development of birds is a process that is threatened by microbial invasion . VerticaTo minimize embryonic contamination, the composition of the egg creates a natural physical barrier against bacterial penetration , and togFirmicutes (including Lactobacillus) and Bacteroidetes and the family of Enterobacteriaceae are amongst the most abundant bacterial groups [Enterobacteriaceae such as Escherichia coli, Salmonella spp. and Yersinia spp., but also Clostridium perfringens belonging to the Firmicutes phylum [Several studies showed that the number of bacteria present on the eggshell is positively associated with the risk of trans-shell infection , 10. Notl groups –13. Mosts phylum .Not only host and pathogen characteristics can influence embryonic development, but also the environment should be taken into account. Changes in host habitat, such as fragmentation of large, homogenous habitat blocks into small, isolated patches, can affect both extrinsic  and intrinsic (life-history traits) factors of the host which are linked to egg contamination and breeding performances. For instance, it has been described that brood parasitism is associated with increased bacterial contamination of host eggs –16 and tTo fill this knowledge gap, this study aims at investigating these relationships in great and blue tits, two relatively closely-related forest species with strongly overlapping ecological niches that are widely distributed and abundant throughout Europe. We first examined bacterial infection pressure (load and community composition) on eggs of free-ranging blue and great tits in 19 mature deciduous forest fragments in East-Flanders (Belgium) and analyzed to what extent bacterial loads varied with fragment area. Next, we analyzed variation in maternal immune investment  into eggs and the extent to which this was correlated with hatching success.Quercus robur), Red oak (Q. rubra) and Beech (Fagus sylvatica) in monocultures, 2 species mixtures or 3 species mixtures). Surface area sizes for each forest fragment were calculated from detailed GIS layers.We performed a study of blue and great tits in 53 study plots located in 19 mature (> 60 years) deciduous forest fragments in the south of Ghent , northern Belgium . All stuDuring autumn 2014, 212 standard nest boxes for blue and great tits  were installed at a height of 1.5 m, at each corner of a plot, of which 3 broke during the experiment [Since fragmentation can lead to changes in feeder visiting, which is correlated with pathogen acquisition and transmission –19, it sTo avoid intraclutch variation, the fifth egg of each great and blue tit clutch was collected using sterile gloves, stored in a sterile bottle and transported to the laboratory where the eggs were cracked under a laminar flow cabinet. Egg yolk and egg white were collected and stored separately at -20°C. In order to avoid antimicrobial activity of the albumen, the inside of the eggshells was washed with sterile phosphate buffered saline (PBS) to remove the adhering egg albumen. The eggshell, including shell membranes, was transferred to an Eppendorf tube and stored at -20°C.The percentage of hatching failure was calculated as ((1-(number of hatchlings/(clutch size– 1)))*100).Micrococcus lysodeikticus  suspension was prepared in phosphate buffer (0.5 mg/ml). A hundred μl of the diluted albumen and 100 μl of the Micrococcus suspension were added to a 96 well plate  and the absorbance was measured every 2 minutes, during 30 min at room temperature and at 450 nm using a Multiskan MS Reader  with the Ascent Software, version 2.6. Each sample was analyzed in duplicate and before each measurement, the plate was mixed for 10 s. The results, given as Unit/mg protein, were calculated from the changes in absorbance per minute and compared to the standards .We assessed lysozyme concentrations following Ruuskanen et al. (2011) . BrieflyTo measure avidin, we used a modified version of the colorimetric method of Gan & Marquardt (1999) . TherefoThe concentration of ovotransferrin was determined using the total iron binding capacity assay of Yamanishi et al. (2002) . TherefoThe antibody (IgY) level was determined using an indirect enzyme-linked immunosorbent assay (ELISA), modified from Morosinoto et al. (2013) . BrieflyFirmicutes and Bacteroidetes phyla, Enterobacteriaceae family and Lactobacillus spp. were quantified using the primers and PCR protocols described in DNA was extracted from the eggshell using a PowerLyzer® PowerSoil® DNA Isolation Kit  according to the manufacturer’s guidelines. The abundance of total bacteria, Firmicutes, Bacteroidetes, Enterobacteriaceae and Lactobacillus numbers were influenced by fragment area, egg and nest characteristics  or species identity (i.e. great versus blue tit), linear mixed models (‘glmer’ function of R library ‘lme4’ [https://rdrr.io/github/markushuff/PsychHelperFunctions). All c_hat variance inflation factors were larger than 1 (varying from 1.12 to 5.92) and were significant for all Poisson models considered . Therefore, an observation-level random effect was added in order to account for the overdispersion present in the data [First, in order to test whether total eggshell bacteria, y ‘lme4’  were runy ‘lme4’ , while sthe data .Second, in order to test whether bacterial eggshell communities significantly differed between great tits and blue tits, we applied an analysis of dissimilarity  Ghent site (EC2015-023).5 ± 1.83 x 105 and 3.67 x 105 ± 5.49 x 104, respectively. This corresponds to a Log10 value of 5.70 ± 0.05 for great tits and 5.42 ± 0.07 for blue tits  higher in egg albumen and egg yolk of great tits compared to blue tits . Mean ± Tables. Hatching success in clutches of great tits declined with increasing bacterial load of the fifth egg  did not differ between great and blue tits, with the Firmicutes phylum being the most abundant in both species. Such pattern is in line with gastrointestinal microbiota sampled in adults from various bird species [Enterobacteriaceae family such as E. coli, Salmonella, Yersinia, Klebsiella, Citrobacter and Enterobacter have been reported to cause disease and mortality in nestling passerines [Streptococcaceae of the phylum Firmicutes has been reported to cause embryonic death and infections in nestlings [In contrast to bacterial load, relative egg microbiota composition Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


Recent technological advances will soon allow pastoral dairy farmers to manage their cattle using increasingly intense and complex grazing regimes. Ensuring there is merit in the implementation of more intense grazing regimes will minimise the potential misuse of this technology and the associated consequences of misuse for animal welfare and productivity. Two groups of dairy cattle were provided with the same amount of fresh pasture over either 2 or 7 feeds per day. Cows that received pasture over 7 feeds produced less milk and spent less time ruminating, but more time resting, per day. However, feeding frequency did not affect the time cows spent feeding or estimated pasture consumption. Increasing the frequency of feeding restricts the amount of pasture available to cattle at any one time. In response, cattle adjust their ingestive behaviour  which may negatively impact digestive processes. The success of more intensive pastoral dairy production requires grazing regimes that support the natural ingestive, digestive, and social behaviours of cattle, rather than necessitating cattle to substantially alter their behaviour in accordance with the grazing regime imposed upon them.+). Milk composition, energy corrected milk (ECM), and live weight were recorded weekly. Feeding mid-lactation dairy cows over seven smaller grazing allocations reduced the time cows spent ruminating (p < 0.001), milk yield (p < 0.001), and ECM (p < 0.05). However, milk composition, live weight, time feeding, and pasture consumption were not affected by feeding frequency (p > 0.05). Cattle may have adapted their ingestive behaviour in response to the more intensive strip-grazing regime utilised in this study, with negative consequences for digestive processes and consequently milk production. Intense grazing regimes need to support the ingestive, digestive, and social behaviours of cattle.Research into the effects of intense grazing regimes on cattle behaviour and productivity will support the ethical intensification of pastoral dairy production. Two treatments were applied to two herds of 30 mid-lactation cows over 28 days. Cows were offered an estimated 12 kg DM/cow (above 5 cm from ground level) of irrigated pasture per day. The control herd received their daily pasture allocation in two equal grazings while the experimental herd received theirs over seven smaller grazings. Backgrazing beyond the current allocation (morning or afternoon) was prevented. Individual records were taken daily for milk production and behaviour (MooMonitor The intensification of animal production is often associated with long periods of confinement and indoor housing , rather Increasingly intense grazing regimes are likely to underpin the continued intensification of animal production in pasture-based systems . At presA more intensive version of strip-grazing, characterised by the provision of fresh pasture to dairy cattle in frequent yet small quantities throughout the day, is one example of a grazing regime which may be made practical by virtual fencing technology. Increasing the frequency of pasture allocation could improve pasture utilisation by reducing damage to palatable herbage caused by fouling or treading . A more The present experiment examined the effect of providing the daily pasture allowance of mid-lactation dairy cows over seven smaller allocations, focused during day light hours, on dairy cow behaviour, and productivity. By increasing our understanding of cattle response to a simple, yet more intense strip-grazing regime, this experiment aimed to produce scientific knowledge that (1) supports the ethical intensification of pastoral dairy production systems; and (2) provides a foundation upon which more complex intensive grazing regimes can be developed. The hypothesis being tested was that the frequent presentation of fresh pasture would increase the time cows spent grazing, pasture consumption, and milk yield.All animal procedures were conducted with institutional ethical approval  prior to the start of the experimentation.This experiment was conducted over 4 weeks in summer at the Tasmanian Institute of Agriculture Dairy Research Facility (TDRF) at Elliott, NW Tasmania . The mean maximum and minimum daily ambient temperatures (°C) for the period of experimentation were 19.3 and 10.4, respectively. The milking herd at TDRF consists of 360 spring calving cows that rotationally graze approximately 112 hectares of pasture . The herd is comprised of approximately 30% Holstein Friesian animals with the remainder primarily being cross-bred Friesian × Jersey, with fewer numbers of Friesian × Aussie Red. Cows are milked twice per day (approx. 0530 and 1500) in a 20 aside swing-over herringbone dairy with automatic cup removers, and supplementary concentrate provided in the dairy .Bos taurus) from the TDRF milking herd were utilised in this experiment. Over the 28 day experimental period, selected cows grazed eight irrigated paddocks (range 2–5 days grazing per paddock), with one paddock being grazed twice (26 days between rotations). The average (±s.d.) paddock size was 1.4 ± 0.32 ha. Cows were offered an estimated 12 kg pasture DM/cow (6 kg DM/cow after the morning and the afternoon milking) and 4 kg of supplementary concentrate  per day. Pasture allowances were based on herbage mass estimates measured using a rising plate meter  that utilised the following site specific calibrated equation:Sixty mid-lactation multiparous (parity range 2–7) dairy cows  that were balanced for breed, age , live weight , days since calving , and milk production . These four groups were combined to produce two herds of 30 animals, and two treatments that differed in feeding frequency were applied to these herds. For all cows, grazing commenced after each of the two milkings per day (cows removed from paddock for milking at approx. 0700 and 1430 h). Cows in the control herd received their daily pasture allowance in two equal grazings per day . For cows in the treatment herd, the morning pasture allocation was provided in 4 equal strips  and the afternoon pasture allocation in 3 equal strips . Thus, treatment cows received their daily pasture allowance in seven grazings per day . For convenience, in the remainder of this paper, the provision of a strip of pasture to cows from the treatment herd is described as belonging to the ‘AM’ or ‘PM’ pasture allocation, depending on the time of day that the pasture was measured and allocated, rather than when each portion of the pasture was provided.n = 15 cows) and one group of cows from the 2A herd (n = 15 cows) crossed treatments. The other two groups of cows remained in the experimental or control treatments. The treatments were then applied to the new herds of 30 cows for a second period of 14 days. Again, cows had a 3 day adjustment period followed by 11 days of observation. Thus, two treatments were applied to two herds of 30 cows over two periods, with the composition of the 2A and 7A herds being different from days 1 to 14 compared to days 15 to 28.This experiment was a high-order crossover study that incorporated Balaam’s design, which is characterised by the application of two treatments  over two periods in one of four possible sequences: XY (control-treatment), YX (treatment-control), XX (control-control), and YY (treatment-treatment). This hybrid of a crossover design and a parallel design allows for unbiased estimations of differences in the presence of carryover effects . In the Paddocks were evenly divided into two sub-paddocks using electrified galvanised steel fencing wire, which allowed the two experimental herds to graze the same paddock side by side. The sub-paddock being grazed by each herd alternated daily. The following milking and grazing procedures were consistent with conventional practise. Grazing commenced after each of the two milkings per day. Backgrazing beyond the current allocation (morning or afternoon) was prevented using electric fences. This also allowed a more reliable estimate of pasture consumption (see measures recorded) per grazing to be calculated. It took approximately 45 to 60 min to collect, milk, and draft the 60 cows. The 2A herd trickled back to their paddocks after milking , but 7A cows were drafted upon exit from the dairy and moved to their paddock as a herd. This ensured all cows in the experimental treatment had equal access to fresh pasture in the first grazing period post-milking.Milk yield of each cow, at each milking (morning and afternoon), was recorded using a DeLaval Alpro milk metering system  at the day prior to the commencement of the experiment (day −1) and at each day of the experiment. As milk secretion is a continuous process in lactating cows , the effMilk composition samples were collected at both the morning and afternoon milking at day −1, and weekly thereafter . Milk samples were assayed for fat and protein concentrations (g/kg), and for somatic cell count (SCC) using a Bentley B2000 Infrared Milk Analyser  by TasHerd Pty Ltd. . The following milk composition variables were then calculated for each cow: (1) kilograms of fat and protein per sample day; (2) percent of fat and protein per sample day; (3) the average SCC (cells/mL) per milking and sample day; and (4) the fat and protein corrected milk yield (FPCM). The FPCM provides an indication of the amount of milk produced adjusted to a standard of 4.00% fat and 3.40% total protein, and was calculated using the following formula :FPCM  as animals exited the dairy. The AWS100 software utilises an algorithm that discounts recorded weights that differ greatly from the 7 day mean weights for individual animals . Thus, the 7 day average weight data provides a more accurate representation of cow live weight and live weight changes.+, Dairymaster Inc., Kerney, Ireland) at day −1. The commercially available MooMonitor+ uses a 3-dimensional accelerometer to determine cow movement and head direction, and, based on proprietary algorithms, distinguish between feeding, resting, and ruminating behaviours [+, the present experiment continuously monitored the time cows spent feeding, resting, and ruminating, and used this data to calculate cow behaviour per day and per hour. For the purpose of this study, a “behavioural day” is defined at the 24 h starting from 0700, when cows were transferred from paddock to the dairy.Each cow was fitted with a neck-collar mounted activity monitoring system (MooMonitorhaviours . Using tEstimated pasture intake (kg DM/cow) for the herd was calculated from pre- and post-biomass estimates using the rising plate meter  were removed within 24 h of the trial commencing due to either lameness or mastitis and were replaced with animals that were comparable in weight, parity, days in milk, and milk yield. No data from days −1 or 1 were available for these replacement cows. Two cows were temporarily removed during the experimental period to be treated for mastitis (1 × 2A cow from days 19 to 23 and 1 × 7A cow from days 26 to 28). Pasture allocations were adjusted in their absence. Extreme values (greater than 3 × SEM) for variables relating to milk composition, milk production, and behaviour, and that were not characteristic of the individual cow (based on previous and following 3 days of data), were assessed to be technical errors and so removed as outliers for that specific day . Due to a technical malfunction no data on milk production were obtained for days 18 and 19 (9% of milk production data).Data collected from animal-based measures has a hierarchical structure in that individual cows were nested within herds of 30 animals. Thus, the possible nonindependence of observational units in our experiment cannot be eliminated. This limitation could not be overcome practically with a different experimental design . There ip ≤ 0.05) the LSD test determined where least square means differed. Data are presented as least square means and pooled standard errors. For the transformed variables, transformed least square means and pooled standard errors are presented with back transformed means presented in parenthesis.All statistical analyses were carried out using either general linear mixed model (LMM) or generalized linear mixed model (GLMM) function of the SPSS statistical software package . Normality was assessed prior to statistical analysis using visual methods (quantile-quantile plots and histograms). Data that were not normally distributed were transformed prior to analysis, so that the residual variation was homogenous between treatments within periods. Specific transformations are indicated in the results. When an interaction was not significant it was removed from the model so that significant effects could be more reliably interpreted. Where there were significant main or interactive effects  in a fixed period of time . GLMMs with an underlying Poisson distribution and log link function were then developed to assess the effects of treatment on behaviour per hour. A negative binomial distribution was fitted when behaviour data per hour were over dispersed. To simplify the analysis, separate models were developed for each hour within a day. The model specified treatment as a fixed effect, while paddock was controlled for as a random effect. Observations on the same individuals were repeated longitudinally over days, so each model accounted for repeated observations of the same cow (within period) over day with a first order auto-regressive correlation structure.To explore the effects of treatment on the relationships between cow behaviour , milk production (L milk yield per day), and characteristics of the individual cow , partial correlations that controlled for day, paddock, and period were conducted for each treatment.The effects of grazing treatment on daily pasture consumption were analysed using LMM. The subject of analysis was the grazing day nested within paddock (within period). Treatment period and their 2-way interaction were fitted to each model as fixed effects. Characteristics of the pre-grazed sward can effect bite mass and intake rate (reviewed by ), so pre1,111 = 27.5, p < 0.001; 2,187 = 59.7, p < 0.001; Cows offered fresh pasture twice per day produced more litres of milk than those fed fresh pasture 7 times per day  in mean milk fat and protein concentrations , measured at the day prior to the application of treatments.The four groups of 15 cows did not differ  and FPCM  produced per day was lower, and there was a tendency for the kilograms of protein produced per day to be lower , in the 7A compared to the 2A cows , protein , and FPCM  produced each day was lower at the second sample day in a period than in the first , while SCC increased over days for both treatments , and less time ruminating  than cows allocated pasture twice per day . However, there was no effect of treatment on the time cows spent feeding . There was a significant effect of prior treatment on the behaviour variables of feeding  and ruminating , but not resting . The crossover did not affect the time cows spent feeding and ruminating. However, cows that were assigned to the 2A treatment for period 1 spent more time feeding in period 2, regardless of treatment in period 2, and all cows spent less time ruminating in period 2 than period 1  or by prior treatment . Irrespective of treatment, all cows weighed more at the first sample day in a period than at the second , but positively correlated to milk yield for cows in the 7A treatment . There was no relationship between start weight  and milk yield for cows in the 2A treatment . By contrast, the start weight of cows in the 7A treatment  was positively correlated with milk yield .Age was negatively correlated with milk yield for cows in the 2A treatment (p > 0.05). Estimated pasture consumed per day was comparable between 2A (10.3 ± 0.17 kg DM/cow day−1) and 7A (10.3 ± 0.20 DM/cow day−1) treatments , but both treatments consumed more in period 2 (10.7 ± 0.21 DM/cow day−1) than period 1 . The estimated mean (±s.d.) post-grazing herbage biomass was 1759 ± 144 kg DM/ha and 1782 ± 141 kg DM/ha for the 7A and 2A treatments, respectively.On average, the estimated pre-grazing herbage mass to ground level was 3332 ± 422 kg DM/ha. There were no interactive effects in the analysis of estimated pasture consumption , feeding mid-lactation dairy cows their daily pasture allowance over seven smaller grazing strips per day  results in a reduction in milk yield and time spent ruminating, and an increase in time spent resting. However, the more intensive strip-grazing regime utilised in the present study did not affect milk composition, pasture consumption, or the time cows spent feeding per day.Research by Dalley and colleagues  found reWhile cows in the 7A treatment of this experiment were offered the same daily pasture allowance as those in the 2A treatment, the quantity of dry matter available to 7A cows at any one time was temporally restricted by the incremental delivery of pasture. Cattle compensate for restricted pasture availability by increasing bite mass and/or decreasing the time taken to masticate each bite . The delThe frequency, quantity, and timing of pasture allocation in the present experiment may also have disrupted normal grazing and rumination cycles. Firstly, while rumination generally follows ingestion, feeding has priority over rumination whenever the causal factors of the two behaviours are in conflict ,38. Gregr2 values for rising plate meter (RPM) calibration curves on irrigated perennial ryegrass at the same site as the present experiment were reported by Pembleton et al. [Regardless of treatment, cows produced 3 L less milk in period 2  than period 1 . This reduction in milk yield explains why SCC, expressed as a concentration of cells per mL, increased over time, and particularly for the 7A treatment. It also partially explains the effects of sample day on kilograms of protein and fat produced, although seasonal and paddock effects may have contributed. The rapid decline in milk yield reported in both treatments of the present research indicates the contribution of factors other than the natural reduction expected for dairy cows in mid-lactation. The 2 per cow, whereas the minimum recommended space allowance for beef cattle in Australian feedlots is 9 m2 per cow [n = 30 per treatment).An interesting finding of this research was that, although treatments were balanced for age and weight, older and heavier cows in the 7A treatment produced more milk, but the same relationships were not significant in the 2A treatment. This study placed considerable spatiotemporal restrictions on the availability of feed to 7A cows. Larger cows may have a competitive advantage under such conditions, partly because their increased mouth size which enables a higher intake per bite . The spa per cow . In catt per cow ,48, weig per cow , and, wh per cow ,47,49. S per cow . The impFeeding mid-lactation dairy cows their daily pasture allowance over seven smaller grazing strips (7A cows) may have reduced the time cows spent ruminating, milk yield, and energy corrected milk yield, compared to cows offered fresh pasture twice per day (2A cows). However, milk composition, the time cows spent feeding, and pasture consumption do not appear to be affected by feeding frequency. Multiple brief periods of high intake rates may have led to poor mastication and improper packing of ingesta for 7A cows, resulting in reduced energy utilisation for productive purposes. Grazing may also have been stimulated at the expense of rumination in 7A cows, while a low rumen fill may have compromised rumination, particularly during the night. The success of increasingly intensive grazing regimes is dependent on a design that supports the natural ingestive, digestive, and social behaviours of cattle, rather than requiring cattle to substantially alter their behaviour in accordance with the grazing regime imposed upon them. Further research is needed to determine the details of complex intensive grazing regimes, including the effects that the temporal availability of pasture strips, the quantity of pasture available in each strip, and the interaction between these two factors, has on the behaviour and productivity of lactating dairy cows.


P < 0.0001) independent of other spirometric indices. The Obstructive Index is a useful spirometric index that reflects the extent of emphysema.A concave-shaped maximal expiratory flow-volume (MEFV) curve is a spirometric feature in chronic obstructive pulmonary disease (COPD). The MEFV curve is characterized by an increase in the Obstructive Index, which is defined as a ratio of forced vital capacity to the volume-difference between two points of half of the peak expiratory flow on the MEFV curve. We hypothesized that the Obstructive Index would reflect the severity of emphysema in patients with COPD and asthma-COPD overlap (ACO). Thus, the aim of this retrospective study was to evaluate whether the Obstructive Index on spirometry is associated with the extent of emphysema on computed tomography (CT) in patients with COPD, ACO, and asthma . The percentage of low-attenuation volume (LAV%) and wall area (WA%) were measured on CT. The Obstructive Index was higher in patients with COPD and ACO than in those with asthma. Spearman correlation showed that a greater Obstructive Index was associated with a higher LAV%, but not WA%. Multivariate analysis showed that Obstructive Index was associated with LAV% (standardized β = 0.43,  The heterogeneity of COPD further complicates its clinical management3. It can be classified into emphysema or non-emphysema phenotypes, which are easily identifiable on computed tomography (CT). Numerous CT studies have shown that the emphysema phenotype is associated with a rapid decline in forced expiratory volume in 1 second (FEV1)4, frequency of exacerbations5, a poor prognosis6, osteoporosis7, occurrence of lung cancer8, loss of skeletal muscle mass9, and reduction in body mass index (BMI)10. These findings emphasize the importance of identifying the emphysema phenotype in the early stage of the disease; however, chest CT scans entail radiation exposure11 and are not always available in primary care facilities. In addition, although it is possible to estimate the extent of emphysema by measuring diffusion capacity, this physiological examination also requires large equipment and it is impractical to routinely perform this examination for all patients suspected of having obstructive lung diseases.Chronic obstructive pulmonary disease (COPD) is a major concern worldwide, as its prevalence and mortality rate continue to increase, imposing a huge economic burden1 and forced vital capacity (FVC). In addition, it allows for the visualization of the maximal expiratory flow-volume (MEFV) curve. MEFV curves with varying concave contours are seen in patients with COPD, and previous reports have shown that this concave shape was partly attributable to loss of elastic recoil13.Spirometry is widely used for low-cost management of COPD and non-invasively measures lung function parameters, such as FEV14. In this study, we explored the usefulness of the Obstructive Index by comparing quantitative CT (QCT) of lung emphysema with visual assessment of MEFV curves in patients with chronic obstructive lung diseases.Therefore, we hypothesized that it would be possible to predict the extent of emphysema among patients with obstructive lung diseases by using a simple spirometry measurement to obtain the Obstructive Index, initially proposed in Japan in 1978, which expresses the concavity of MEFV curves even in patients who have low FVCBetween January 1, 2015, and March 31, 2017, 10,808 patients had a chest CT scan and 3,875 patients had a spirometry test at the Tsukuba Medical Centre Hospital. Among them, 942 patients had both a chest CT scan and a spirometry test within a 3-month period on the same day or on separate occasions. Four hundred and six patients were excluded because their thin slice digital imaging and communications in medicine (DICOM) data were not available. Furthermore, patients with lung cancer (N = 249), interstitial pneumonia (N = 42), bacterial pneumonia (N = 35) and pleural effusion (N = 14), as well as 62 patients who had surgery were excluded. Finally, one patient with a severe cough during spirometry was excluded. A total of 133 patients who had COPD, asthma–COPD overlap (ACO), or bronchial asthma (BA) were subsequently analyzed for the current study . The half of the peak expiratory flow (PEF), i.e., the reference point of the Obstructive Index, was larger than the forced expiratory flow at 50% (FEF50), which reflects the concavity of the MEFV curve in COPD or ACO compared to BA, suggesting that half of the PEF is reached faster than the halfway volume of the MEFV curve, FEF50, in COPD or ACO. In terms of QCT measurements, the percentage of low attenuation volume (LAV%) was highest in the COPD group, followed by those in the ACO and BA groups, while there was no difference in the percentage of wall area (WA%) of the right apical segmental bronchus (B1) or right anterior basal segmental bronchus (B8) among the disease groups.Table The classification of MEFV curves is shown in Fig. ρ = 0.56, P < 0.0001), but not with the WA%. FEV1 , FEV1% predicted , forced expiratory flow between 25 and 75% (FEF25–75), , and FEF25–75/FVC  were also related to the LAV%. FEV1 and FEV1% of the predicted were weakly correlated with the right B1 WA%  , which was independent of FEV1% predicted, FEF25–75/FVC, and lower FEV1/FVC, as judged by the lower limit of normal for the FEV1/FVC ratio. BMI was significantly inversely associated with the extent of emphysema . These correlations of LAV% with the Obstructive Index and BMI remained significant for a subgroup consisting of patients with both COPD and with ACO , and for a subgroup consisting of only patients with COPD only   for receiver operating characteristic (ROC) curves by levels of LAV% were calculated Fig. . We eval1% predicted, FEF25–75/FVC, and a lower FEV1/FVC ratio. This association was preserved even when the same analyses were performed in a subgroup including patients with COPD only or a subgroup including patients with both COPD and with ACO or with BA  and the European Respiratory Society (ERS) guideline23, we believe that the quality of the spirometry was superior. Secondly, we did not assess total expiratory time in our patients with airway obstruction. We acknowledge that vital capacity in many obstructed patients is a function of total expiratory time and variations in expiratory time may have influenced our computations. Thirdly, we did not include healthy persons or patients who have combined pulmonary fibrosis and emphysema24 (CPFE). Future studies, using larger numbers of patients with obstructive lung diseases, should examine the utility of the Obstructive Index for predicting the presence and severity of emphysema, and should further explore the effects of fibrosis on the concavity of the MEFV curve. Fourthly, although we used multivariate analysis to adjust for the effects of the use of two different CT scanners with different slice thicknesses, it cannot be denied that measurements of LAV might have been influenced by the differences between these scanners. However, we also did the same analysis excluding patients who were scanned using the VCT, and we confirmed that the Obstructive Index was still a significant factor associated with LAV% .29. The definition of each obstructive lung disease and smoking status are shown in Supplementary Appendix We retrospectively screened patients who underwent both chest CT and spirometry, on separate occasions within a 3-month period, and included 133 patients who were diagnosed with COPD (N = 65), ACO (N = 15), or BA (N = 53) with varying levels of airflow obstruction  methods published by the Japanese Respiratory Society30. As the variability in an individual’s lung volume and airway size could affect the concavity of the MEFV curve31, we included FEF25–75/FVC32, which is an index of dysanapsis33, to evaluate the curvilinearity of the MEFV curve.Spirometry was performed with an automated electronic spirometer SYSTEM 21 , with the patient in a sitting position; bronchodilators were withheld before spirometry. These tests were conducted and evaluated by well-trained technicians following the statement of the ATS/ERS guideline14.The Obstructive Index was calculated by dividing FVC by the volume-difference between two points of half of the peak expiratory flow on the MEFV curve Fig. 14.12 and Healy’s criteria13: AC, Int, C, and N, , and 30 were examined using a Light Speed VCT™ . Images were obtained at a slice thickness of 2.0 mm (Aquilion) or 1.25 mm (VCT), with a scan time of 400 or 500 milliseconds (Aquilion) or 400 milliseconds (VCT), tube voltage of 120 kV, and with autoexposure. The CT images were reconstructed with a smoothing kernel (FC10 and Standard).1 and the right B8 were measured34.The DICOM formatted data of each patient were used to analyze lung measurements. Using SYNAPSE VINCENT™ ver 4.6 , LAV% at −960 Hounsfield units, divided by the whole-lung volume, and WA% of the right B35 was used to identify the Obstructive Index cut-off values that maximized sensitivity and specificity. P values less than 0.05 were considered significant.All statistical analyses were performed using JMP™ version 14.1.0 for Windows . Differences in characteristics of subjects and QCT measurements among obstructive lung diseases were analyzed using omnibus tests , followed by nonparametric comparisons for all pairs using the Steel-Dwass method. Spearman’s rank correlation was used to evaluate the relationship between QCT measures and spirometric indices. To investigate the factors associated with LAV%, multivariate regression analysis was performed. To determine the optimal cut-off values of the Obstructive Index for predicting the presence of emphysema, we constructed ROC curves and estimated AUC by levels of LAV%. The Youden indexSupplementary Information


The muscle fitness ) of the subjects in Q4 was significantly greater than that of those in Q1 to Q2 (p < 0.001). Subjects with a medium-high MVF ratio  had an odds ratio of 2.103 of ideal CVH metrics after adjusting for age, gender, university, and alcohol intake  1.832 to 2.414; p < 0.001). A lower MVF ratio is associated with worse CVH metrics and a higher prevalence of MetS in early adulthood, supporting the hypothesis that the MVF ratio could be used as a complementary screening tool that could help clinicians identify young adults with unfavorable levels of CVH and metabolic risk.This study aimed to investigate the association between the muscle fitness to visceral fat level (MVF) ratio and the prevalence of metabolic syndrome (MetS) and ideal cardiovascular health (CVH) metrics among college students. A total of 1467 young adults recruited from the FUPRECOL study (Asociación de la Fuerza Prensil con Manifestaciones Tempranas de Riesgo Cardiovascular en Jóvenes y Adultos Colombianos), were categorized into four quartiles based on their MVF ratio. Muscular fitness was assessed using a digital handgrip dynamometer and visceral fat level was determined through bioelectrical impedance analysis. Ideal CVH was assessed, including lifestyle characteristics, anthropometry, blood pressure, and biochemical parameters. The body weight, waist circumference, body mass index (BMI), fat mass, fat mass index, and visceral fat level were significantly higher in subjects in Q1 (lower MVF ratio) than those in Q2, Q3, or Q4 ( Abdominal adiposity, dyslipidemia, elevated blood pressure (BP), and impaired fasting glycemia have been identified as cardiometabolic risk factors associated with cardiovascular disease (CVD), with the fastest rise being seen in Latin-American countries . In ColoResearch assessing CVD risk in college students aged 18–24 has revealed an alarmingly high prevalence of abnormal risk factor profiles , and thiVery few studies have analyzed this ideal CVH in young adults, but these suggest a direct association with markers related to future cardiovascular events such as carotid intima-media thickness  or left Poor diet, obesity, and low physical fitness levels have been shown to be the leading CVD risk factors in Colombian adults, raising concerns about whether an increase in the risk level of these conditions also affects cardiometabolic status ,20. ThesLow muscle fitness and high levels of visceral fat tissue have separately been shown to increase the risk of metabolic disorders and CVD early in life ,22. HoweThis was a secondary analysis of cross-sectional of data from the FUPRECOL study (Asociación de la Fuerza Prensil con Manifestaciones Tempranas de Riesgo Cardiovascular en Jóvenes y Adultos Colombianos), collected between 2014–2017, which aimed to assess changes in lifestyle and CVD during attendance at university (aged 18–30 years) ,24,25. A® BC-420-MA® Tokyo, Japan) with a range of 0 to 200 kg and with an accuracy of within 100 g. Height was measured with a portable stadiometer with a precision of 0.1 mm and a range of 0–2.50 m . Body mass index (BMI) was calculated by using the formula proposed by Quetelet, where BMI = body mass (kg)/height (m2). Body mass index status was evaluated according to the World Health Organization criteria  criteria  [®, Parsippany, NJ, USA) at the waist (at the midpoint between the last rib and the iliac crest). The morphological evaluation process was carried out by a team of professionals  with extensive experience in anthropometric measurement. Anthropometric variables were measured in accordance with the International Society for the Advancement of Kinanthropometry (ISAK) guidelines [After completing another general information questionnaire, participants were instructed to wear light clothing  for the physical exam. Once the subjects were barefoot and in their underwear, their body weight (kg) was measured using an electric scale (Model Tanita0 kg/m2) . Waist cidelines . Two per® BC-420-MA® Tokyo, Japan). This single-frequency foot-to-foot BIA device provides estimated values for BF% by subtracting the weight of fat-free mass from the total body water. The impedance between the two feet was measured while an alternating current (50 kHz and ~200 μA) passed through the lower body [Body fat percentage (BF%), visceral fat level, skeletal muscle mass (kg) and fat mass (kg), were determined for bioelectrical impedance analysis (BIA) by a tetrapolar whole body impedance  . It was performed to measure the participant’s maximum force of handgrip. The grip span of the dynamometer was adjusted to the hand size of the participant. With the elbow in full extension, the participant had to press the dynamometer with the right hand for at least 2 s. The test was then repeated with the left hand, performed twice, and the maximum score for each hand was recorded in kilograms. The average of the maximum scores for both hands was used in analyses. As there is substantial covariance between strength capacity and body mass, and the link between strength and both physical function and chronic health is directly mediated by the proportion of strength relative to body mass, handgrip strength was normalized as grip strength (NGS) per body mass; i.e., (handgrip strength in kg)/(body mass in kg). The reproducibility of our data was R = 0.96. Intra-rater reliability was assessed by determining the intraclass correlation coefficient .Grip strength as a “proxy” measure for muscle fitness and physical health was assessed using an adjustable digital handgrip dynamometer TKK-18 digital Grip-D dynamometer (TakeyData on smoking were collected via self-reported questionnaires (number of cigarettes smoked per day). Ideal smoking status was determined as non-smoker or quit smoking ≥12 months. A standardized questionnaire, the “FANTASTIC” lifestyle , nutrition, tobacco toxins, alcohol, sleep/stress, personality type, insight, career) questionnaire, was used to collect comprehensive information about substance use via a personal interview with participants . The phyA seven-day recall was the dietary assessment tool used to complete the MetDiet adherence survey. The total score was divided into two categories of Mediterranean diet quality: (1) ≤7 points = poor diet quality; and ≥8 points = good diet quality . Participants who had at least ≥8 points were categorized as having an ideal healthy diet, whereas collegiate students with 7 points were classified as having a non-ideal healthy diet.® equipment .For blood measurements, the participants were asked to arrive in a fasting state, abstain from exercise training, caffeine, nicotine, and alcohol 12 h before the clinical examination, and continue their regular medication routines. Capillary blood samples (40 µL) were collected to determine serum biochemical parameters, including fasting glucose and total cholesterol using portable CardiocheckBlood pressure was taken with an automatic monitor  following the recommendations of the European Heart Society  . During the measurement, the participants were seated with their arm supported at the level of the heart. 2, physical active individuals (participants who exercised ≥150 min of moderate activity per week), non-smoking status (either never having smoked or having quit smoking >12 months ago), and consumption of a dietary pattern that promotes ideal CVH. Some modifications of the AHA recommendations were required to evaluate diet. Participants who had at least ≥8 points in the MetDiet questionnaire were categorized as having an ideal healthy diet, whereas collegiate students with 7 points were classified as having a non-ideal healthy diet. The clinical and laboratory parameters were classified as an untreated systolic blood pressure <120 mmHg and diastolic blood pressure <80 mmHg, untreated total cholesterol ≤200 mg/dL, and untreated fasting blood glucose <100 mg/dL.The AHA guidelines  were useFinally, the participants were categorized into 1 of 3 health levels based on the number of CVH metrics in the ideal range that they exhibited: the healthiest level  was defined as having between 5 and 7 metrics in the ideal range; the intermediate level, 3 to 4 metrics; and the unfavorable level, 0 to 2 metrics. These cut-off points have been used in prior international studies .Participants were considered to have a diagnosis of MetS if they had three or more of the following: (1) abdominal obesity ; (2) hypertriglyceridemia (≥150 g/dL); (3) low high density lipoprotein cholesterol, HDL-c ; (4) high blood pressure (systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg); (5) high fasting glucose (≥100 mg/dL). MetS was defined in accordance with the updated harmonized criteria of the IDF .p value of <0.05 was defined as statistically significant.Descriptive characteristics are provided as means, 95% confidence Interval (CI), and percentages. Both statistical (Kolmogorov–Smirnov test) and graphical methods  were used to examine the fit to a normal distribution for each continuous variable. The categorical variables were compared using the Chi squared test. To assess the relationship between muscular fitness and visceral fat ratio and the number of CVH metric, MetS and cardiometabolic risk factors, all study subjects were divided according to quartiles of the MVF ratio (first quartile (Q1 lowest group), second quartile (Q2), third quartile (Q3) and fourth quartile (Q4 highest group). The association between MVF and ideal CVH metrics, as well as with ideal CVH behaviors and factors separately, was assessed by ANCOVA. Finally, logistic regression (odds ratio (OR)) models were employed to compare the prevalence of medium-high MVF  across a number of ideal CVH metrics (poor (0–2 metrics), intermediate (3–4 metrics) and ideal (5–7 metrics)) after adjusting by age, gender, university and alcohol use. CVH metrics were also examined as a continuous variable, considering the OR per a 1-metric-higher overall profile. Thus, association between MVF across ideal CVH behaviors , ideal CVH factors  and 7-metric cardiovascular health in college students were analyzed after adjusted by age, gender, university and alcohol use. Statistical analyses were performed using SPSS-IBM . A 2, a healthy diet, and fasting glucose <100 mg/dL. The proportions of subjects with MetS were 32.3% in Q1, 5.0% in Q2, 1.9% in Q3, and 2.2% in Q4, which were strongly negatively correlated with the MFV ratios in the respective quartiles.As shown in p for trend <0.001). Skeletal muscle mass was significantly lower in Q1 than in Q3, while fat mass, fat mass index, body fat, and visceral fat level were significantly higher in Q1 than in Q2, Q3, or Q4 (p for trend <0.001). The muscle fitness (handgrip and NGS) of the subjects in Q4 was significantly greater than that of the subjects in Q1 to Q2 (p for trend <0.001).There was an increasing trend of body weight, WC, and BMI from Q1 to Q4 (p < 0.001).The aim of this study was to investigate the relationship between MVF ratio and the prevalence of MetS and CVH metrics in a large population of 1467 Colombian young adults. The findings indicate that those subjects positioned in Q4 present better CVH metrics, they show a greater proportion of normal BMI values, are more active, and have a healthier diet, as well as having total cholesterol and glucose levels within the normal range. Consistent with these results, the participants in Q3 to Q4 showed the lowest prevalence of metabolic syndrome (1.9 to 2.2%). In contrast, those subjects who were stratified into Q1 presented, in general, a higher number of altered CVH metrics, together with a higher prevalence of MetS (32.3%). Several studies have shown that subjects who exhibit greater numbers of these healthy lifestyle components (HLCs) are more likely to reach later adulthood with lower blood pressure, cholesterol, and blood glucose levels, and favorable intermediate CVD markers ,36,37.Muscle fitness and visceral adipose tissue have been independently associated with cardiometabolic health; however, to the best of our knowledge, the association between MVF ratio and CVH has not been examined previously. Our results show that each of the indicators of improved cardiovascular health are worse with a decreased MVF ratio. Overall, these findings support the hypothesis that the MVF ratio could be used as a complementary assessment measure that could help clinicians identify young adults with a low CVH score. In addition, adopting a lifestyle that does not result in a decreased MVF ratio is important in order to prevent unfavorable CVH levels and a higher prevalence of MetS in early adulthood. In this sense, our findings indicate that the MVF ratio is a relevant and effective indicator for defining CVH metrics in Colombian young adults. Therefore, these findings add to the literature supporting the importance of healthy lifestyles for maintaining CVH , and redOn the other hand, the relationship between muscle fitness and the prevention of chronic disease in adult populations is widely recognized ,43,44. EIn addition, growing scientific evidence suggests that visceral adipose tissue linked to insulin resistance may play a relevant role in the development of cardiometabolic risk and MetS ,52,53,54To the best of our knowledge, our study is the first to apply the CVH metrics to a population of young adults from Colombia, a country with significant health inequality and which is in a situation of nutritional transition. In this context, applying the 7 AHA cardiovascular health metrics allows us to find out how these measures are useful for assessing risks at the populational level in Colombia. In clinical practice, the results from the AHA measures could be used to help assess baseline cardiovascular health in Colombian young people and identify areas where interventions should focus, especially with regard to policies and environmental changes.Certain limitations of this study also should be addressed. First, the cross-sectional design does not allow us to explain causality. Thus, future prospective analysis is necessary to determine any relationship between the MVF ratio and CVH. In addition, the gold standard for the assessment of visceral fat is magnetic resonance imaging (MRI) and computed tomography (CT). However, in this study, visceral fat measurement was assessed by BIA, which, although it is a highly reliable device, provided an indirect measure. Finally, a potential limitation of our study is that we were not able to adjust for unmeasured variables such as eating time interval and frequency that could affect cardiovascular health, muscle fitness and visceral fat deposition . DespiteA lower MVF ratio is associated with worse CVH metrics and a higher prevalence of MetS in early adulthood. Therefore, MVF ratio is an important and efficient indicator for defining ideal CVH in young adults. Consequently, we propose the use of the MVF ratio as a complementary assessment measure that may be useful for clinicians. Taking into account the fact that CVD and MetS in young adults are considered important public health problems, this study is especially relevant since it provides a novel tool for the early identification of these health issues.