The usage of toxic solvents was low; the chromatographic separation of the medicaments was performed using a C18 column and mobile phases A and B containing 15.0% (v/v) ethanol, 3.0% (m/v) sodium dodecyl sulfate (SDS), and 0.02 mol·L−1 phosphate at pHs 7.0 and 8.0, respectively. The method is simple, selective, and fast, and the analytes were separated in 23.0 min. For extraction, 1000 mL of sample containing 2.0% (v/v) ethanol and 0.002 mol·L−1 citric acid at pH 2.50 was loaded through a 1000 mg of C18 cartridge. The analytes were eluted using 3.0 mL of ethanol, which were evaporated and redissolved in 0.5 mL of mobile phase. Concentration factors better than 1200, except amoxicillin (224), were obtained. The analytical curves were linear (R2 better than 0.992); LOD and LOQ (n=10) presented values in the range of 0.019–0.247 and 0.058–0.752 mg·L−1, respectively. Recoveries of 99% were obtained, and the results are in agreement with those obtained by the comparative methods.Method for extraction and determination of amoxicillin, caffeine, ciprofloxacin, norfloxacin, tetracycline, diclofenac, ibuprofen, nimesulide, levonorgestrel, and 17 For many years, the analysis of environmental contaminants was done to determine the presence of pesticides, air pollutants, petroleum residues, and many other substances designated as conventional pollutants , 2. ReceMany tons of these drugs are produced annually and used in human and veterinary medicine. Generally, the exact amount of pharmaceuticals produced is not published in the literature , but it In Brazil, the population self-medication is common because part of the population does not have access to adequate medical care and has easy access to medicines due to the high number of drugstores, including those with unethical business practices , 10. AftFurthermore, some drugs are used in animal treatment in the rearing of livestock, pigs, and chickens, and the waste generated from these activities has become a major source of environmental contamination due to its use as fertilizer in farmland. Thus, the pharmaceutical compounds are not metabolized and their metabolites can pass into groundwater and eventually to watercourses such as rivers and lakes, affecting aquatic life , 8, 12. The presence of hormones in the environment has been indicated as responsible for causing endocrine disturbances in human and animal organisms and endocrine disruptors , 14. Then-hexane/ethyl acetate/methanol [Due to the growing concern of the presence of antibiotics, anti-inflammatories and hormones in water intended for public supply a number of methodologies devoted to identify and quantify these compounds in various samples that have been proposed. The most common are those involving separation techniques such as gas and liquid chromatography and capillary electrophoresis coupled with several detectors such as MS, UV-Vis, FID, ECD, and others –27 assocmethanol , or moremethanol  and the methanol .As it is not an easy task the development of extraction and determination methods to chemical compounds with very different characteristics, few methods permit the determination of the analytes present in this paper in a single run. Thus, environmental researchers and laboratories dedicated to the analysis of drugs should use several methods for the determination of drugs from different classes in environmental samples, which increase the cost and time of the analysis. The exception is the method based on MS detector that can furnish adequate results to water analysis of emergent pollutants, but it should be considered that a lot of laboratories dedicated to routine water analysis did not have mass spectrometer, mainly due to the cost to acquire the equipment or lack of skilled professionals capable of properly using the equipment.α-ethynylestradiol and levonorgestrel), and caffeine in water samples, providing the laboratories dedicated to water analysis with a tool able to determine the more representative pharmaceuticals from classes of antibiotics, anti-inflammatories, and hormones used in Brazil in a single chromatographic run and using a simple HPLC-UV; a common equipment in a number of laboratories devoted to contaminant analysis, contributing to environmental researchers involving emerging pollutants. The task can be done exploiting the micellar chromatography as the surfactant can modify the C18 and the aqueous phase, increasing the possibilities of interactions of the analytes with both phases, permitting the separation of substances with different polarities in the same chromatographic run. The selected compounds are considered toxic, are among the most used in Brazil, present biological activity, and their presence in the aquatic environment has already been demonstrated, which justify their determination.Thus, to fill this gap, in the present work is proposed a robust, simple, and green HPLC-UV method associated with SPE extraction for the determination of antibiotics , anti-inflammatories , hormones ; the aqueous solutions were prepared using deionized water and were ultrasonically degassed and vacuum-filtered through a cellulose acetate membrane of 0.45 n-propanol or n-butanol of HPLC-grade , anhydrous dibasic sodium phosphate (Na2HPO4), SDS, or CTAB (cetyl trimethyl ammonium bromide), or SDBS (sodium dodecyl benzene sulfonate), or Triton X-100 (polyoxyethylene (9-10) p-phenol tertoctyl)  at different concentrations and pH values were tested as mobile phase. The main mobile phases containing 3.0% (m/v) SDS, 20.0 mmol·L−1 Na2HPO4, and 15.0% (v/v) ethanol at pH 7.0 (phase A) and pH 8.0 (phase B) were prepared by dissolving 3.046 g of SDS, 0.284 g of Na2HPO4, and 15.0 mL of ethanol at approximately 70.0 mL of water and leaving the solution under stirring until complete homogenization. Later, the pH was adjusted by adding HCl or NaOH 1.0 mol·L−1, and the final volume was made up to 100.0 mL in a volumetric flask. All mobile phases used in this work were prepared following the same procedures and just modifying the percentages of reagents or changing the organic solvent to n-propanol or n-butanol or the surfactant.Solutions containing ethanol or α-ethynylestradiol, levonorgestrel, and caffeine were supplied by Sigma-Aldrich , and 20.0 mg·L−1 stock solutions were prepared by dissolving 1.0 mg of each drug in the mobile phase A, the solution was submitted to sonication for 3.0 min to ensure complete dissolution of analytes, and the volume was completed to 50.0 mL in a volumetric flask. The solutions were stored in amber flasks, protected from light, and were frozen. Just before analysis, the solutions were left to attain thermal equilibrium with the room temperature.Amoxicillin, ciprofloxacin, norfloxacin, tetracycline, diclofenac, ibuprofen, nimesulide, 17−1 of each drug standard solution was prepared in the mobile phase A and divided into two portions of 25.0 mL. The first set was frozen and protected from light, and the second set was kept at 25°C and immediately used to obtain the UV-Vis spectra (200 to 600 nm). The procedure was repeated each 60 min for 3 h during the first day and then the solution was stored in the refrigerator at 10°C. Later, the solutions (two sets) were analyzed every day during 4 days.To verify the stability of each drug in the mobile phase and the wavelength to monitor them, 50 mL of 10.0 mg·LA total of 7 river water samples were collected from rivers flowing in the center of Maringá  in zones with different population densities and industrial activities. Samples 1–3 were collected from the south of Maringá in Moscados stream (ca. 2.9 km from its source in the Inga Park), Cleópatra stream , and Borba Gato stream , respectively. Samples 4–6 were collected in the north of Maringá in Mandacarú stream (ca. 2.0 km from the source), Morangueiro stream (ca. 4.0 km from the source), and Maringá stream (ca. 2.0 km from the search), respectively; sample 7 was tap water collected in the analytical chemistry laboratory at Maringá State University.µm membrane; then, to 1.0 L of each sample was added 2.0% (v/v) ethanol and 2.0 mmol·L−1 citric acid, and the pH was adjusted to 2.5 with HCl 1.0 mol·L−1.Before sample collection, each bottle was prerinsed with the sample for three times. The samples were sent in boxes packed with ice to the laboratory at Maringá State University. Immediately upon reception, the samples were vacuum filtered through Millipore 0.45 μm membrane and to 1.0 L of each sample was added 2.0% (v/v) ethanol and 2.0 mmol·L−1 citric acid, and the pH was adjusted to 2.5 with HCl 1.0 mol·L−1. Later, the samples were submitted to the following extraction procedure: before the extraction, the C18 stationary phase was preconditioned by passing 5 mL of methanol, 5 mL of pure water, and 5 mL of an aqueous solution containing 2.0% (v/v) ethanol and 2.0 mmol·L−1 citric acid at pH 2.5; then, 1000 mL of each sample was passed through the cartridge at a flow rate of 7.0 mL·min−1. The solid phase was dried under vacuum for 30 s, and the analytes were eluted using 3.0 mL of ethanol at 1.0 mL·min−1. The sample was filtered through a teflon membrane, evaporated, and redissolved in 0.5 mL with the mobile phase A and injected into the chromatograph. To obtain the extraction procedure able to be applied to the ten analytes, the following variables were tested: pH (2.0–8.7), sample (1.0–10.0 mL·min−1) and eluent (0.5–3.0 mL·min−1) flow rates, sample (25.0–1000 mL) and eluent (1.0–10 mL) volumes, amount of stationary phase (500 and 1000 mg), and chemical nature of the eluent .For sampling, 4 L of each water sample was collected according to the standard protocol established by the Water Resources Company Management , which cμL Rheodyne Model 8096, UV-Vis photodiode array detector model Surveyor PDA with quartz cell with optical path of 5.0 cm, ChromQuest software version 4.2  for acquisition and signal recording and a column RP-18 ODS off base 250 × 4.6 mm (id) equipped with a guard column RP-18 ODS 10 × 4.0 mm (id) both with particles of 5-micron pore size of 100 Å and carbon content of 15.5%.The chromatographic separation was performed using an HPLC from Thermo Electron Corporation  containing quaternary pump model Surveyor LC Plus, manual injector valve of 20 −1. The initial conditioning of the stationary phase was performed by passing mobile phase A through the column for 20.0 min at a flow rate of 1.0 mL·min−1. After standard/sample injection (20.0 μL), the separation process was carried out as follows: 0.0–2.0 min with 100.0% of phase A with the instantaneous change to the 100.0% of phase B and keeping it until 25 min always at a flow rate of 1.0 mL·min−1. The temperature was fixed at 25°C, and the analytes were monitored at 220 , 240 (levonorgestrel), and 280 nm (ciprofloxacin), simultaneously. After each analysis, the column was reconditioned for 10.0 min using phase A at a flow rate of 1.0 mL·min−1.Before the first and after the last injection of the day, the column was cleaned with ultrapure water for 30.0 min at a flow rate of 0.5 mL·minn-butanol, n-propanol, and ethanol), SDS concentration ), and flow rate . Therefore, a factorial design experiment 23 was carried out in duplicate in order to optimize the separation conditions and the contribution of the variables  in the chromatographic separation , percentage of the organic modifier in the mobile phase ), nature of the organic modifier (paration .y) versus concentration of the analytes in the following ranges: amoxicillin, ciprofloxacin, diclofenac, ibuprofen, tetracycline, levonorgestrel, nimesulide, and norfloxacin from 0.1 up to 25.0 mg·L−1; 17α-ethynylestradiol from 0.5 up to 25.0 mg·L−1; and caffeine from 0.08 up to 25.0 mg·L−1. The calibration curves were used to determine the analyte concentrations in the samples and in the blank with and without spiking.For quantitative determination of pharmaceuticals and caffeine in the water samples, calibration curves were plotted using the peak area (n=10) were estimated using the signal-to-noise ratio of 3.3 and 10.0, respectively [α-ethynylestradiol [The LOD and LOQ  by the proposed and by comparative methods during five consecutive days (n=5).Sample recovery tests were done spiking the samples with the following: 5.0 λmax = 240 nm) and ciprofloxacin (λmax = 280 nm) that presented low molar absorptivity at 220 nm. For this reason, the signals can be monitored at 220 nm by laboratories that do not have chromatograph with PDA detector, but the sensitivity to levonorgestrel and ciprofloxacin will be poor.The UV-Vis spectra of analytes showed electromagnetic radiation absorption in the region between 200 and 600 nm with higher intensity in the UV region. It is observed that signal overlaps, which makes it difficult for the simultaneous determination of the analytes without prior separation. It was decided to monitor the analytical signals at 220, 240, and 280 nm, because the molar absorptivity is high to the majority of the analytes and the mobile phase absorption is low; the exceptions are levonorgestrel . Variations in the citric acid concentration  did not change the extraction efficiencies; thus, 2.0 mmol·L−1 of citric acid was maintained.A flow rate of 7.0 mL·min−1 were associated with extraction sample volumes of 25, 50, 100, 500, and 1000 mL , it was noted an improvement in the extraction with the increase of sample volume; however, for the more polar analytes , a reduction in the recovery percentages was observed. For sample volume of 500 mL, the best recoveries for most analytes were obtained with values higher than 85%, except for amoxicillin (15.3%); however, in order to obtain high concentration factor to levonorgestrel and 17α-ethynylestradiol hormones, that could be present at very low concentrations in the samples, the condition of 1000 mL was selected, even with a reduction in the extraction efficiency to amoxicillin, tetracycline, caffeine, and diclofenac.The loading sample volume should be as high as possible to get high concentration factors. When solutions of the analytes in the concentrations of 1.0, 0.5, 0.25, 0.05, and 0.025 mg·LThe amount of solid phase in the cartridge was also studied, and it was noted that reducing the amount of solid phase led to lower extraction efficiency to tetracycline and amoxicillin. Thus, the cartridge with 1000 mg of C18 was selected.−1; then, the eluent flow rate was fixed at 1.0 mL·min−1.In order to achieve the highest possible concentration factor with minimum use of organic solvents , the volume of the solvent was varied  and it was not observed significant variations in the analytes recoveries when the eluent was methanol or ethanol and the eluent volume was varied from 3.0 to 10.0 mL; thus, 3.0 mL of ethanol was elected as the better condition due to its low toxicity. To acetone and acetonitrile, the recoveries were low to all analytes, mainly to tetracycline, norfloxacin, and ethynylestradiol. The eluent flow rate was varied, and it was observed a decrease in the extraction efficiencies for all the analytes to flow rates higher than 1.0 mL·min−1 citric acid at pH 2.50, C18 cartridge with 1000 mg of the solid phase, and flow rate of 7.0 mL·min−1. After analytes' retention, the adsorbent was dried under vacuum 30 s and the analytes were eluted with only 3.0 mL of ethanol at a flow rate of 1.0 mL·min−1. The solvent was evaporated, and the samples were redissolved in 0.5 mL of a solution with 15.0% (v/v) ethanol, 3.0% (m/v) SDS, and 20.0 mmol·L−1 phosphate buffer at pH 7.0 (mobile phase A). Under these conditions, it was yielded recovery percentage values of higher than 95%, except to tetracycline (64.3%) and caffeine (66.0%), and concentration factors higher than 1200, except to amoxicillin (224).Thus, the final extraction conditions for simultaneous extraction and concentration of the ten analytes were as follows: 1000 mL of the sample containing 2.0% (v/v) ethanol, 2.0 mmol·L−1 in water), which permits to use lower surfactant concentration. Furthermore, SDS presents low light absorption and scattering effect in the UV region, where the analytes should be monitored, allowing to get lower baseline signals and better sensitivity.The developed chromatographic method should be as green as possible, and taking into account the different polarities of the analytes, it was decided to exploit the micellar chromatography , 33 due n-propanol or n-butanol with water and SDS. It was observed that solutions containing 15% of ethanol or butanol or n-propanol together with at least 3.0% SDS and 20 mmol·L−1 of phosphate buffer at pH 7.0 were able to solubilize all the analytes as well as elute them from the C18 column , 17α-ethynylestradiol , and levonorgestrel  did not show significant variation in their retention time as these compounds are neutral in that pH range; the different retention time periods were attributed to their different hydrophobicities. On the other hand, the density of negative charges increased with pH to diclofenac , ibuprofen , and nimesulide , causing large reduction in their retention time due to the high affinity of the charged analytes by the micellar aqueous mobile phase and their repulsion by the negatively charged groups of the surfactants adsorbed on the stationary phase.pH is an important variable to be studied when the analytes have ionizable groups, as variations in pH can promote changes in the solubility and in the ionic interactions of analytes with the micellar medium and with the stationary phase. When the pH was varied from 6.0 up to 8.0 , caffeinKa's 3.94, 7.62, and 9.19), ciprofloxacin , and norfloxacin  have positive charge densities in the tested pH interval and they are the most soluble molecules in aqueous medium, but their electrostatic interaction with the negative part of the surfactant chain attached to C18 phase increased their retention time to values to those obtained with the hydrophobic compounds. The effect also caused peak broadening, and at pH 5.0, it was not possible to identify the tetracycline peak due to excessive molecule retention. Amoxicillin  is also an amphoteric molecule and with the pH reduction from 7.0 to 6.0  to 100% of phase B (same composition of phase A at pH 8.0). The phase with pH 6.0 was not chosen because some analytes were highly retained in the stationary phase under this condition .It was observed that as sooner B phase was introduced, lower analytes' retention time and better analytes separation were obtained, especially to nimesulide (peak 8), ciprofloxacin (peak 9), norfloxacin (peak 10), and levonorgestrel (peak 7) . The chr3  promotes an increase in the analyte retention time and in time of analysis  and levonorgestrel (peak 7) by C18 phase, causing the coelution of 17α-ethynylestradiol (peak 6) with nimesulide (peak 8). The compounds 3, 4, 9, 10, and 7 increased their affinities by the stationary phase due to electrostatic interactions between analytes, partially positive charged, and the negative ionized sulfonic acid groups adsorbed on C18 phase. This factor improved the separation between nimesulide (peak 8) and ciprofloxacin (peak 9) and ibuprofen (peak 9) and norfloxacin (peak 10) (The reduction in SDS concentration from 3.0% to 2.5% (m/v), experiments 4 and 2 , also inpeak 10) .−1), and therefore Na+ concentration, reduced the affinity of ibuprofen (peak 5), nimesulide (peak 8), norfloxacin (peak 10), and ciprofloxacin (peak 9) by the stationary phase due to two reasons. First, because ion-exchange competition between Na+ and the positively charged analytes by the ionized sulfonic acid groups absorbed on C18 phase and, second, due to the partial stabilization of the analytes charged by the phosphate ions. Thus, the chromatographic resolutions between diclofenac (peak 4) and ibuprofen (peak 5) and 17α-ethynylestradiol (peak 6) and nimesulide (peak 8) decreased from 1.68 and 2.10 to 0.51 and 1.42, respectively  and nimesulide (peak 8).The association of lower percentages of SDS and ethanol led to higher time of analysis (30 min), and despite an improvement in the resolution between peaks 6 and 10, the resolution between diclofenac (peak 4) and ibuprofen (peak 5) decreased . FurtherFrom the main effects , it was The second-order  and third-order (ABC) interaction effects indicated intense variable interactions , showingConsidering all variable effects, it was concluded that experiment 4 presented the best characteristics; under this condition, it was possible to separate the ten analytes in only 23.0 min always with chromatographic resolution better than 1.45.−1, the chromatographic resolutions worsened, and the separation between tetracycline and diclofenac, diclofenac and ibuprofen, and ciprofloxacin and norfloxacin was poor, then 1.0 mL·min−1 was selected. Thus, the final separation conditions were as follows: mobile phase with 15.0% (v/v) ethanol, 3.0% (m/v) SDS, 20.0 mmol·L−1 phosphate buffer at pH 7.0 to phase A and 8.0 to phase B; maintaining 100% of phase A until 2.0 min with abrupt change to 100% of phase B; flow rate of 1.0 mL·min−1; 25°C; injected volume of 20 μL; monitoring the signals at 220, 240, and 280 nm; and time of analysis of 23.0 min  and quantification (LOQ), n=10, considering the signal/noise ratio of 3.30 and 10.0 times, respectively, were estimated between 0.019 and 0.247 mg·L−1 and 0.058 and 0.752 mg·L−1 for caffeine and 17α-ethynylestradiol, respectively.The analytical curves for the ten analytes in the concentration range of 0.10 up to 25.0 mg·LWhen the proposed method was applied to the analysis of the 7 water samples, the standard deviation was ca. 2% and the peaks showed to be free of interferences. The results furnished by the proposed method are in agreement with those obtained by the comparative chromatographic methods . Furthern=3) and interday (n=5) precision of 1.11% and 2.30%, respectively, whereas the comparative methods presented analytes average recovery of 98.64% and intraday (n=3) and interday (n=5) precision of 1.34% and 2.97%, respectively, indicating that the proposed method furnished results similar to those obtained by the comparative methods, but with the difference that it was necessary to carry out more than one method for the determination of the ten analytes.Considering all the obtained results, the proposed method presented analytes average recovery of 99.12% and intraday  ethanol, 3.0% (m/v) SDS, and 20.0 mmol·L−1 of phosphate at pHs 7.0 and 8.0) and to extract the analytes in only one procedure using a C18 cartridge and ethanol as eluent. The method was selective, robust, simple, fast, and inexpensive, presented adequate LOD and LOQ, and it can be an important tool for laboratories dedicated to the analysis of emerging pollutants that do not have MS detector.The proposed extraction and determination method demonstrated to be able to do the extraction and determination of tetracycline, ibuprofen, 17


Recent obesity studies have reported that the rising trend in obesity has stabilized or leveled off. Our study aimed to update estimates of the recent prevalence trend in obesity based on the Korean National Health and Nutrition Examination Survey 1998–2014.2 or lower, 23 kg/m2 or higher, and 25 kg/m2 or higher, respectively.A total of 66,663 subjects were included and defined as being either underweight, overweight, or obese, in accordance with a BMI of 18.5 kg/mp for trend = 0.04, β = 0.003) and 4.7%, 3.3%, 3.4%, 3.3%, 2.7%, and 2.6%, respectively, in women . Also for KNHANES I through VI, the respective prevalence of overweight/obesity was 50.3%, 57.2%, 62.5%, 62.3%, 61.4%, and 61.3% in men  and 48.3%, 50.3%, 50.0%, 47.8%, 47.0%, and 45.3% in women , respectively.The prevalence of underweight in KNHANES I through VI surveys was 5.4%, 6.1%, 5.8%, 6.5%, 7.6%, and 7.5%, respectively, in men (The obesity occurrence in men was trending upward with respect to overweight/obesity and for grade 1 and 2 obesity, but not for abdominal obesity. However, the obesity trends in women were leveling off from overweight/obesity, grade 1 obesity, and abdominal obesity measures. Further studies are required with data on muscle mass and adiposity for effective obesity control policies. •We conducted this study using the KNHANES from 1998 to 2014.•Male obesity showed upward trends in overweight/obesity and grade 1 and 2 obesity.•Overweight/obesity, grade 1, and abdominal obesity were leveling off in females.•The prevalence of underweight decreased in men but increased in women. Obesity is disease of global epidemic proportions that has increased during the recent decades.Recent studies have reported that the rising trends of obesity have stabilized or leveled off in some European countries and China.The KNHANES is a cross-sectional, nationally representative survey conducted by the Korean Ministry of Health and Welfare and has been performed in 6 phases: the KNHANES phase I (1998), II (2001), III (2005), IV (2007–2009), V (2010–2012), and VI (2013–2015) studies. As the most recent data (from 2015) was unavailable, the data from 1998 to 2014 are included in this study. The KNHANES is composed of a Health Interview Survey, a Health Behavior Survey, a Health Examination Survey, and a Nutrition Survey. Households as sampling units were stratified, and the information was collected through a multistage probability-based sampling design based on sex, age, and geographic area, using household registries. On completion of this survey, participants provided written informed consent for use of their data for further analyses and were given the right to refuse to participate, in accordance with the National Health Enhancement Act.Of the 177,056 participants in the KNHANES I-VI, we excluded those younger than 20 years and those who did not have information for body mass index (BMI) . After these exclusions, a total of 66,663 participants  were included in the final analysis. The Institutional Review Board (IRB) of the Korea Centers for Disease Control and Prevention approved this study.2). According to the Asia-Pacific regional guidelines of the WHO and International Obesity Task Force, we defined the cutoff points for being underweight, overweight, and obesity as a BMI of 18.5 kg/m2 or lower, 23 kg/m2 or higher, and 25 kg/m2 or higher, respectively.2) and grade 2 (BMI ≥30 kg/m2), which were not mutually exclusive. Abdominal obesity was defined as waist circumference ≥90 cm for men and ≥85 cm for women using the cut-off points in Korea.Physical examinations were performed by a trained medical staff, following standardized procedures. Body weight, height, and waist circumference were measured to the nearest 0.1 kg and 0.1 cm, respectively, with participants wearing light indoor clothing without shoes. Waist circumference was checked at the midpoint between the lower border of the rib cage and the iliac crest. BMI was calculated as the ratio of weight in kilograms to height in meters squared  and one-way analysis of variance was used to compare mean values for continuous variables, such as age, BMI, and waist circumference, across KNHANES phases. Linear regression analyses for P trends and logistic regression analyses for odds ratios (ORs) were used. All analyses were conducted using SAS statistical software . All statistical tests were two-sided, and statistical significance was determined at The 66,663 participants included in this study were distributed as follows: 7962 from KNHANES I, 6572 from KNHANES II, 5462 from KNHANES III, 17,126 from KNHANES IV, from 18,255 KNHANES V, and 11,286 from KNHANES VI .Table 1Sp for trend<0.001, β = 0.12), whereas it decreased in women . Mean BMI increased in men of all age groups , whereas it only decreased in the middle-aged group among women . Waist circumference in men showed no significant change overall , as waist circumference increased in the youngest group but decreased for the middle-aged group across the KNHANES phases . However, waist circumference in women decreased overall , as the waist circumference of the middle-aged and the oldest groups decreased across the KNHANES phases .p for trend = 0.04, β = 0.003) and 4.7%, 3.3%, 3.4%, 3.3%, 2.7%, and 2.6% in women . The underweight prevalence trend in men decreased with the KNHANES phase , while that in women increased . The KNHANES I through VI prevalence of overweight/obesity was 50.3%, 57.2%, 62.5%, 62.3%, 61.4%, and 61.3% in men  and 48.3%, 50.3%, 50.0%, 47.8%, 47.0%, and 45.3% in women , respectively. The prevalence trend of overweight/obesity in men increased , whereas that in women decreased . Trends in the prevalence of grade 1 obesity in both sexes were similar with that of overweight/obesity. The prevalence of grade 2 obesity in both sexes increased time-dependently , and recently, a decrease was observed in women alone. Also, the respective prevalence of abdominal obesity was 20.4%, 24.2%, 24.6%, 25.0%, 24.7%, and 24.1% in men  and 24.0%, 24.9%, 23.5%, 24.1%, 22.7%, and 19.8% in women . Male abdominal obesity showed no significant changes across the KNHANES phases, but female abdominal obesity showed a decrease .The prevalence of being underweight, overweight/obese, grade 1 and grade 2 obesity, or abdominal obesity is presented in p for trend<0.01, β = −0.01), but that of overweight/obesity increased  for the oldest male group. The prevalence of grade 1 and 2 obesity and abdominal obesity increased in the youngest male group . The prevalence of grade 2 obesity in the middle-aged men increased , while that of abdominal obesity decreased . The prevalence of being underweight in the youngest and middle-aged women increased , whereas that in the oldest women decreased . Female overweight/obesity prevalence in the youngest and middle-aged group leveled off, especially for the KNHANES II group . However, the prevalence of grade 2 obesity tended to increase in the youngest women , but recently, a decreasing pattern was observed during the 2010–2012 year of the survey. There was decrease in the prevalence of abdominal obesity for the middle-aged and oldest women .Detailed information on the prevalence of underweight, overweight/obesity, grade 1 and 2 obesity, and abdominal obesity according to age group and sex is shown in p for trend = 0.007, p < 0.001, and p < 0.001, respectively), while those for being underweight were significantly decreased (p for trend<0.001). In contrast, the odds for being underweight in women were statistically higher in KNHANES II–VI than in KNHANES I and tended to increase with the KNHANES phases (p for trend = 0.0001). Women showed a tendency of decreased prevalence of overweight/obesity, grade 1 obesity, and abdominal obesity , but an increased prevalence in grade 2 obesity (p for trend = 0.014).The trends in underweight, overweight/obese, grade 1 and 2 obesity, and abdominal obesity during the 1998–2014 survey are presented as ORs (95% CIs), and these indicated an estimated increase in the odds for obesity occurrence after adjusting for age group . CompareAnalyses of KNHANES data for 1998–2014 showed sex differences in trends in underweight, overweight/obesity, grade 1 and 2 obesity, and abdominal obesity prevalence among the Korean adults. The prevalence of underweight in men decreased, but that in women increased with the KNHANES phases. It is of note that the association between underweight and health outcomes is still controversial. Jee et al reported that underweight men and women had higher mortality;From the KNHANES data, the trends in overweight/obesity and grade 1 and 2 obesity were in an upward trajectory in men. In contrast, the female trends were downward pointing in overweight/obesity and grade 1 obesity categories, and also recently decreasing in grade 2 obesity. There was a decrease in prevalence of abdominal obesity in women, but no significant change was seen in men.We compared our present results with those of similar previous studies. Kang et al reported an upward trend of overweight/obesity and obesity in men and a slightly decreasing trend of overweight/obesity in women.These changes in the obesity epidemic in Korea might be closely related to changes in healthy behavior, although the direction of change is controversial. According to the Korea Health Statistics, which is published by the Korea Centers for Disease Control and Prevention, the trend of daily intake calorie for years 1998, 2001, 2005, 2007, 2008, 2009, 2010, 2011, 2012, and 2013 year were as follows: 2152 kcal, 2107 kcal, 2214 kcal, 2107 kcal, 2146 kcal, 2391 kcal, 2360 kcal, 2310 kcal, and 2386 kcal, respectively, for men, and 1729 kcal, 1713 kcal, 1742 kcal, and 1549 kcal, 1583 kcal, 1590 kcal, 1733 kcal, 1699 kcal, 1682 kcal, and 1779 kcal, respectively, for women.Interestingly, our study shows that the rate of obesity has increased overall, but that for abdominal obesity has reached a plateau in men. This discrepancy can be explained by an increase in BMI in men, which is not due to an increase in adiposity but from an increase in muscle mass. Several studies reported that the exercise rate in men has increased.Our study has several limitations that should be considered when interpreting the results. First, this was a cross-sectional study, which could not identify cause-and-effect relationships. Second, those who had missing anthropometric data were excluded in this study, so selection bias could not be excluded, as overweight or obese women tend to avoid anthropometric measurements. Third, overweight and obesity were defined based on BMI numbers, which is calculated from body weight and height. Thus, we cannot distinguish whether these trends resulted from body fat or muscle mass changes or both. Therefore, misclassified adiposity may lead to overestimation of being fat in muscular participants, as BMI is strongly correlated with both muscle and fat mass.Despite its potential limitations, this study has several strengths. First, to represent the general Korean population aged 20 years or older, we applied sampling weights to all analyses, and adjustment for age and sex was applied to the Korean population distribution in the year 2005. Second, we showed the prevalence trends in abdominal obesity based on waist circumference, in addition to measures of underweight, overweight/obese, and extent of obesity, as calculated by BMI. Thus, detailed information about the obesity pattern was gathered. Third, this is the most recent study on Korean obesity and the first to include data from 2014.In summary, the trend in obesity prevalence increased without a change in abdominal obesity in men, and overweight/obesity and abdominal obesity prevalence decreased in women during the 17-year period.Further studies on trends of muscle mass and adiposity are needed for establishment of effective obesity control policies through targeted interventions.None declared.


After publication of the original article , it cameIn addition, there were errors in the EQ‑5D and QALYs sub-section of the Results.In the second paragraph, the following sentence has been amended to change the post-CBT value from 0.199 to 0.189: “The mean changes in the EQ-5D index from baseline were 0.143 at mid-CBT and 0.189 at post-CBT.”In the same paragraph, the final sentence has been amended to correct the change in QALYs from 0.178 to 0.167: “Under the best conditions—namely, that EQ-5D maintained a high level at 12 months—the change in QALYs from baseline was estimated as 0.167 QALYs. Therefore, between 0.102 and 0.167 QALYs were gained per 1 year.”Finally, in the last paragraph of the sub-section, the JPY and US$ values have been corrected: “Using these values to convert the change in QALYs per 1 year into WTP values, we obtained values of JPY 510,000–835,000 (Japan) and US$ 6320–10,350 (United States). Because we provided patients 16 sessions of CBT, we estimated that patients would spend JPY 31,800–52,100 (Japan) and US$ 395–647 (US) per one session (50 min) of CBT.”


Participants were asked to select a color that they associated with an odor on two separate occasions (to test for consistency), and finally to label the odors. We found the hunter-gatherer Maniq showed few, if any, consistent or accurate odor–color associations. More importantly, we found the types of descriptors used to name the smells were related to the odor–color associations. When people used abstract smell terms to describe odors, they were less likely to choose a color match, but when they described an odor with a source-based term, their color choices more accurately reflected the odor source, particularly when the odor source was named correctly . This suggests language is an important factor in odor–color cross-modal associations.People appear to have systematic associations between odors and colors. Previous research has emphasized the perceptual nature of these associations, but little attention has been paid to what role language might play. It is possible odor–color associations arise through a process of labeling; that is, participants select a descriptor for an odor and then choose a color accordingly . If correct, this would predict odor–color associations would differ as odor descriptions differ. We compared speakers of Dutch  contains supplementary material, which is available to authorized users. There appears to be a tight link between odors and colors. Odor identification is easier when an appropriate color is presented together with the odor  . If, however, odor–color associations are based on structural or statistical associations alone, we should see comparable odor–color associations across cultures .When Jahai speakers are asked to name odors in controlled experimental settings, they rarely use source-based descriptions; they rely on these abstract smell terms instead. In fact, they describe smells in a comparable manner to colors, unlike English speakers , settlement patterns, economic organization , schooling (literate vs. nonliterate), and so forth. Thus, any differences in cross-modal associations between these two groups could, in principle, be difficult to interpret. We therefore also included a third group of Thai speakers in our study. Like the Maniq, the Thai appear to have a more elaborate smell lexicon than Dutch speakers (see the Results section); but unlike the Maniq, the Thai tested here also live in an urban environment, with a modern postindustrial society, widespread literacy, and so forth. The Thai group, then, presents an additional test case for the hypothesis that abstract smell terminology should lead to less consistent and precise color choices to odors than source-based odor descriptors.Mage = 41.4 years, range = 20–65), 24 Thai , and 24 Dutch . The Maniq sample was smaller due to practical restrictions: There are only about 300 Maniq speakers, living in small nomadic groups scattered across a large area, which makes it hard to recruit participants. All participants gave informed consent and were paid for participation  according to local rates.We recruited 59 participants: 11 Maniq . The interval between two consecutive odors was the amount of time it took for the experimenter to exchange the bottle for a new one (approx. 25 s).The participants completed two tasks. In the odor-naming task, participants were presented with the odors again and were asked to name each one and rate its familiarity on a 3-point scale . Familiarity was restricted to a 3-point scale to facilitate the task for the Maniq, who were not used to scales. The responses were audio-recorded for later transcription and coding.In the Participants completed the odor–color task twice, separated by 2 h on average (range = 1–4 h), followed later by the odor-naming task. The order of the 15 odors was fixed within a task but varied across tasks.Before examining the possible role language plays in odor–color associations, we first tested whether people consistently mapped the colors to odors. We looked at within-participant consistency, within-language consistency, and cross-language consistency. Thereafter, we examined the role of odor-naming strategies on odor–color mappings.One Maniq participant completed the odor–color task only once and was therefore excluded. The naming data for one Dutch participant were not available, so that participant was not included in the later analyses involving naming data.N = 360, observed proportion = .18, p < .001; Dutch: N = 360, observed proportion = .17, p < .001), but Maniq speakers were not   would be mentclass2pt{minimIf odor–color mappings were not consistent within a language, the expected chance within each session to choose a color chip would be mentclass2pt{minimThere were few consistencies across languages . The descriptors coded as “other” included references to taste , intensity , touch , and evaluations . Following Majid and Burenhult (χ2(6) = 176.83, p < .001, Cramer’s V = 0.32, and in all responses, χ2(6) = 239.68, p < .001, Cramer’s V = 0.32.To investigate the possible role of language on odor–color associations, we first examined the odor descriptions in the odor-naming task, focusing on the main content descriptors. Modifiers and comments regarding the familiarity of the odor were excluded. The main responses were coded as “abstract” smell terms, “source-based” terms, or “other”. Abstract smell terms are not derived from an existing name of an odor source and are dedicated to smell quality , Type of Odor Description , and Familiarity Rating , with participants and items included as random effects. “Other” descriptions were not considered in this model, since no specific prediction could be made for these responses. The dependent variables were (1) color consistency, in which a color choice was considered consistent if the same chip was chosen in both sessions, and inconsistent otherwise; and (2) color match, in which a color was considered a match if it reflected the actual color of the odor source in at least one of the sessions . Three of the authors separately determined which color chips constituted possible matches. Disagreements were resolved by discussion.β = 0.87, SE = 0.28, z = 3.15, p = .002, confirming the results from the binomial tests. Consistency was also higher for more familiar stimuli, β = 0.54, SE = 0.19, z = 2.91, p = .004. The type of description did not predict consistency, however, β = 0.39, SE = 0.35, z = 1.10, p = .27 .The Thai (18.5 %) and Dutch (18.5 %) were more consistent in their odor–color associations than the Maniq (1.5 %), β = 1.06, SE = 0.30, z = 3.58, p < .001. We also observed a main effect of language, with Thai (36.3 %) and Dutch (37.7 %) participants selecting more matching colors than the Maniq (9.2 %), β = 0.81, SE = 0.20, z = 4.10, p < .001. Familiarity did not predict an odor–color match, β = 0.27, SE = 0.15, z = 1.78, p = .076.The pattern was different for color matches. Here the type of response significantly predicted the likelihood of a veridical color match. More color matches were found for odors described with a source-based term (35.5 %) than for those described with an abstract term (22.6 %), β = 0.65, SE = 0.31, z = 2.10, p = .036. Similarly, participants chose more matching colors to odors when their response was correct (63.1 %) than when it was incorrect (25.1 %), β = 1.36, SE = 0.28, z = 4.82, p < .001.If odor–color associations are determined by a label, we might predict correctly identified sources would have more correct and consistent matches than incorrectly identified sources. Since “correctness” can only be determined for source-based descriptions, we ran a separate analysis on the source-based descriptions with the fixed factors Language , Correctness of Odor Description , and Familiarity Rating , controlling for random subject and item effects. The dependent variables again were (1) color consistency, that is, whether the same color chip was chosen in both sessions, and (2) color match, that is, whether the color choice reflected the actual color of the odor source. Participants were more consistent when their response was correct (27.0 %) than when it was incorrect (11.6 %), Describing odors is difficult , and more consistent, when the odor source was named correctly. This suggests an important strategy for assigning colors to odors is via language. A previous study Below is the link to the electronic supplementary material.


In pigs, gut bacteria have been shown to play important roles in nutritional, physiological, and immunological processes in the host. However, the contribution of their metagenomes or part of them, which are normally reflected by fragments of 16S rRNA-encoding genes, has yet to be fully investigated.Clostridium, Escherichia, Bacteroides, Prevotella, Ruminococcus, Fusobacterium, Campylobacter, Eubacterium, and Lactobacillus. Two enterotypes were identified at each time point. However, only enterotypes at week 15 and off-test were significantly associated with BF. We report herein two novel findings: (i) alpha diversity and operational taxonomic unit (OTU) richness were moderately heritable at week 15, h2 of 0.15 ± 0.06 to 0.16 ± 0.07 and 0.23 ± 0.09 to 0.26 ± 0.08, respectively, as well as at off-test, h2 of 0.20 ± 0.09 to 0.33 ± 0.10 and 0.17 ± 0.08 to 0.24 ± 0.08, respectively, whereas very low heritability estimates for both measures were detected at weaning; and (ii) alpha diversity at week 15 had strong and negative genetic correlations with BF, − 0.53 ± 0.23 to − 0.45 ± 0.25, as well as with ADG, − 0.53 ± 0.32 to − 0.53 ± 0.29.Fecal samples, collected from a population of crossbred pigs at three time points, including weaning, week 15 post weaning (hereafter “week 15”), and end-of-feeding test (hereafter “off-test”), were used to evaluate changes in the composition of the fecal microbiome of each animal over time. This study used 1205, 1295, and 1283 samples collected at weaning, week 15, and off-test, respectively. There were 1039 animals that had samples collected at all three time points and also had phenotypic records on back fat thickness (BF) and average daily body weight gain (ADG). Firmicutes and Bacteroidetes were the most abundant phyla at all three time points. The most abundant genera at all three time points included These results are important for efforts to genetically improve the domesticated pig because they suggest fecal microbiota diversity can be used as an indicator trait to improve traits that are expensive to measure.The online version of this article (10.1186/s40168-017-0384-1) contains supplementary material, which is available to authorized users. Until recently, research in physiology and production in livestock has focused on understanding individual’s variability for a wide array of traits. With the assistance of DNA technology, research in swine genetics has changed to include the use of single nucleotide polymorphisms (SNP) in identifying causative mutations that underpin variation in phenotypic measures, as well as to predict future performance of the pig, using all the genes or subsets of genes across the animal genome. However, the pig genome contains less than half the number of genes existing in its second genome, which is its microbiome  whose imThe literature documents a growing body of research characterizing the microbiomes of pigs at different stages of development, including early life –5, growiStudies in domestic pigs have revealed how outbred pigs carry an amount of genetic variation comparable to that of outbred human populations  and are The research reported herein is part of a larger project aimed at investigating the use of microbial information to improve pig health and production, including higher efficiency of feed utilization, better meat quality, and healthier pigs. Within this paper, we focused on two main objectives: (1) characterizing temporal changes in the microbiome community of pig feces with respect to both composition and diversity; and (2) investigating the potential influence of host genetics on this diversity.The pigs used in this study were raised in a commercial setting operated by The Maschhoffs, LLC ; therefore, animal use approval was not needed for the collection of these data. Twenty-eight purebred Duroc sires, from a Duroc population under selection for lean growth, were crossed with Large White × Landrace or Landrace × Large White sows (dam lines) to produce the offspring that were used in this study. The pigs were weaned at 18.6 ± 1.09 days old and were moved to a nursery-finishing facility, where they were put in groups of 20 individuals per pen. Pen mates were paternal half-siblings of the same gender and of similar weaning weight. The experiment was repeated 6 times, each of which comprised of 2 pens  from each of the 28 sires. Pigs that came together in 1 replicate were put in 1 contemporary group (hereafter “cg”) in analyses that followed.The test period began the day the pigs were moved to the nursery-finishing facility. During the nursery, growth, and finish periods, they were fed standard pelleted feed. During the grow-finish period, they were fed standard diets, which were based on sex and live weight. Details of diet formulae and their nutritional values are provided , and off-test. Four pigs were chosen randomly per pen for lean carcass growth measurements, and their rectal swabs were used for microbiome sequencing. In the end, the number of samples at weaning, week 15, and off-test were 1205, 1295, and 1283, respectively. There were 1039 animals having samples collected at all 3 time points. More details on the distribution of samples across families, time points, and sex are provided  and from week 14 through week 22 (hereafter “ADG14_22”).g) to pull down any dislodged material, each swab head was removed from its tube using sterile forceps. Samples were frozen solid at − 80 °C, and approximately 250 μL of 0.1 mm zirconia/silica beads (BioSpec) and a 3.97 mm stainless steel ball were added to the sample (while still frozen to avoid splashing). Samples were allowed to thaw briefly, after which 210 μL 20% SDS and 500 μL phenol:chloroform:IAA  were added. Bead-beating was performed on the MBB-96 , samples were centrifuged , and 250 μL of the aqueous phase was transferred to a new tube. One hundred microliters of this crude DNA was then further purified using a QIAquick 96 PCR purification kit . Purification was performed per the manufacturer’s instructions with the following minor modifications: (i) sodium acetate  was added to Buffer PM to a final concentration of 185 mM to ensure optimal binding of genomic DNA to the silica membrane; (ii) crude DNA was combined with 4 volumes of Buffer PM (rather than 3 volumes); and, (iii) DNA was eluted in 100 μL Buffer EB (rather than 80 μL).Total DNA (gDNA) was extracted from each rectal swab by mechanical disruption in phenol:chloroform. Briefly, 650 μL of extraction buffer  was added to each swab stored in a 2-mL self-standing screw cap tube . Tubes were shaken using a Mini-BeadBeater-96  for 20 s to free sample material from the swab head. Following a brief centrifugation  of the 16S rRNA gene was employed to generate indexed libraries for Illumina sequencing using the strategy described in . AmplicoPairs of V4 16S rRNA gene sequences were first merged into a single sequence using FLASh v1.2.11 , with a P values for multiple testing was completed via Bonferroni correction. This comparative analysis was performed at the genus and species levels.In order to compare microbiome composition longitudinally, relative abundance counts were logarithm-transformed and zero-centered, then plugged into Kruskal-Wallis test. Adjustment of Clustering analysis in this paper was performed in two parts. The first part was aimed to investigate whether or not samples at the three time points could separate from one another based on their microbiome compositions. OTU counts were divided by the total count for each sample , logarithm-transformed, and zero-centered before being applied to the R function “prcomp” for principal component analysis. This analysis was performed at the phylum, class, order, family, genus, and species levels. The results are presented in Fig. Part 2 of the clustering analysis was focused on identifying enterotypes among the samples collected at each time point. For that purpose, Jensen-Shannon Divergence (hereafter “JSD”)  was calcThe clustering analyses described above were repeated using the unrarefied microbiome data as suggested in , and thepi was the proportional abundance of OTU i. A univariate linear regression model was formed to test the significance of fixed effects,μ was the overall mean, age was the time point , family was the paternal half-sib family (n = 28), bs was the birth site (n = 3), dl was the dam line (n = 2) and potential interaction between the age and family, and a random residual effect e; the response was the Shannon index. The index at week 15 and off-test was pre-adjusted for contemporary group (n = 6). Fixed effects that were found insignificant from model (1) were removed from subsequent analyses. After testing the significance of the fixed effects, we explored longitudinal changes in the family effect using the model (1.1), which was analyzed using function “lmer” of the R package “lme4” [The R package “vegan”  was usede “lme4” .1.1\docudays, which were the age of the animal on the day when back fat thickness and live weight were measured; enterotype was the enterotype at weaning, week 15, or off-test; and sex, family, bs, and e remained the same as previously described.We modified model (1) to form model (1.2) to test the impact of enterotypes on BF_18, BF_22, ADGw_14, and ADG14_22. Model (1.2) consisted of five fixed effects, including sex, family, and bs, as well as random permanent environmental effect of litter (n = 718), at three separate time points. The litter effect in this study refers to the nursing environment provided by the mother and siblings, influencing the development of individual pigs, potentially having profound impact on fitness and other phenotypic traits later in life. The random effects were assumed to be uncorrelated with each other. Covariance matrices of the random effects were equal to III was an identity matrix.Model (2) was formed to test the fixed effects of sex and bs. Random effects included animal and residual e in model (3). Model (4) was an extension of model (3) to include the permanent environmental effect litter. We estimated heritability of the Shannon index at each time point, as well as phenotypic and genetic correlations of the index among the three time points. Model (5) was an extension of model (4) to include the random effects of pen where the animals were raised after weaning. The response in model (5) was the Shannon index at week 15 and off-test. We estimated heritability of the index, as well as its phenotypic and genetic correlations between these two time points. Assumptions of the random effects of litter and pen remained similar to model (2). The random effect of animal in models (3), (4), and (5) was given a covariance matrix of AA was the additive numerator relationship matrix, determined from a pedigree. The animal in models (4) and (5) was assumed uncorrelated with other random effects.Genetic parameters of the Shannon index were investigated using models (3), (4), and (5) as described below. The model included fixed effects of pen effect applied to data collected only after weaning; thus, model (5) was used for data from week 15 and off-test only. Genetic parameters for OTU richness, which was the number of OTUs obtained in our rarefied data, were obtained in a similar manner. We also performed bivariate analyses for pairs of traits between Shannon index  and BF_18, BF_22, ADGw_14, and ADG14_22. All of the linear models were tested using ASReml v.4.1 [Models (3), (4), and (5) differed from each other by the number of random effects. The goodness of fit of the models was evaluated via Likelihood Ratio Test (LRT). The ml v.4.1 .The results shown in Fig. Ruminobacter and Akkermansia) were unique to the weaners, 101 were present in all three age groups, one (Anaerotruncus) was present only in the weaners and the 15 weekers, one (Cellulosilyticum) was present only in the weaners and the off-test, and one (Anaerostipes) was present only in the 15 weekers and off-test animals. The number of genera that had significant difference in relative abundance counts (at least P < 0.01 with Bonferroni correction for multiple testing) between weaning and week 15 was 90, between weaning and off-test was 100, and between week 15 and off-test was 82. A full list of P values from Kruskal-Wallis tests at the genus level is provided in Additional file Clostridium significantly increased in proportion over time, from 8.18 ± 6.68% (mean ± SD) at weaning to 15.50 ± 6.67 and 17.80 ± 4.93% at week 15 and off-test, respectively . Other predominating genera at weaning included Escherichia, Bacteroides, and Prevotella with 7.73 ± 12.17, 7.30 ± 7.86, and 6.78 ± 5.76% of the total sequences, respectively. The average proportions of Escherichia and Bacteroides dropped significantly (P < 0.001) to 0.17 ± 0.87 and 0.15 ± 0.45% at week 15, and 0.23 ± 1.79 and 0.40 ± 0.87% at off-test, respectively, whereas the average proportion of Prevotella significantly increased to 13 ± 5.97% at week 15, then significantly (P < 0.001) dropped to 6.74 ± 3.08% at off-test.Among the identified genera, two  existed only at weaning and week 15, 4 species  existed only in the week-15 and off-test individuals, 2 species  were present only in the weaners and off-test animals, and 3 species  were found exclusively in the weaners. Remarkable shifts in the abundance of sequences were observed in E. coli , P. djf_ls16 , B. fragilis , C. jejuni , S. gallolyticus .At the species level, there were 202 identified species, of which 7 species , of all samples at the three time points are presented in Fig. S. gallolyticus, T. sanguinis, C. butyricum, C. catus, E. coli, B. fragilis, B. vulgatus, C. jejuni, C. scindens, and C. bolteae. PC2 was heavily loaded by the species that separated the 15 weekers from the off-test pigs, including E. coli, T. sanguinis, L. amylovorus, O. sp. g2, P. sp. DJF b116, P. copri, P. sp. DJF ls16, P. sp. rs2, D. formicigenerans, and M. elsdenii.Table The highest CH and RS were obtained for two clusters for both male and female samples collected at weaning, week 15, and off-test. We combined male and female samples at each time point and re-ran the CH test for optimal number of clusters. The CH indexes at each time point and clusters of samples corresponding to the optimal number of clusters are presented in Fig. Escherichia, were more abundant in type A samples than in type B samples, and 27 genera, led by Prevotella, were significantly more abundant in type B samples than in type A ones. Enterotypes C and D at week 15 and enterotypes E and F at off-test were significantly distinguished by 24 and 26 genera, respectively. Both type C and type E samples at week 15 and off-test, respectively, were significantly dominated by Clostridium and Turicibater, whereas both type D and type F samples at week 15 and off-test, respectively, were significantly enriched by Lactobacillus and Streptococcus.Compositional characteristics of the enterotypes were studied, and genera that significantly (absolute LDA score > 2) separated one enterotype from the other at each time point are presented in Fig. P > 0.05). Enterotypes C and D had significant effect on BF_18 (P < 0.001) and BF_22 (P < 0.001) but not on ADGw_14 and ADG14_22 (both P > 0.05). Enterotypes E and F had significant effect on BF_22 but not on ADG14_22. Enterotypes D and F were both significantly enriched mainly with Lactobacillus, Streptococcus, and Prevotella compared to Clostridium-enriched enterotypes C and E, and our analysis revealed that enterotype D at week 15 was associated with an increase of 0.08 and 0.10 cm in backfat thickness at week 18 (BF_18) and week 22 (BF_22), respectively, compared to enterotype C. Similarly, enterotype F was associated with an increase of 0.10 cm in BF_22 compared to enterotype E.Using model (1.2), we tested the potential impacts of enterotypes A, B, C, and D on BF_18, BF_22, ADGw_14, and ADG14_22, as well as the effect of enterotypes E and F on BF_22 and ADG14_22. Enterotypes A and B did not have significant effect on the traits , week 15 (P < 0.05), and off-test (P < 0.001).We tested whether or not the distribution of samples in the enterotypes . At week 15, types C and D had an average Shannon indices of 4.50 ± 0.30 and 4.47 ± 0.29 in males and 4.60 ± 0.26 and 4.57 ± 0.27 in females, respectively. These averages were not significantly different between the two enterotypes in both sex groups (P > 0.05). The average Shannon indices for type E and type F off-test pigs were 4.57 ± 0.32 and 4.59 ± 0.31 in males and 4.67 ± 0.28 and 4.73 ± 0.23 in the females, respectively. Significant difference in the Shannon index between type E and type F animals was observed only in the female pigs (P < 0.01).Alpha diversity of the microbiome was evaluated using the Shannon index and plotted in Fig. P < 0.05), bs (P < 0.001), and interaction between age and family (P < 0.001) on the alpha diversity of microbiome measured using the Shannon index. The estimated effects of the fixed factors are listed in Additional file dl was not significant (P > 0.05) and was thus removed in subsequent analyses. We compared the clustering results from the rarefied data to the results from the unrarefied, and found no major differences in the clusters and enterotypes. For the reader reference, we put the results using the unrarefied data in Additional file The test from model (1) showed a significant impact of sex  and are plotted in Fig. sex (P < 0.05) and bs (P < 0.001) and insignificant effect of family (P > 0.05) at weaning. At week 15 and off-test, both family and sex effects were significant (P < 0.001), whereas the bs effect was insignificant (P > 0.05).Analyses performed at individual time points and described in model (2) revealed significant impact of We considered the index as a phenotypic record and hence attempted to estimate its genetic parameters. The results from model (3), not including the litter effect, are presented in Table We also estimated genetic parameters of OTU richness and observed a trend similar to the one found with the Shannon index. Table Tables Tables P < 0.001), as well as when including the pen effect, model (4) vs model (5) (P < 0.001), for both Shannon index and OUT richness. The results suggest that it is important to include both litter and pen in the model when estimating genetic parameters for the two traits.The LRT revealed significant improvement in goodness of fit when including the litter effect, model (3) vs model (4)  an Escherichia-enriched cluster in which the presence of Enterococcus might indicate gut health disruption [The gut microbiota of the animals in this study were predominated by two phyla, Firmicutes and Bacteroidetes, in agreement with published research , 44–46. orted in , 46, 47.ng birth , 49 and ng birth , 51. At sruption .Prevotella and Ruminococcus enterotypes as reported in pigs [Prevotella, Bacteroides, and Ruminococcus enterotypes as reported in human research [Analyses of enterotypes in this study were based on the assumption that there existed at least two enterotypes among the pigs at each time point, and we were interested in their potential association with back fat deposition and growth rate. The pigs used in this study did not clustered into  in pigs  nor did research , 53. TheA highly diverse microbiota is beneficial to the host –56. We hBefore this discussion extends to the genetic parameters of microbiota diversity, it might be worth clarifying the use of permanent environmental effect of litter in the statistical models that we used in this paper. The permanent environmental effect in this study refers to the nursing environment provided by the mother and siblings. This environment influences the development of individual pigs and potentially has a profound impact on fitness and other phenotypic traits later in life –67. It ih2 = 0.13) and belly weight (h2 = 0.28) [h2 = 0.26), meat color (h2 = 0.28), growth rate (h2 = 0.30), and feed conversion ratio (h2 = 0.29) [This study is the first to describe OTU richness and alpha diversity as phenotypic traits in farm animals and the first to estimate their genetic parameters at three key stages of pig development. The heritability of 0.15 – 0.33 reported in this study means that 15 – 33% of the variation in alpha diversity measured in our pigs, at week 15 and off-test, was due to genetics. Examples of traits with similar heritability range include residual feed intake ( = 0.28) ; tendern = 0.29) –75. ThesDomestic pigs are similar to humans in terms of anatomy, genetics, and physiology . They caThis study was conducted on a group of crossbred pigs living through three stages of life  and was designed to explore longitudinal changes in fecal microbiome composition and diversity, as well as the influence of host genetics on microbiome diversity. Two enterotypes were identified at each stage of life, but only enterotypes at week 15 and off-test were proven to be associated with back fat thickness. Microbiome alpha diversity as measured using the Shannon index was found to be lowly to moderately heritable at week 15 and off-test. The diversity at these two time points was also found to have strong genetic correlation to each other. The diversity index at week 15 was also strongly correlated with back fat and average daily gain of the pigs. These findings may lead to a new direction of research in animal breeding and genetics and suggest potentially significant utility for gut microbiome data in the genetic evaluation process.Additional file 1: Table S1.Diet formulae and their nutritional values. (PDF 70 kb)Additional file 2: Table S2.Table S3. Injectable medications. Table S4. Water medications. (PDF 36 kb)Vaccinations. Additional file 3: Table S5.Distribution of samples across families, sex, and time points. (PDF 39 kb)Additional file 4: Table S6.Table S7. Proportion of OTU counts at weaning, week 15, and off-test by class. Table S8. Proportion of OTU counts at weaning, week 15, and off-test by order. Table S9. Proportion of OTU counts at weaning, week 15, and off-test by family. Table S10. Proportion of OTU counts at weaning, week 15, and off-test by genus. Table S11. Proportion of OTU counts at weaning, week 15, and off-test by species. (PDF 141 kb)Proportion of OTU counts at weaning, week 15, and off-test by phylum. Additional file 5: Table S12.P values from Kruskal-Wallis tests for differences in abundance between pairs of time points at the genus level. (PDF 71 kb)Additional file 6: Figure S1.Sample flow between enterotypes through weaning, week 15, and off-test. (PDF 46 kb)Additional file 7: Table S13.Distribution of samples in enterotypes by family. (PDF 43 kb)Additional file 8: Table S14.Effects and standard errors of family and time on alpha diversity estimated from model (1). (PDF 19 kb)Additional file 9: Table S15.Proportion of variation explained by the first two principal components at different taxonomic levels and contribution of top members to the first two principal components, using unrarefied microbiome data. (PDF 47 kb)Additional file 10: Figure S2.Scatter plots of samples by principal component 1 (PC1) and principal component 2 (PC2) at six taxonomic levels, using unrarefied microbiome data. (PDF 447 kb)Additional file 11: Figure S3.Calinski-Harabasz indexes (CH) for number of potential clusters of samples at weaning, week 15, and off-test, using unrarefied microbiome data. (PDF 174 kb)


Under these conditions, the AM and TZ signals were observed at 0.56 and 0.74 V, respectively. A linear response were found over the 0.15 to 1.20 µmol L−1 and 0.15 to 0.80 µmol L−1 concentrations, with detection limits (3σ/slope) of 26 and 70 nmol L−1 for AM and TZ, respectively. Reproducibility for 17.7 µmol L–1 AM and TZ solutions were 2.5 and 3.0% (n = 7), respectively, using three different electrodes. The method was validated by determining AM and TZ in spiked tap water and unflavored gelatin spiked with AM and TZ. Because a beverage containing both AM and TZ was not found, the method was applied to the determination of AM in a kola soft drink and TZ in an orange jelly and a soft drink powder.A fast, sensitive, and selective method for the simultaneous determination of one pair of synthetic colorants commonly found mixed in food products, Amaranth (AM) and Tartrazine (TZ), based on their adsorption and oxidation on a screen-printed electrode (SPE) is presented. The variation of peak current with pH, supporting electrolyte, adsorption time, and adsorption potential were optimized using square wave adsorptive voltammetry. The optimal conditions were found to be: pH 3.2 (PBS), E Wu et al. [−1 for AM. On the other hand, Ma et al. [Amaranth E123. AM) and tartrazine (E102. TZ) are synthetic water-soluble azo dyes that are widely used in drinks, cake mixes, ice-creams, cereals, candies, wines, soups, salad dressings, jams, chocolates, and coffee, as well as in a variety of drugs and cosmetics. Color provides the first impression of a food taste. AM is used extensively to give reddish or brownish color to drinks, syrups, and sweets, while TZ is a bright orange-yellow powder. Thus, synthetic dyes can replace natural colors due to their several advantages such as low cost, high stability to light, oxygen, pH, and low microbiological contamination. However, recent studies have revealed that many of these synthetic food colors may be harmful to people’s health. It has been reported that under anaerobic conditions, azo dyes can be reduced to form aromatic amines. For example, azoreductases from intestinal bacteria, some environmental microorganisms, and, to a lesser extent, mammals, catalyze the cleavage of the azo bond to produce aromatic amines which may be toxic, mutagenic, and carcinogenic to animals [23. AM an mg kg−1 ,5. For t mg kg−1  reportedAdsorptive stripping voltammetry (AdSV) is a useful technique for determining organic compounds with oxidizable or reducible groups, such as azo dyes, since it combines excellent sensitivity, selectivity, accuracy, and precision with low instrumentation cost. The electrochemical reduction of azo dyes was studied extensively many years ago with mercury electrodes , but electrochemical oxidation and detection using unmodified and modified electrodes is still missing. One possible way of enhancing the adsorptive process and the sensitivity of the method is the use of surfactants, whose beneficial effects are unpredictable, as they tend to interfere by competitive adsorption. Surfactants may affect the speed of the electrochemical reactions, increase the reversibility of the system, and/or increase the oxidation or reduction current. Gomez et al.  reportedThe aim of this work is the determination of AM and TZ using an unmodified screen-printed carbon electrode. To the best of our knowledge, the use of this electrode for the simultaneous determination of AM and TZ has not yet been reported.Cyclic voltammograms (CV) and square wave voltammograms (SWV) were obtained using a DropSens µStat 400 potentiostat. The working electrode was a screen-printed 4-mm diameter carbon electrode (DRP-110) provided by DropSens (Oviedo-Spain). It had a built-in Ag reference electrode and an auxiliary carbon electrode. A magnetic stirring stick was used to homogenize the solution during the accumulation time. The pH measurements were made using a Lovibond SD 50 pH meter.−1 of AM and TZ were prepared in water. Phosphate buffer solutions (PBS) were prepared from H3PO4, NaH2PO4, or NaHPO4 (Merck), and adjusted to the required pH with NaOH or HCl solutions. Acetate buffer solutions were prepared with acetic acid, adjusted to the required pH with NaOH.Water used for sample preparation, reagent dilution, and rinsing purposes was obtained from Wasselab Purifier System (ASTM D1193). All chemicals  were of analytical grade from Merck . Amaranth (AM) and tartrazine (TZ) were obtained from Aldrich. Stock solutions containing 0.71 mmol L–1), 250-μL aliquots of 0.708 mmol L−1 AM and/or TZ solution (17.7 µmol L−1), and deionized water up to a final volume of 10.0 mL were added to the voltammetric cell. An accumulation potential of 0.00 V (Eads) was applied for 30 s (tads) with constant stirring at 1000 rpm. After an equilibrium time of 10 s, voltammograms were recorded, while the potential was scanned from 0.00 to 1.00 V using square wave modulation with 10 mV step amplitude, 10 mV pulse amplitude, and a frequency of 15 Hz. Each voltammogram was repeated three times. The calibration curves were obtained and linear regression and detection limits were calculated. Validation of the methodology was carried out in tap water spiked with AM and TZ. The proposed method was applied to the determination of AM in kola soft drink and TZ in an orange gelatin and a soft drink powder. In order to eliminate matrix effects, the standard addition method was used. The DL was calculated from DL(x) = by 3σx/y/b where σx/y is the random error in x and y, and b is the slope assuming that errors occur mainly in the y-direction. On the other hand, in the cyclic voltammograms the potential was scanned from 0.3 to 1.2 V at a scan rate of 50.0 mV s−1 without accumulation.Buffer solution  at pH 3.2 (curve a).The effect of the solution’s pH on the anodic stripping peak currents of AM and TZ using a screen-printed electrode was studied in the range from 3.2 to 6.8 with nitric acid and phosphate buffer solutions. pa vs. pH plot were 18 and 26 mV for AM and TZ, respectively. Wang et al. [This figure shows two well-defined oxidation peaks at 0.66 V (ipa 0.37 µA) and 0.78 V (ipa 0.65 µA), which corresponded to the oxidation of AM and TZ, respectively. No reduction peak appears on the reverse scan, indicating the irreversible oxidation of these dyes. It was found that the peak potential shifted negatively when the pH increased (curve b), proving that protons are involved in the electrochemical reaction of AM and TZ. At pH 6.8, the signal of AM was only a slight inflection  and the signal of TZ  was extremely wide. Upon increasing the pH from 3.2 to 6.8, the currents decreased. pH 3.2 was used to achieve a sensitive and selective determination method. Plots with the relationship between the oxidation peak potentials of AM and TZ and pH are presented in g et al.  reportedg et al.  reportedg et al. .−1, pH 3.2) recorded on SPCE in various solutions of supporting electrolytes with the addition of 300 µL of nitric acid (curve a), acetic buffer (curve b), or phosphoric buffer (curve c), all of them at a concentration of 0.01 mol L−1. For all the investigated solutions, two signals of well-defined equal oxidation peaks were seen in recorded CVs. With nitric acid the oxidation signals were found at 0.66 V and 0.79 V (0.36 and 0.78 µA), with acetic buffer the signals were found at 0.60 V and 0.76 V (0.69 and 0.95 µA), and with phosphoric buffer the signals were found at 0.56 V and 0.74 V (1.34 and 1.07 µA) for AM and TZ, respectively. On the other hand, the separation of the AM and TZ signals (∆Epa) were 0.13, 0.16, and 0.18 V for nitric acid, acetic buffer, and phosphoric buffer, respectively. According to these results, it can be concluded that the highest peak currents for AM and TZ (1.34 and 1.07 µA) and the greatest variation of ∆Epa (about 180 mV) occurs with phosphoric buffer. All subsequent measures were carried out using phosphate buffer pH 3.2 (0.01 mol L−1), because it gave an adequate sensitivity and selectivity to determine AM and TZ simultaneously.The supporting electrolyte decreases the electrical resistance of the cell and reduces the Ohmic drop effect. On the other hand, it suppresses the migration of electro-active species towards the electrodes through electrostatic attractions to achieve diffusion-controlled currents. pa vs. scan rate . The oxidation peak currents of both dyes increased linearly with the scan rate in the range of 10–100 mV s−1 with the regression equations of ipa = 1.071 + 0.034v (r = 0.994) for AM and ipa = 0.919 + 0.028v (r = 0.992) for TZ. The linear relationship between the peak currents and scan rate suggested a predominantly adsorption-controlled process. Meanwhile, the oxidation peak potential (Epa) of AM and TZ was not shifted with the scan rate. Zhao et al. [−1 range, while it also deviates from linearity from 400 to 1000 mV s−1, indicating the adsorption process changing to a diffusion process using a carbon nanotube and polypyrrole composite-modified electrode. Yu et al. [−1 range, indicating diffusion-controlled electrode processes, and the oxidation peak potential positively shifted with the scan rate using a graphene and mesoporous TiO2-modified carbon paste electrode.With the purpose of identifying the process in the mass transport, the influence of the scan rate (v) on anodic peak currents for these azo dyes was studied using SPCE. 4v r = 0.94 for AMu et al.  also ass–1) was studied in the range of −0.20 to 0.20 V using square wave stripping voltammetry. Anodic peak currents for AM and TZ increased when the potential was changed from −0.20 to 0.00 V, and at more positive values it decreased sharply. A potential accumulation of 0.00 V gives the highest anodic peak currents for AM and TZ, and it was chosen for further measurements. On the other hand, the effect of accumulation time was examined in the 0–60 s range. Peak current increased with increasing accumulation time prior to the potential scan, indicating that the AM and TZ are readily adsorbed on the SPCE. At first, the peak current of AM and TZ increased almost linearly with accumulation time until 30 s and then it tended to a steady value, probably due to electrode saturation. Results are not shown. On the basis of this result, 30 s was chosen for all measurements.The effect of the adsorption potential on the anodic peak currents for AM and TZ , Eads 0.00 V, and tads 30 s. Under these conditions, the peak current was proportional to the concentration of AM over the 0.15–1.20 μmol L–1 range as well as proportional to the concentration of TZ over the 0.15–0.80 μmol L–1 range  for AM and TZ (17.7 µmol L–1), respectively, using three different electrodes.Optimal analytical conditions were found to be: phosphate buffer pH 3.2  and the determination was carried out using the standard addition method, getting 0.80 ± 0.18 µmol L–1 for AM and 0.73 ± 0.12 µmol L–1 for TZ . Due to these errors, more dye was added. A second aliquot of tap water (sample 2) was contaminated with 1.00 µmol L–1, obtaining 1.05 ± 0.10 µmol L–1 for AM and 1.13 ± 0.14 µmol L–1 for TZ . Tap water spiked with AM and TZ was used for validation measurements. An aliquot of water was contaminated with these dye solutions (0.65 µmol L−1) are shown in −1), Eads 0.00 V, and tads 30 s. The SWVs showed that SY, AM, RA, and TZ were oxidized at 0.55, 0.63, 0.67, and 0.78–0.83 V, respectively. These results indicate that only the simultaneous determination of SY-TZ (∆E: 230 mV), AM-TZ (∆E: 200 mV), and SY-RA (∆E: 120 mV) are possible. Interference in the determination of AM is produced by AR.In the voltammetric simultaneous determination of AM and TZ, the main interference may be caused by the presence of other dyes such as Sunset yellow (SY), Allura red (AR), Sudan I, Sudan II, and so on. Sudan I and Sudan II did not show signals under the conditions optimized for AM and TZ when they were added in concentrations 200 times greater than these. Instead, SY and AR were oxidized on a screen-printed carbon electrode. Square wave stripping voltammograms for AM, TZ, SY, and RA solutions (0.5 µmol L–1 of AM (21.5 mg L−1). –1 (5.7 and 32.3 mg L−1), respectively. These last two samples also contained 5.0 ± 1.0 and 10.0 ± 2.0 µmol L–1 (2.3 and 4.6 mg L−1) of SY. WHO and FAO recommend an acceptable daily intake between 0 to 0.5 mg kg–1 for AM, and of 0.75 mg kg−1 for TZ. Therefore, child weighing 30 kg should not consume more than 700 mL of kola and orange soft drinks daily.In order to apply the optimized methodology, we looked for drinks that contained both AM and TZ. However, this was not possible, as kola soft drinks contain only AM. Food manufacturers indicate what kind of dye or of dyes are present, but the amount is not specified. In addition, the ingredient information for kola soft drinks reported the use of AM. Using the standard addition method, three samples of kola soft drinks were analyzed obtaining 35.5 ± 0.50 µmol L−1, respectively) were below the permitted limits of AM and TZ in commercial beverages (100 mg L−1). The same electrode without treatment was used for a series of measurements. The relative error of the validation was considerable at lower concentrations of 10.0 µmol L−1.A screen-printed carbon electrode was very adequate for the quick determination of AM in kola soft drinks and of TZ in gelatins and soft drinks. The calibration curve showed good linearity with only 30 s of accumulation. The DLs obtained for AM and TZ (15.7 and 37.4 µg L