In this study, bio-oil from the fast pyrolysis of renewable biomass was used as the raw material to synthesize bio-oil phenol formaldehyde (BPF) resin—a desirable resin for fabricating phenolic-based material. During the synthesis process, paraformaldehyde was used to achieve the requirement of high solid content and low viscosity. The properties of BPF resins were tested. Results indicated that BPF resin with the bio-oil addition of 20% had good performance on oxygen index and bending strength, indicating that adding bio-oil could modify the fire resistance and brittleness of PF resin. The thermal curing behavior and heat resistance of BPF resins were investigated by differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA). Results showed that adding bio-oil had an impact on curing characteristics and thermal degradation process of PF resin, but the influence was insignificant when the addition was relatively low. The chemical structure and surface characteristics of BPF resins were determined by Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). The analysis demonstrated that adding bio-oil in the amount of 20% was able to improve the crosslinking degree and form more hydrocarbon chains in PF resin. Through a long history of development, phenol formaldehyde (PF) resin-based materials have been widely used in many fields, thanks to their desirable properties of high rigidity, good corrosion resistance, and less smoke and toxicity when burning. However, the environmental stress and price fluctuation caused by petroleum-based phenol obtained from fossil resources has severely limited the application of PF resin-based material. Therefore, industry efforts have been focused on identifying phenolics from renewable resources.Biomass is an important renewable source for energy and chemical feedstock, considered as an effective alternative to fossil resources ,2,3. PyrBased on previous research, utilizing bio-oil or its phenolic fraction as a phenol source to prepare resol PF resin was feasible, and the BPF resins created with lower cost and toxicity had been proved to be an effective wood adhesive. However, BPF resins used for adhesives were unable to meet the requirement when fabricating resin-based material because of low solid content, high viscosity, and high curing temperature. A distillation processes was needed to improve the properties of traditional BPF resins, yet that would cause increased costs and waste water discharge. In addition, due to the relatively low activity of bio-oil, the adhesive properties of BPF resin synthesized with phenol replacement by bio-oil was weaker than that of commercial PF resin.In this study, solid paraformaldehyde was used to replace formaldehyde solution (37 wt %) during BPF resins synthesis, aiming to get the properties of high solid content and low viscosity without the distillation processes. In order to maintain the inherent structure of PF resin, the content of synthetic phenol from the petrochemical industry and formaldehyde remained consistent when adding bio-oil to prepare BPF resins. The performance of BPF resins was tested using corresponding standards. The characteristics of BPF resins in terms of curing behavior, curing kinetics, thermal gravimetric properties, chemical structure, and surface characteristics will be described. Larix gmelinii) sawdust with dimensions <1 mm was pyrolysised by a fluidized bed at 500–510 °C, which belonged to the Institute of Wood Based Material, Beijing Forestry University, Beijing, China. The liquid products were subsequently extracted with ethyl alcohol , washed by distilled water, and neutralized with sodium hydrogen carbonate . The obtained bio-oil contained ingredients of 22% phenolic, 24% ketone, 20% aldehydes, 16% organic acids, 8% sugars, 4% hydrocarbons and other compounds, as determined by GC-MS analysis. The solid paraformaldehyde and phenol  were reagent-grade and used as obtained without purification.Dried larch  were mixed in a 250-mL three-neck round-bottom glass reactor equipped with a stirrer, thermometer, and reflux condenser. The reactant was heated to 70 °C in 30 min. Then, 80% paraformaldehyde was gradually added and the temperature was held at 70–75 °C under continuous stirring until completely depolymerized. Next, the remaining 30% of NaOH solution (30 wt %) was added, and the reactant was heated to 80 °C in 30 min and kept for 15 min. After the temperature was dropped to 70 °C, the remaining 20% of paraformaldehyde and bio-oil was gradually added. The reactant was heated to 85–90 °C and held for 45 min. When the reaction was complete, it was rapidly cooled to 40 °C to yield the BPF resins.The BPF resin castings with the size of 150 × 6.5 × 3 mm³ (length × width × thickness) were separately prepared to meet the tests of bending strength and oxygen index, using acidic curing agent at room temperature.After surface clearing and drying, the mold was coated with solid paraffin as the release agent. For each resin casting, acidic curing agent (0.35 g) was gradually added to resins (7 g) and the mixture was blended. However, to make it easy to prepare, the amount of resins that was blended for one time should be used in six or eight castings. Next, the resin mixture was poured into the mold and laid for 24 h at 25 °C to cure. After demolding and further curing at 70 °C for 2 h, the casting model was finally prepared.The viscosity, solid content, free formaldehyde, and free phenol of BPF resins were determined in accordance with the Chinese National Standard (GB/T 14704-2006). The bending strength and oxygen index of BPF resins were measured according to Chinese National Standard . Oxygen index was defined as the minimum volume fraction of oxygen in the mixed gas in which the resin casting could be fired. Oxygen index was used to investigate the property of fire resistance, and was measured by special equipment with the resin coasting prepared as the size of 150 × 6.5 × 3 mm³ (length × width × thickness).−1 in aluminum pans. The thermal degradation processing of uncured resins was conducted by thermal gravimetric analysis (TGA)  in a nitrogen atmosphere. The scanning temperature ranged from 50 to 850 °C with the rate of 10 °C·min−1. The flow rate of nitrogen in DSC and TGA analysis was 20 mL·min−1. The functional groups of uncured and cured resins were characterized by FTIR . The Fourier transform infrared (FTIR) spectra of BPF resins ranged from 4000 to 400 cm−1. The surface characteristics of cured resins were observed by SEM  at an accelerating voltage of 5000 V. The uncured resins were freeze-dried before DSC, TG, and FTIR analysis. The cured resins were prepared by treating uncured resins at 120 °C for 2 h in an air convection oven.The thermal curing behavior was analyzed by a differential scanning calorimeter  in a nitrogen atmosphere. Uncured resins were heated from 40 °C to 200 °C at heating rates of 5, 10, 15, and 20 K·minAs shown in As the bio-oil addition increased, the free formaldehyde content of BPF resin was gradually reduced, but the free phenol content continuously enhanced. This showed that natural polyphenols in bio-oil can undergo hydroxyl-methylated reaction with formaldehyde as the synthesis process, and most of the formaldehyde was consumed, but excessive natural polyphenol would raise the content of free phenol. The low-toxicity resin needed the free formaldehyde and phenol to be below the limited requirement at the same time.Oxygen index and bending strength of resin casting model reached their maximum values at the bio-oil addition of 20%, which proved that bio-oil can improve the brittleness and fire resistance of PF resin. Bio-oil includes plenty of active ingredients, such as phenols, aldehydes, ketones, and hydrocarbon, and can react with a PF resin system, the performance of which is thus improved. However, adding too much bio-oil to the formaldehyde–phenol system would interfere in the formation of the inherent crosslinking structure and have a negative effect on the performance of the resin.The results indicated that BPF resin synthesized by suitable proportions of bio-oil and paraformaldehyde exhibited good performances compared to prepared resin-based materials.−1 are displayed in The DSC curves of the various BPF resins at the heating rate of 10 °C·minThe surfaces of exothermic peaks of BPF resins were larger than that of PF resin, and the peak temperature values increased with the increased bio-oil addition, indicating that adding bio-oil may reduce the reactivity of PF resin. The results of curing temperature—determined from linear-regression analysis of peak temperatures at different heating rates ,18—also f) were always slightly lower than the values produced by Kissinger method (Ek). All the regression coefficient values were more than 0.98, proving that the results calculated by the above methods were credible. The activation energy values of BPF resins were higher than that of PF resin, and that phenomenon became more obvious with the increasing amount of bio-oil addition. It was also demonstrated that the BPF resins with relatively high bio-oil addition  required much more energy to cure completely than those having low bio-oil addition.The activation energy was obtained by the Flynn–Wall–Ozawa or Kissinger method, depending on the peak temperature at different heating rates . As showAs mentioned above, the differences of the curing properties between PF and BPF resins were mainly attributed to the lower chemical activity of bio-oil, which may cause serious steric hindrance during the curing process. However, as compared to the data of curing temperature and activation energy in 4. Some phenolic hydroxyls were stripped off, and hydroxyl radicals were generated correspondingly. The methylene and hydroxymethyl could be oxidized by hydroxyl radicals to create the structures of carbonyl and carboxyl [2 may come from the further reactions. A few of benzene and its derivatives came out of the methylene bridge and ether bridge breaking. Besides, reactions between phenolic hydroxyl and methylene or two hydroxyl groups resulted in the release of water. In region III, the weight loss was mainly caused by the dehydrogenation of benzene rings [The TGA and derivative TGA (DTG) curves are summarized in carboxyl , of whicne rings .As clearly shown, the temperature-dependent weight loss of BPF resins were more than in the case of PF resin, indicating that the carbohydrates and carbides of bio-oil were easily volatilized at higher temperature. However, as compared the TGA and DTG curves of various resins, the gap between the maximum weight loss of 54.6% and minimum 49.7%—attributed to 40%-BPF and PF resin, respectively—actually appeared to be slight. This demonstrated that adding bio-oil had a limited impact on degrading the heat resistance.The uncured and cured BPF resins were analyzed using the FTIR spectrum in −1 was assigned to the stretching vibration of hydroxyl [−1 and benzyl hydroxyl C–O at 1104 cm−1 [−1 and benzene C=C at 1608 cm−1. The accompanied peaks around 877 cm−1, 815 cm−1, and 750 cm−1 were associated with the bending vibration of C–H in the aromatic rings [−1 and 2865 cm−1 or the bending vibration of aliphatic –CH2 at 1431 cm−1 and –CH3 at 1386 cm−1 [hydroxyl , and the104 cm−1  suggesteic rings . The mol386 cm−1 .The peaks of uncured 20%-BPF resin related to the above functional groups were remarkable, as shown in The FTIR spectrum of the cured BPF and PF resins is illustrated in 2, and –CH3 were strong in cured 20%-BPF resin, proving that many hydrocarbons were present after the curing process. This demonstrated that adding bio-oil had toughening effects, and its initial mechanism was revealed. The above results are consistent with the performance of the oxygen index and bending strength of various BPF resins, as shown in Compared to cured PF resin, the intensity of the peaks related to aliphatic C–H, –CHThe SEM images of the surface of cured PF resin and cured BPF resins with bio-oil addition of 20% and 40%, respectively, are shown in That use of bio-oil from fast pyrolysis and paraformaldehyde as raw materials was an effective approach for synthesizing BPF resins—a desirable resin for fabricating phenolic-based material. The BPF resin with the bio-oil addition of 20% had good performance in oxygen index and bending strength, indicating that adding bio-oil was beneficial to modify the fire resistance and brittleness of PF resin. Adding bio-oil had an impact on curing characteristics and thermal degradation process of PF resin, but the influence was insignificant when the addition was relatively low. When the addition of bio-oil was 20%, more chains of hydrocarbons were formed in BPF resin. This showed that the crosslinking degree of cured 20%-BPF resin was improved and the intermolecular force of crosslinking structure was enforced.


Hyperglycemia is a known predictor of negative outcomes in stroke. Several glycemic measures, including admission random glucose, fasting glucose, and glycated hemoglobin (HbA1c), have been associated with bad neurological outcomes in acute ischemic stroke, particularly in nondiabetic patients. However, the predictive power of these glycemic measures is yet to be investigated.This retrospective study enrolled 484 patients with acute ischemic stroke from January 2009 to March 2013, and complete records of initial stroke severity, neurological outcomes at three months, and glycemic measures were evaluated. We examined the predictive power of admission random glucose, fasting glucose, and HbA1c for neurological outcomes in acute ischemic stroke. Furthermore, subgroup analyses of nondiabetic patients and patients with diabetes were performed separately.p = 0.026; fasting glucose = 0.598, p = 0.001; HbA1c = 0.510, p = 0.742). Subgroup analyses of nondiabetic patients and those with diabetes revealed that only fasting glucose predicts neurological outcomes in patients with diabetes, and the AUCs of these three glycemic measures did not differ between the two groups. A multivariate logistic regression analysis of the study patients indicated that only age, initial stroke severity, and fasting glucose were independent predictors of poor neurological outcomes, whereas admission random glucose and HbA1c were not . Furthermore, subgroup multivariate logistic regression analyses of nondiabetic patients and those with diabetes indicated that none of the three glycemic measures were associated with poor neurological outcomes.Receiver operating characteristic (ROC) analysis revealed that admission random glucose and fasting glucose were significant predictors of poor neurological outcomes, whereas HbA1c was not (areas under the ROC curve (AUCs): admission random glucose = 0.564, Fasting glucose is an independent predictor of poor neurological outcomes in patients with acute ischemic stroke and had greater predictive power than that of admission random glucose and HbA1c. The predictive power of glycemic measures for poor neurological outcomes did not differ significantly between the nondiabetic patients and those with diabetes. Hyperglycemia may be present in more than half of patients with acute ischemic stroke . It is aThe present study was approved by the Joint Institutional Review Board of Taipei Medical University (No. 201304008). Data were obtained by reviewing medical records of the patients with acute ischemic stroke admitted to the hospitals affiliated with Taipei Medical University, Wan Fang Hospital and Shuang Ho Hospital, between January 2009 and March 2013. Data were included for analysis if the patients were aged ≥20 years, presented to the emergency department with acute ischemic stroke and were subsequently admitted to the neurology department, and complete records of admission random glucose, fasting glucose, HbA1c, National Institute of Health Stroke Scale (NIHSS) scores at admission, and modified Rankin Scale (mRS) scores at three months after stroke were obtained. After reviewing the enrolled medical records, patients with a history of stroke or who died or were lost to follow-up within three months after stroke were excluded.In the present study, acute ischemic stroke was confirmed by clinical symptoms and neuroimages. The etiologic subtype of acute ischemic stroke was classified according to the Trial of Org 10172 in Acute Stroke Treatment (TOAST) study . Admissit, Mann–Whitney U, and chi-squared tests, respectively. Receiver operating characteristic (ROC) analysis was conducted for evaluating the predictive performance of the glycemic measures, namely admission random glucose, fasting glucose, and HbA1c, in distinguishing between the favorable and poor neurological outcomes. Specifically, the predictive performance of these glycemic measures was evaluated by comparing their areas under the ROC curve (AUCs)  and as the median with the interquartile range (IQR), respectively, and p < 0.05 was considered statistically significant. Statistical data were analyzed using PASW Statistics for Windows version 18.0  and MedCalc Statistical Software version 16.8 .The continuous variables, rank variables, and proportions of categorical variables were compared between the nondiabetic patients and those with diabetes by using the Student e (AUCs) . A univap = 0.026; fasting glucose =0.598, 95% CI [0.553–0.642], p = 0.001); however, HbA1c was not a significant predictor . The cutoff values with the highest sensitivity and specificity were 127 mg/dL for admission random glucose  and 109 mg/dL for fasting glucose . The AUCs of admission random glucose and fasting glucose did not differ significantly , and the AUCs of admission random glucose and fasting glucose were significantly higher than that of HbA1c . The ROC curves of admission random glucose, fasting glucose, and HbA1c in the nondiabetic patients (n = 212) and patients with diabetes (n = 272) are presented in p = 0.071) with an optimal cutoff value of 109 mg/dL , whereas admission random glucose and HbA1c were nonsignificant predictors of poor neurological outcomes . In the patients with diabetes, fasting glucose was a significant predictor of poor neurological outcomes  with an optimal cutoff value of 140 mg/dL , whereas admission random glucose and HbA1c were nonsignificant predictors of poor neurological outcomes . The AUCs of glycemic measures were not significantly different between the nondiabetic patients and those with diabetes .The AUCs of admission random glucose, fasting glucose, and HbA1c in all patients are presented in p = 0.003). However, neither admission random glucose nor HbA1c were significant predictors of poor neurological outcomes. The adjusted OR of admission random glucose was determined using multivariate logistic regression model 1, which included age, sex, the NIHSS score at admission, atrial fibrillation, and admission random glucose. Age and the NIHSS score at admission were significant predictors of poor neurological outcomes, and admission random glucose was not associated with poor neurological outcomes in model 1 . The adjusted OR for fasting glucose was determined using multivariate logistic regression model 2, which included age, sex, the NIHSS score at admission, atrial fibrillation, and fasting glucose. Age, the NIHSS score at admission, and fasting glucose were significant predictors of poor neurological outcomes in model 2 . The adjusted OR of HbA1c was calculated using multivariate logistic regression model 3, which included age, sex, the NIHSS score at admission, atrial fibrillation, and HbA1c. Age and the NIHSS score at admission were significant predictors of poor neurological outcomes, and HbA1c was not associated with poor neurological outcomes in model 3 .p = 0.002; patients with diabetes =1.004, 95% CI [1.001–1.008], p = 0.003). However, after adjustment for the influence of age, sex, the NIHSS score at admission, and atrial fibrillation, all three glycemic measures were not associated with poor neurological outcomes in both groups.Subgroup univariate and multivariate logistic regression analyses were performed in the nondiabetic patients and patients with diabetes . In the In the present study, of the three glycemic measures, fasting glucose was an independent predictor and the most predictive factor of poor neurological outcomes in patients with acute ischemic stroke. ROC analysis revealed that admission random glucose was a predictor of poor neurological outcomes in all study patients; however, it was not associated with poor neurological outcomes in the logistic regression analysis. Furthermore, ROC and logistic regression analyses revealed that HbA1c was not a predictive factor of poor neurological outcomes. The predictive value of glycemic measures for poor neurological outcomes did not differ significantly between the nondiabetic patients and patients with diabetes.ROC analysis revealed that fasting glucose and admission random glucose have more predictive value than HbA1c does for poor neurological outcomes in acute ischemic stroke. Studies have reported that a high level of fasting or random glucose is associated with poor neurological outcomes or mortality in acute ischemic stroke , and hypSome studies have reported that in acute ischemic stroke, hyperglycemia is more detrimental in nondiabetic patients than in patients with diabetes ; howeverIn preclinical animal studies, controlling hyperglycemia with insulin resulted in heterogeneous neuroprotection outcomes . In randThe present study had some limitations. First, most patients enrolled had mild to moderate initial stroke severity , and the influence of hyperglycemia might be more prominent in patients with severe stroke. However, only 84 patients in the present study had an NIHSS score at admission ≥10, and a subgroup analysis of these patients would not have yielded sufficient statistic power. Second, mortality was not included as an outcome because the timing of and reason for mortality could not be assessed accurately in the retrospective medical review. Some patients might have died or received follow-up in facilities other than the two hospitals included in the present study; therefore, the patients who died or were lost to follow-up within three months after stroke were not included in this study. Because of the aforementioned limitations, the results of this study cannot be generalized to all ischemic stroke patients. Third, diabetes was defined as HbA1c < 6.5% or as medication usage; therefore, patients with diabetes who were on dietary modification alone with satisfactory HbA1c may have been misclassified as nondiabetic patients.Fasting glucose is an independent predictor of poor neurological outcomes in patients with acute ischemic stroke, with more predictive power than that of admission random glucose and HbA1c. The predictive value of glycemic measures for poor neurological outcomes in acute ischemic stroke did not differ significantly between the nondiabetic patients and those with diabetes.10.7717/peerj.2948/supp-1Supplemental Information 1Click here for additional data file.


All samples were analyzed by high pressure liquid chromatography (HPLC). Final serum lutein in the supplemented group was 5 times higher than in the unsupplemented group. All brain regions examined showed a selective increase in lutein deposition in the supplemented infants. Lutein differentially accumulated across brain regions, with highest amounts in occipital cortex in both groups. β-carotene accumulated, but zeaxanthin and lycopene were undetectable in any brain region. Supplemented infants had higher lutein concentrations in peripheral retina but not in macular retina. Among adipose sites, abdominal subcutaneous adipose tissue exhibited the highest lutein level and was 3-fold higher in the supplemented infants. The supplemented formula enhanced carotenoid deposition in several other tissues. In rhesus infants, increased intake of carotenoids from formula enhanced their deposition in serum and numerous tissues and selectively increased lutein in multiple brain regions.Lutein is the predominant carotenoid in the developing primate brain and retina, and may have important functional roles. However, its bioaccumulation pattern during early development is not understood. In this pilot study, we investigated whether carotenoid supplementation of infant formula enhanced lutein tissue deposition in infant rhesus macaques. Monkeys were initially breastfed; from 1 to 3 months of age they were fed either a formula supplemented with lutein, zeaxanthin, β-carotene and lycopene, or a control formula with low levels of these carotenoids, for 4 months ( Lutein, a yellow xanthophyll pigment, cannot be endogenously synthesized. Therefore, humans depend on dietary sources of this carotenoid, especially from green leafy vegetables, like kale, spinach, and broccoli . Lutein Emerging evidence suggests that lutein plays important roles in early human development. Lutein was the most abundant carotenoid in cord plasma, and its concentration was strongly correlated with maternal plasma lutein, suggesting a possible role of lutein in the neonatal period . Lutein Lutein is poorly absorbed and present in negligible levels in the retina and brain of most experimental animal models. Amongst non-human animals, only primates selectively accumulate lutein and zeaxanthin in their retina/macula and brain . Li et aAll procedures for this specific study were approved by the Institutional Animal Care and Use Committee of Oregon Health and Science University (OHSU IACUC Protocol IS3766) and carried out in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.Macaca mulatta) were group-housed with their dams from birth until 1–3 months of age; the dams were fed Monkey Diet Jumbo 5037  supplemented with a variety of fresh fruits and vegetables. The infants were then weaned and formula-fed according to detailed protocols for nursery rearing at ONPRC. One male and one female, starting at 27 and 86 days old, respectively, were fed a formula  supplemented with lutein, zeaxanthin, β-carotene, and lycopene, and one male and one female, starting at 63 and 26 days of age, respectively, were fed an unsupplemented formula with low levels of these carotenoids. The carotenoid profiles of the two formulas are provided in Two male and two female rhesus monkeys (Fasted 1 mL blood samples were collected in Ethylenediaminetetraacetic acid (EDTA) and processed to obtain plasma at baseline (before the first formula feeding) and after two and four weeks of formula feeding. After four months (133–135 days) of formula feeding, at ages 162 and 221 days for the supplemented group and 160 and 196 days for the unsupplemented group, infant monkeys were humanely euthanized by a veterinary pathologist under deep pentobarbital anesthesia, in accord with the recommendations of the Panel on Euthanasia of the American Veterinary Medical Association.g for 15 min to obtain serum. Tissues for carotenoid analysis were collected rapidly, placed in cryotubes and frozen in liquid nitrogen. From the brain, samples (approximately 0.5–1 g each) were dissected from prefrontal cortex, occipital cortex, superior temporal cortex, striatum, hippocampus and cerebellum. From each retina, 4 mm biopsy punches were used to obtain a macular sample centered on the fovea and samples of the peripheral retina; the vitreous was removed by blotting with filter paper, and the neural retina was gently dissected from the underlying retinal pigment epithelium and choroid.At the time of death, fasting blood samples were drawn and centrifuged at 800× All extractions and analyses were performed under yellow light to prevent light-induced damage of carotenoids. All extracts were stored under argon at −20 °C for less than 48 h before high pressure liquid chromatography (HPLC) analysis.w/v in methanol) and the mixture was vortexed for 15 s. Five mL of tetrahydrofuran (THF) was added and the mixture was vortexed for 1 min. Ten mL of extraction solvent  containing 0.005% butylated hydroxytoluene (BHT) was added, vortexed for 1 min, and centrifuged at 800× g for 15 min. The upper organic layer was transferred into a test tube. To the bottom layer, 10 mL of extraction solvent was added and the extraction process was repeated one more time. The extract was pooled and evaporated under nitrogen. To the dried extract were added 3 mL deionized water and 3 mL ethanol. This mixture was vortexed for 2 min, sonicated for 3 min and centrifuged at 800× g for 5 min at 4 °C. The upper layer was transferred to a 12 × 75 mm test tube and dried under nitrogen in a water bath (40 °C). The dried extract was reconstituted with 50 μL ethanol, vortexed, and sonicated for 30 s. Twenty μL of the reconstituted extract was analyzed by HPLC using a semibore C 30 column [To 2 mL of formula was added 10 mL 5% KOH  for 10 min. The upper hexane phase was removed and reserved. The hexane extraction process was repeated two more times. Hexane was pooled, dried using a Speedvac concentrator , and evaporated to dryness under argon. Recovery of the internal standard averaged 80%.Brain carotenoids were extracted according to the method of Vishwanathan et al. . Brain sv/v) and 100 ng echinenone  in ethanol were added. After vortexing for 30 s, the phases were separated by centrifugation at 800× g at 4 °C  for 15 min. The lower chloroform layer was transferred and evaporated to dryness under argon. The extraction was repeated using 3 mL hexane. The hexane layer was combined with the first extract and evaporated to dryness under argon. Recovery of the internal standard averaged 91%.Retina carotenoids were extracted according to the method of Vishwanathan et al. . The retg at 10 °C  for 10 min, the lower phase was collected and evaporated to dryness under argon. Ethanol-KOH  and 100 μL of freshly prepared ethanol-pyrogallol  were added to the dry residue and vortexed for 1 min. The mixture was incubated at 37 °C for 90 min for saponification. After incubation, 1 mL distilled water, 100 ng echinenone dissolved in ethanol, and 3 mL hexane were added. Carotenoids were extracted by hexanes using the above-described steps. Recovery of the internal standard was 100%.Carotenoids were extracted from adipose tissues according to the method of Sy et al. . BrieflyThe extraction method used for other tissues has been previously described . Samplesv/v) for brain, retina, and adipose tissue extracts or 40 μL of mobile phase B for serum and the other tissues. The extracts were separated on a reverse-phase C 30 column  maintained at 18 °C. A phase gradient method used for carotenoid separation was based on the method of Yeum et al. [All analyses were carried out on an Alliance HPLC system (e2695 Separation Module) equipped with 2998 photodiode array detector . Sample extracts were reconstituted with 100 μL of ethanol: methyl tert-butyl ether mixture . Correlations between two variables were determined by Pearson’s correlation coefficient.The carotenoid profiles of the two formulas are presented in Lutein and β-carotene concentrations in the prefrontal cortex, occipital cortex, superior temporal cortex, striatum, cerebellum, and hippocampus after 16 weeks on infant formula are shown in Lutein and zeaxanthin concentrations in each retinal region are presented in r = 0.98, p = 0.02), but not with 4 mm macular retina lutein .Across all four subjects, serum lutein was strongly correlated with peripheral retina lutein , abdominal subcutaneous (ASAT), thigh subcutaneous (TSAT), and axillary brown adipose tissues (BAT) are shown in Among dietary carotenoids, lutein is preferentially deposited in the human infant brain and retina, a finding that suggests a role in the development of these tissues ,22. AlthInterest in the potential role of lutein in brain development has been prompted by studies showing that this carotenoid is selectively accumulated in the brain of infants, as well as adults ,7. A priIt is interesting to note that lutein concentration in the lutein-supplemented group was increased in the peripheral retina, but not in the 4 mm macular retina. We also observed a correlation between serum lutein and peripheral retina lutein, but not between serum lutein and macular retina lutein. One possible explanation is that lutein was preferentially deposited in the macular region in the first few weeks of life, prior to the initiation of formula feeding, whereas lutein in the peripheral retina continued to increase over the next four months in the supplemented group. Johnson et al. previously showed that lutein in both foveal and peripheral retina was increased by a high level of lutein supplementation in adult monkeys initially devoid of xanthophylls . Many huInterestingly, we observed that lycopene, a non-hydroxylated carotene transported mainly by low-density lipoprotein (LDL) ,37, was It is also noteworthy that lutein’s distribution is not homogenous among adipose depots. This result parallels a previous study showing site-specific lutein distribution in the subcutaneous adipose tissues in healthy human subjects . One pos2O2 and superoxide anions, decreasing the level of intracellular H2O2 accumulation and thereby inhibiting the NF-κB pathway [Lutein has both antioxidant and anti-inflammatory effects in vitro and in vivo. Considering that infants can be at special risk of oxidative stress, lutein’s role as an antioxidant may be essential in early life. In an in vitro model of LPS-stimulated macrophages, lutein scavenged H pathway . Similar pathway . Supplem pathway . In addi pathway . CollectIn conclusion, in our small pilot study we found that increased early exposure to dietary lutein leads to enhancement of lutein tissue deposition. Notably, we found differential deposition of lutein in brain and adipose areas. Additional studies will be required to determine if lutein has a different half-life across different brain areas and other tissues and, more importantly, if lutein has a functional role in development of the brain, retina, and other organ systems.


The prevalence and consequences of prediabetic dysglycemia and undiagnosed diabetes is unknown in patients with heart failure (HF) and preserved ejection fraction (HFpEF) and has not been compared to heart failure and reduced ejection fraction (HFrEF).We examined the prevalence and outcomes associated with normoglycemia, prediabetic dysglycemia and diabetes (diagnosed and undiagnosed) among individuals with a baseline glycated hemoglobin  measurement stratified by HFrEF or HFpEF in the Candesartan in Heart failure Assessment of Reduction in Mortality and morbidity programme (CHARM). We studied the primary outcome of HF hospitalization or cardiovascular (CV) death, and all-cause death, and estimated hazard ratios (HR) by use of multivariable Cox regression models.p value for trend across groups < 0.001. Dysglycemia was also associated with worse outcomes in HFrEF.HbA1c was measured at baseline in CHARM patients enrolled in the USA and Canada and was available in 1072/3023 (35%) of patients with HFpEF and 1578/4576 (34%) patients with HFrEF. 18 and 16% had normoglycemia (HbA1c < 6.0), 20 and 22% had prediabetes (HbA1c 6.0–6.4), respectively. Finally among patients with HFpEF 22% had undiagnosed diabetes (HbA1c > 6.4), and 40% had known diabetes (any HbA1c), with corresponding prevalence among HFrEF patients being 26 and 35%. The rates of both clinical outcomes of interest were higher in patients with undiagnosed diabetes and prediabetes, compared to normoglycemic patients, irrespective of HF subtype, and in general higher among HFrEF patients. For the primary composite outcome among HFpEF patients, the HRs were 1.02 (95% CI 0.63–1.65) for prediabetes, HR 1.18 (0.75–1.86) for undiagnosed diabetes and 2.75 (1.83–4.11) for known diabetes, respectively, These findings confirm the remarkably high prevalence of dysglycemia in heart failure irrespective of ejection fraction phenotype, and demonstrate that dysglycemia is associated with a higher risk of adverse clinical outcomes, even before the diagnosis of diabetes and institution of glucose lowering therapy in patients with HFpEF as well as HFrEF. People with diabetes have a higher risk of developing incident heart failure (HF) than those without diabetes –3 and amRecently, both undiagnosed diabetes and prediabetic dysglycemia were also found to be common in patients with HF with reduced ejection fraction (HFrEF) and each was associated with worse outcomes, compared with normoglycemia, although the risk was not as high as in patients with diagnosed diabetes . The aimThe CHARM-Programme consisted of one HFpEF trial and two HFrEF trials. CHARM-Preserved enrolled 3023 patients 18 years or older, in York Heart Association (NYHA) functional class II–IV, with a prior hospitalization for a cardiac reason and a left ventricular ejection fraction (LVEF) above 40% . We cateWe used multivariable Cox proportional hazards models to evaluate the primary composite outcome of CHARM, which was death from cardiovascular (CV) causes or a hospitalization for HF, as well as all-cause mortality, according to glycemic status. Analyses were adjusted for age, sex, treatment arm, ejection fraction, NYHA class, heart rate, systolic blood pressure, body mass index (BMI), history of coronary artery bypass grafting, percutaneous coronary intervention, implantable cardioverter defibrillator, stroke and atrial fibrillation.HbA1c was available in 1072/3023 (35%) of the patients in CHARM-Preserved and 428 patients with a HbA1c measurement in this trial had a history of diabetes (Table HbA1c was available in 1578/4576 (34%) of individuals in CHARM-Alternative and CHARM-Added, and 558 patients in these trials with a HbA1c measurement had a history of diabetes Table . SimilarOnly 18% of patients with HFpEF and 16% of patients with HFrEF were normoglycemic.p = 0.25).Prediabetes was more common than normoglycemia in both types of HF: 20% in patients with HFpEF and 22% in those with HFrEF (p = 0.01). Conversely, the prevalence of known diabetes was higher in patients with HFpEF . As a result, the prevalence of any diabetes (diagnosed and previously undiagnosed) was 62% in each study.The prevalence of undiagnosed diabetes was also high, but was less common in patients with HFpEF compared with HFrEF  in CHARM than in those with HFrEF (35%). However, the novel data in the present report relate to the prevalence of undiagnosed diabetes and prediabetic dysglycemia, each of which were more common than a normal HbA1c in both HFpEF and HFrEF. Indeed, when undiagnosed and diagnosed diabetes were combined, a remarkable 62% of patients with each type of HF had diabetes. An additional fifth or so of patients had prediabetes, leaving just approximately one in six patients with a normal HbA1c, an observation that was true for both HFrEF and HFpEF. In comparison 26% had a normal HbA1c and 49% had either diagnosed or undiagnosed diabetes in the Prospective Comparison of ARNI with ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure (PARADIGM-HF), and corresponding numbers were 30 and 41%, respectively, in the Gruppo Italiano per lo Studio della Sopravvivenza nella Insufficienza Cardiaca-Heart Failure (GISSI-HF) trial . One expAs was recently reported for patients with HFrEF, undiagnosed diabetes and prediabetic dysglycemia in patients with HFpEF (and HFrEF) in the present study were associated with worse outcomes than observed in patients with a normal HbA1c. Although this pattern was very similar to that reported in the PARADIGM-HF trial, no elevated risk was reported for patients with prediabetes in GISSI-HF. The difference in risk in the present study was not statistically significant, probably because of the smaller number of subjects with a HbA1c measurement in CHARM . AlthougLike PARADIGM-HF, our study has the limitation of a single HbA1c value without a confirmatory measurement and the additional limitation of only including patients from North America where the prevalence of diabetes is higher than in other geographic regions, which may introduce a selection bias and impair applicability of our results to other regions of the world.However, even with these limitations, our data confirm the remarkably high prevalence of dysglycemia in HFrEF and show that a similarly high prevalence is found in HFpEF . In both types of HF, dysglycemia is associated with a higher risk of adverse clinical outcomes, even before the diagnosis of diabetes. These findings raise questions about the potential value of screening (for undiagnosed diabetes) and treatment targeted at correcting dysglycemia in patients with both HF phenotypes .


Body mass losses reached 2.5 ± 0.2% and serum osmolality 299 ± 5 mOsmol.kg−1 in DEH. A significant trial by time interaction was observed for IOP (p = 0.042), indicating that over the duration of the trials IOP declined to a greater extent in the DEH compared with the CON trial. Compared with baseline measurements IOP was reduced during DEH  but remained stable in CON (150 min: −0.3 ± 2.4 mm Hg). However, using an IOP value of 13.2 mm Hg to predict a 2% body mass loss resulted in only 57% of the data being correctly classified (sensitivity 55% and specificity 57%). The use of ΔIOP (−2.4 mm Hg) marginally improved the predictive ability with 77% of the data correctly classified . The present study provides evidence that the large inter-individual variability in baseline IOP and in the IOP response to progressive dehydration, prevents the use of IOP as an acute single assessment marker of hydration status.Current hydration assessments involve biological fluids that are either compromised in dehydrated individuals or require laboratory equipment, making timely results unfeasible. The eye has been proposed as a potential site to provide a field-based hydration measure. The present study evaluated the efficacy and sensitivity of intraocular pressure (IOP) to assess hydration status. Twelve healthy males undertook two 150 min walking trials in 40°C 20% relative humidity. One trial matched fluid intake to body mass loss  and the other had fluid restricted . IOP (rebound tonometry) and hydration status  were determined every 30 min. Body mass and serum osmolality were significantly ( Current best-practice human hydration assessments include osmolality of blood, saliva, or urine; specific gravity or color of urine; and changes in body mass compared to a baseline collected over several days  between IOP and serum osmolality was observed , and change in body mass (r = 0.507).To date only two studies have assessed IOP over a prolonged duration and/or in a hot environment where an individual would experience significant body mass losses using different methods of IOP assessment. The first involved a 24 h march  where IOP progressively declined for the first 15 h, at which time serum osmolality peaked  with normal ocular health as confirmed by an optometrist volunteered to participate. Exclusion criteria included any history of ocular disease involving raised eye pressure (or existing glaucoma or ocular hypertension).Twelve healthy, physically active males (mean ± SD): age 24 ± 2 year, height 178 ± 6 cm, mass 75 ± 7 kg, Participants were required to attend the laboratory on three occasions. The first laboratory visit involved eye testing, to determine high contrast visual acuity (Snellen chart) and health of the anterior and posterior eye  by an experienced optometrist. The first visit also involved the determination of maximal aerobic power by an incremental treadmill running test to exhaustion and skin fold assessment of body composition, as previously described  between 4 and 10 pm the night before each session, and a further 250 mL of fluid the morning of the trial . The participants were also given a calibrated . Participants with a USG value < 1.020 were classified as euhydrated  and those with higher values  were provided with an additional 500 mL of water to be consumed prior to the commencement of the walking trials. A chest strap  and data logger  were then fitted to provide continuous heart rate and core temperature recordings, respectively.Participants were then seated and a cannula was inserted in the left antecubital fossa to attain venous blood samples. Following at least 10 min of seated rest IOP and blood pressure from the right arm, using the auscultatory method, were obtained and blood samples drawn. Intraocular pressure was measured by an optometrist using a handheld contact (rebound) tonometer . The device measures the IOP in <0.1 s and averages six readings to minimize deviation and to produce a calculated measurement value. The IOP measurement was performed in duplicate (triplicate if difference was > 1 mm Hg) for the right eye only  and commenced walking at 5 km·h−1 and 1% gradient with core temperature and heart rate recorded and monitored continuously. Following 30 min the participants were removed from the environmental chamber into an air-conditioned laboratory and had 10 min of seated rest, after which IOP, blood pressure, blood collection, and nude body mass (after towel drying) were determined, in that order. This was repeated five times for a total of 150 min walking which equated to a total distance of 12.5 km for all participants.Participants then entered the environmental chamber  water was provided in the first 30 min walking bout and in the remaining four walking bouts water provision was equated to the body mass loss in the preceding walking bout. To ensure the fluid consumption had no subsequent effect on the measurement of IOP all fluid was consumed within the first 10 min of the walking bout  and inferential statistics (Shapiro–Wilk Test). Continuous variables were summarized as mean ± standard deviation (unless otherwise stated). A two way repeated measures analysis of variance (ANOVA) was performed to assess the effects of time  and trial (DEH and CON) on IOP, indicators of hydration status, heat strain, and blood pressure variables. Post-hoc analysis, using a Bonferroni correction, were conducted where appropriate. A Pearson's correlation coefficient was determined to observe the relationship between IOP and indicators of hydration status, heat strain, blood pressure and lactate across all trials and time points. Where a statistically significant relationship was observed, a univariate general linear model, with participant ID as a random effect, was utilized to determine statistical significance. This was to account for the within-participant correlation likely present within the data (due to repeated measures), and provides an average equation of the linear association from the association within each participant. Confidence intervals around the slope of the line were calculated using the t statistic for eleven degrees of freedom. Finally, the sensitivity and specificity of IOP and ΔIOP to identify a 2% loss in body mass, in accordance with the ACSM Position Stand in Exercise and Fluid Replacement , serum osmolality, core temperature, heart rate, mean arterial pressure and blood lactate were similar (p < 0.001) body mass losses and increases in serum osmolality compared with the CON trial . No significant differences were observed in mean arterial pressure or blood lactate concentration, however heart rate and core temperature were significantly elevated (p < 0.05) in the DEH trial at the 120 and 150 min and 90, 120, and 150 min time points, respectively . A significant main effect for time (p < 0.001) and trial by time interaction was observed for IOP  were observed between IOP and body mass loss (r = 0.181), blood pressure (r = 0.501), and blood lactate (r = 0.190). As such these variables were entered into a univariate general linear model as covariates  with IOP as a dependent variable and participant number as a random factor, to account for the repeated measurements. Only body mass loss was found to be significantly associated , time (p < 0.001) and their interaction (p = 0.020). Significant post-hoc comparisons, adjusted for multiple comparisons, were observed at 30, 90, 120, and 150 min , serum osmolality (r = −0.385) and core temperature (r = −0.314). Univariate general linear model revealed a significant association for ΔIOP with body mass loss, serum osmolality and core temperature , this relationship became insignificant when corrected for repeated measurements within each participant  but not between absolute IOP and core temperature   or ΔIOP  with heart rate. Our findings are supported by other researchers who have previously observed no relationship between heart rate and IOP  Table . Heart rAlthough the current data suggest an association between IOP and hydration status, there is limited potential for IOP to be used as a simple and practical technique to indicate hydration status in non-clinical settings . A body mass loss of 2% was chosen as a criterion level of hypohydration, as this level has previously been associated with decrements in physical endurance performance, increased heat strain, and increased risk of developing heat illness (Armstrong et al., Despite the high individual variability in IOP a decline during the exercise-induced hypohydration was observed in all the participants. Therefore, we examined the use of a change score, from baseline, as a potential indicator of a change in hydration status. Using the relationship between body mass loss and ΔIOP from baseline, a 2% body mass loss corresponded to ΔIOP of −2.4 mm Hg and slightly improved the classification accuracy to 77% Figure  and the In conclusion, IOP is progressively reduced during exercise-induced hypohydration, but remains stable if hydration is maintained during exercise in the heat. The present study provides novel evidence to suggest that IOP is significantly correlated to hydration status, likely due to the effect of a rise in serum osmolality on the rate of formation of aqueous humor. However, large inter-individual variability in baseline IOP and in the IOP response to progressive dehydration prevent IOP use, as measured by rebound tonometry, as an acute single assessment marker of hydration status.IS and JC conceived and designed the research, analyzed the data and drafted the manuscript. BD, DB, and AB performed the experiments. IS, JC, and BF interpreted results of the experiments. All authors edited and revised the manuscript. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Felis silvestris silvestris). Array use in trait association mapping was investigated under different modes of inheritance, selection and population sizes. The efficient array design and cat genotype dataset continues to advance the understanding of cat breeds and will support monogenic health studies across feline breeds and populations.The development of high throughput SNP genotyping technologies has improved the genetic dissection of simple and complex traits in many species including cats. The properties of feline 62,897 SNPs Illumina Infinium iSelect DNA array are described using a dataset of over 2,000 feline samples, the most extensive to date, representing 41 cat breeds, a random bred population, and four wild felid species. Accuracy and efficiency of the array’s genotypes and its utility in performing population-based analyses were evaluated. Average marker distance across the array was 37,741 Kb, and across the dataset, only 1% (625) of the markers exhibited poor genotyping and only 0.35% (221) showed Mendelian errors. Marker polymorphism varied across cat breeds and the average minor allele frequency (MAF) of all markers across domestic cats was 0.21. Population structure analysis confirmed a Western to Eastern structural continuum of cat breeds. Genome-wide linkage disequilibrium ranged from 50–1,500 Kb for domestic cats and 750 Kb for European wildcats ( The domestic cat, Felis silvestris catus, represents one of the ~41 species in the family Felidae6 with the extant species having a common ancestor ~11 million years ago8. Previous archeological and genetic research has suggested the modern domesticated cat descends from at least one wildcat progenitor subspecies, Felis silvestris libyca, around 10,000 years ago11.Feral and owned cats are collectively referred to as “domestic cats”. Over 88 million domestic cats live in homes in the USA alone13. The availability of grains and other food sources in and around areas of human settlements resulted in substantial rodent population expansion, which in turn attracted the natural predator, the progenitor of the domestic cat, from the wildcat population. Over time, individual cats with temperaments suitable for co-habitation with human populations became isolated from the wild counterparts and evolved into the semi-domesticated cat of today. In spite of their rapid spread and isolation from the progenitor populations, domestic cats have remained remarkably similar to their felid cousins (Felis silvestris subsp.) in form and behavior14 and these wild populations have remained widespread across the Old World.Agricultural development is thought to be the key event that initiated and influenced the domestication of the cat16. All these desired qualities are the products of hundreds to thousands of years of selective breeding13. However, the domestic cat breeds were selectively bred primarily for aesthetically pleasing traits such as coat color, length, and texture, most of which occurred only in the past 150 years18. Between 40 and 55 different cat breeds are currently recognized for standardized phenotypic characteristics by worldwide cat fancy associations, including the Cat Fanciers’ Association19, The International Cat Association20, the Governing Council of the Cat Fancy21, Federation International Feline22, and the World Cat Federation24. Due to inbreeding, many cat breeds harbor heritable diseases that are important biomedical models for human health (http://omia.angis.org.au/home/)25. However, owned random-bred and un-owned or semi-owned feral cats represent the overwhelming majority of cats in the world26.The establishment of cat breeds from domesticated and tamed free-roaming cat populations is a relatively recent event. Many domesticated animal species such as cattle, goats, pig, dog, and horse, were selected for traits of economic value such as meat, milk, drought tolerance, endurance, strength, protection, hunting ability, speed and metabolic efficiency from the onset of their domestication29. Similarly, genetic resources for the domestic cat have methodically and systematically been developed, which include somatic cell hybrid panels31, radiation hybrid maps39, genetic linkage maps44, and the sequencing of the cat genome48. Feline genome sequencing efforts to date have included: (1) a 1.9x draft sequence as a representative of the family Felidae45, (2) additional light sequencing (~1X coverage) of six individuals from several breeds and an African wild cat (Felis silvestris cafra) for SNPs discovery47, (3) high throughput sequencing of four pooled samples from each of six different domestic cat breeds, wildcats, as well as the reference cat genome46 and (4) a high-resolution SNP array-based linkage map that supported the assembly of Felis_catus v8.048. The SNPs discovered via these sequencing efforts were used to construct an Illumina Infinium iSelect 63K DNA cat array. The produced array contains 62,897 variants that enable genome-wide case–control association studies and population-based investigations for cats rather than focusing only on pedigree analysis and candidate gene-based approaches.The continued development and progress of genetic resources for humans have transformed the field of genetics and accelerated the rate of scientific discoveryUsing an extensive dataset of over 2,000 cats genotyped using the feline SNP array, this study had two main objectives: firstly, to evaluate the array’s accuracy and efficiency for genome-wide genotyping that included validation tests of (1) remapping SNP physical positions to the newest cat genome assembly, (2) SNP genotyping rate, (3) SNP Mendelian inheritance, and (4) allelic variability across breeds, and secondly, to test the reliability of the array’s genotype data for population based analyses. The population-based analyses included assessments of (1) genetic diversity, (2) population structure, (3) linkage disequilibrium, and (4) association mapping.45 and the improved assembly by re-sequencing47 (FelCat4 (Felis catus 5.8)), ~10 million polymorphic variants, were submitted for design to produce a low density Illumina Infinium iSelect DNA array. SNPs that represented all known cat phenotypes and diseases at the time were submitted, as well as SNPs unique to a single assayed wildcat (Felis cafra)47. The final design  included 59,469 autosomal SNPs, 2,724 X-linked SNPs  were identified in the Felis_catus_8.0 genome assembly, including 2,724X chromosome markers. The remaining 704 variants were assigned to chromosome 20, representing unknown chromosome locations , two tigers (Panthera tigris), nine leopard cats , and 60 European wildcats (Felis silvestris ssp). Three breeds were sampled from familial lineages for pedigree studies, including Birman52, Lykoi, and Tennessee Rex. The samples from a mixed-breed research colony of known matings were included to support segregation analyses of the SNPs49. Each sample had a genotyping rate ≥90%. Complete genotype data is found in Supplementary Data File 46. The multi-dimensional scaling (MDS) clustering (see below) suggested the sample provided and identified as a Burmese (Pixel) was switched with the sample identified as a Cornish Rex (Tipper)47.Table 49. All samples represented in the trios exhibited Mendelian errors in ≤2% of the markers, supporting familial relationships. Marker-specific Mendelian errors were identified for 232 SNPs, and each showed ≥10% errors  had a genotyping rate >90%. The array’s SNPs n = 62,897) were evaluated for genotyping quality. Only ~1% (n = 625) of the SNPs were missing ≥10% of genotypes and were therefore excluded from downstream analysis  samples. Twenty cats were genotyped in replicas, including four samples replicated from the same DNA aliquot but genotyped on different arrays, one sample as a whole genome amplification, two samples represented by tumor tissue of the genotyped cat, and 13 samples replicated as part of separate studies from different DNA aliquots. SNP mismatches between repeated samples were calculated after removing SNPs with a genotyping rate ≤90% and after removing SNPs with Mendelian errors. The average mismatch between samples repeated from the same aliquot of DNA was 0.14%, ranging from 0 to 0.55%. However, the sample with the highest mismatches was a commonly used cat cell line . The whole genome amplified DNA had 2.62% mismatches from the non-amplified DNA sample. The two samples represented by the tumor versus non-tumor tissue had 0.69% and 1.06% mismatches. The replicated samples from different DNA aliquots had an average of 0.48% mismatches, ranging from 0.07% to 0.85%  across all samples. None of the SNPs with low genotyping rates were of wildcat origin. Only 752 SNPs were monomorphic in all genotyped individuals, with the highest number of monomorphic SNPs on chromosome A1 (n = 89) and the lowest on chromosome E3 (n = 11)  exhibited very low polymorphism and only 1,754 SNPs were polymorphic. No genotypes were obtained from 3,733 SNPs for the large wild felids (BIGW). Asian Leopard cats  were polymorphic for 3,547 SNPs. The European wildcats (n = 60) possessed a considerably higher number of polymorphic markers . For the wildcat-specific SNPs, 2,576 of 4,240 (61%) were polymorphic with a MAF ≥ 0.05 within the domestic cats. In the Pantherine, 116 (2.7%) wildcat-specific SNPs were polymorphic.Four wild felids that were genotyped represent the most distant lineage from the domestic cat, including two lions and two tigers both from the FIS) for the cat populations ranged from −0.12 for the Lykoi and American Wirehair breeds to 0.12 for the Burmese. Random bred domestic cats had an (FIS) of 0.096.Breed-specific population summaries are presented in Table To visualize the relationship within and among cat breeds, all of 2,078 cats were assessed for population structure by multi-dimensional scaling (MDS). The MDS was performed using the 62,272 SNPs that had a call rate ≥90%. To illustrate breed structure, key breeds are highlighted in Fig. 53 analysis. Approximately 99.99% of the genetic variation (K∅c statistic in fastSTRUCTURE) among the twenty cat breeds and two wild cat (F. silvestris and F. libyca) was explained by a K = 19  between pairs of autosomal SNPs on each chromosome, independently. Only SNPs with MAF ≥ 0.05 were included in the analysis for each breed, separately, therefore the number of markers varied between breeds  was measured using the squared correlation coefficient  population was used as the cutoff point and the r2 value of comparison. Genome-wide LD among cat breeds ranged from 50 Kb in Munchkin, Siberian and Turkish van to a maximum of ~1,500 Kb in Birman cats. , in the presence or absence of artificial selection and allowed a comparison of association of the causative variant and adjacent SNPs. The SNPs associated with each trait and Pgenome values after permutation testing are presented in Table To evaluate the power of the feline array for localizing traits via association analyses in cats, four aesthetic traits were chosen based on sufficient phenotypic documentation in the dataset. Of the four traits, three are inherited in an autosomal recessive fashion, specifically coat color loci Dense (a.k.a Dilute coat color), a trait not under selection in random bred cats , which is the causal variant within Melanophilin (MLPH)54 54 Fig. . For theDense association in random bred cats without selection, 30 cases and 56 controls were used to perform the same GWAS within the Burmese breed and 60 cases and 41 controls within the Birman breed with selection for the trait. Several SNPs detected association together with the causal variant . While the analysis with random bred samples showed an association only with the causative variant, Burmese exhibited a ~150 Kb haplotype block  and Birman had a ~60 Kb haplotype block  across all cases. This comparison showed how an association analysis within a breed with positive selection for a trait is likely to be more successful than in a random bred population. Furthermore, while achieving an association of the closest marker with the causative variant in random bred cats requires significantly increasing the number of samples or markers, an association can be detected in Burmese even when reducing the number of samples from 101 to 37 (13 cases and 24 controls); the most significantly associated SNP remained statistically significant  after permutations (Pgenome 0.009). In Burmese, a unique haplotype of ~4.5 Mb containing the causal variant for the phenotype was detected across all cases while in Birman a unique haplotype of ~200 Kb containing the causal variant was identified in all cases. The SNP composition of the Birman and Burmese haplotypes were different, including within the 200 Kb haplotype that is within the 4.5 Mb haplotype of the Burmese.In comparison to the Dense and the region of homozygosity for these cats extended 190 Kb in Chartreux and Russian blue and 280 Kb in Korat.The Chartreux, Russian blue, and Korat breeds are fixed for the variant in 20. However, only the curly coat texture is consistently selected in the breed while the longhair variant is not under selection. LaPerm breed displayed low LD (100 Kb) and high polymorphism (7.5% monomorphic SNPs). Thirty-two cases (longhair) and 22 controls (shorthair) of the LaPerm breed were selected to perform a GWAS for the longhair trait. (Table fibroblast growth factor 5 (FGF5)59, which is located on chromosome B1 46. The FGF5 causative variant was the most significantly associated with the hair length phenotype , in addition to several other adjacent SNPs  locus within Tyrosinase (TYR) and have a darker coat color on the ears, face, paws and tail55. Using pointed (cscs) Persian cats  as cases and non-pointed Persian cats as controls  and controls (69) from multiple breeds from the dataset as previously described in Gandolfi et al.61. Orange was localized to the X chromosome by allelic association , and by Cochran-Mantel-Haenszel test (CMH) to position 107,777,134 with raw Pvalue = 4.4e−5 . A haplotype block was detected from position 106,241,242 to position 107,745,900 (~1.5 Mb) of the X chromosome , canine (49.6K) and bovine (58.3K) arrays66. This low density array is highly suitable for Mendelian trait analyses, particularly in cat breeds.Low-density genotyping arrays are available for a variety of species. The design of the feline array benefitted from the results and outcomes from the designs for dogFelis catus 5.8). After SNP remapping to the latest feline genome assembly FelCat 8.0 (Felis catus 8), only 704 SNPs (1.1%) remained unassigned, a significant improvement from remapping to cat Felis_Catus_6.2 by Alhaddad et al.49, where 6,893 SNPs had unknown locations. Marker coverage on the X chromosome is not as robust, likely due to the complexity of the X chromosome and the high density of repetitive sequences67. The feline inter-marker average distance of 37.7 Kb is equivalent to cattle63 and denser than the horse array, which has a ~43 Kb inter-marker distance65. The cat, cow, and horse genomes  are roughly equivalent in size. Although the feline genome assembly contains several gaps (~40 Mb) and unplaced scaffolds46, the inter-marker distances suggest balanced and slightly better coverage of the cat genome than for other species with early lower density arrays. However, the 20 gaps >500 Kb in the cat SNPs is higher than horse, with only 12 gaps >500 Kb and cow, where the highest gap between SNPs is <350 Kb65.The position of the SNPs was based on the feline genome assembly FelCat 4  across ~2,000 samples, a low number of SNPs with Mendelian errors , leaving 62,051 robust SNPs for downstream analysis. The number of SNPs excluded for low genotyping rate and Mendelian transmission errors is lower than that of cow and horse 63.Moreover, the presence of duplicate controls confirms the high reproducibility of the genotypes, with a negligible number of errors between replicates from the same aliquot of DNA. Slightly higher mismatch rates were observed in tumor versus genomic DNA and a cell line, both likely due to somatic mutation heterogeneity. The error rate between WGA samples and the original sample was 2.62%. Thus, excluding SNPs from analyses with a MAF ≤ 0.03 instead of the typical 0.05 may be acceptable. The removal of poor quality SNPs did not significantly affect mismatch rates. The mismatch rate is 10-fold higher than reported in cattle63 and horses (0.24)65. Specifically, 2,628 SNPs (~4%) showed a MAF < 0.01 across all samples. This observed MAF is lower than other species, and is likely due to inclusion of SNPs that were specific to one wildcat species.The average MAF was variable across breeds, ranging from 0.11 for Korats to 0.22 for random bred cats. The average MAF of domestic cat populations was 0.18, which is lower than cows (0.26)47, the percentage of monomorphic SNPs was the highest, at ~31%. For Burmese, the low number of polymorphic SNPs confirms the high inbreeding coefficient in the breed and inbreeding history69. A high number of monomorphic SNPs were observed in the large wild felids of the genus Panthera , which is consistent with previous reports8. Even with limited numbers of polymorphic SNPs on the array for large wild felids, the remainder of polymorphic SNPs can be used for conservation and zoo management applications. A substantial number of SNPs (63.8%) are informative for European wildcats. These thousands of polymorphic markers may be useful for population and conservation studies, especially in wildcat subspecies70. However, the cat 63K array is unlikely to be useful for disease mapping studies in distant wild felids.Although a Burmese cat was used as part of SNP sequencing discovery panel69. The cat breeds displayed a continuum on the MDS plots, however, three main clusters are observed representing cat breeds with Western, Central and Eastern origins. The Western breeds were represented mainly by the Persian family71, clustering in the second and third dimension as well, confirming a strong Persian genetic influence in British shorthair, Selkirk Rex and Scottish Fold, and in agreement with previous STR and SNP based studies72. Previously unstudied breeds, such as American Curls and Peterbalds demonstrated their Western and Eastern origins, respectively.The MDS clustering and Structure analyses confirmed the known origins of the cat breeds and their relationships61 may be explained by the presence of a high number of related individuals that belong to mainly two big pedigrees of Birman cats. The Abyssinian breed clustered with the central origin breeds in the MDS that includes only domestic cats, specifically with Siberian in the 2nd and 3rd dimension. However, the close clustering with Siberian cats does not reflect the historical development of the breed. In previous studies, the Siberian breed was suggested to be genetically distinct from the other breeds68. The cross-bred Ocicat, an Abyssinian and Siamese hybrid, clustered in between the central and Asian breeds, showing both the European and/or Asian genetic influences73.Breeds with Eastern origins  are found at the opposite end of the MDS and showed shared ancestry. The Birman cats are strongly clustered but genetically distinct from other Eastern breeds. The difference between the Birman clustering compared to results from previous study73. The greatest difference of LD estimates  was found between 10 and 25 samples of random bred individuals. As a result, LD was calculated for breeds and populations represented by at least 20 individuals. Eight breeds  and random bred cats displayed LD estimates that were similar to previously published results73. In contrast, a substantial difference in LD is evident for Abyssinian and Birman cats, where the LD was 10 and 7-fold higher, respectively, using genome-wide data. A significant difference was also observed in Siamese, where the LD was estimated at almost twice as long (400 Kb vs 230 Kb) as detected in the previous study. The discrepancy in LD estimates for these breeds is likely related to the size of region and number of SNPs used. Overall, Eastern breeds tended to have higher levels of LD  relative to central and Western breeds.The present study represents the genome-wide LD estimation in cats and is in overall agreement with the previously reported estimates using selected regions75.The short LD of some cat breeds can be explained by (1) a large breeding population, such as Persian and Persian-derived breeds, (2) limited selection, whereby several possible coat colors are permitted , and (3) active outbreeding strategy (Munchkin), or random bred based breeds (Siberian). Persian and Persian-derived cats showed very similar levels of LD, as well as in Eastern breeds, such the Oriental Shorthair, which was used in the development of the Peterbald. The random bred population showed very low levels of LD, and breeds such as Munchkin, Siberian and Turkish Van displayed a haplotype structure similar to the random bred population, which is consistent with their breed history. Haplotypes length and LD levels also reflect the number of successful GWAS conducted in several cat breedsDense54 trait that is not under selection using 114 random bred cats. As expected, the association identified only the causative variant (c.83delT in Melanophilin (MLPH)), and association analyses using random bred samples will require a denser array or a larger number of samples. When the same trait was analyzed using two breeds (Burmese and Birman) where the trait is under selection only in certain lines, a large haplotype block was associated using substantially fewer samples (n = 37) compared with random bred cats.The main application of a high-density array is the localization of simple Mendelian diseases and traits of interest. Using the presence of phenotypic SNPs on the feline array, several association scenarios were conducted and the power of the array was examined by comparing the p-values and LD of genotyped phenotypic SNPs (causative) to that of the surrounding SNPs. The first scenario was a GWAS for the recessive FGF5 variant (c.475A > C) for Long fur length58. The LaPerm breed is defined by and selected for curly coat texture but exists in longhair and shorthair varieties20. Despite the absence of positive selection for the variant, along with low LD, and high polymorphism within the breed, a significant association was detected with SNPs linked to the FGF5 variant. Clearly, GWAS using cat breeds with traits under selection is more efficient than studies within random bred cats.The second scenario using LaPerm cats identified a significant association of the most common Color mutation c.940G > A within Tyrosinase (TYR)56. The TYR variant is under positive selection in Himalayan cats, which have low LD and low inbreeding. A significant association was detected by multiple SNPs linked to the genotyped TYR variant and a haplotype block is shared among Himalayan cats.The third scenario analyzed the association of the Orange locus60. The association analysis across breeds refined the region of association to a 1.5 Mb haplotype block. The region contains twelve genes, and after visual inspection of the genes and their function, a candidate was not apparent. Additional mapping efforts are required to refine the position of the locus and to identify candidate causal variant(s). This analysis, in addition to its contribution to refining the region of Orange, illustrates the efficiency of performing association analysis of X-linked traits, in random bred cats with no selection for the trait.The fourth scenario localized and refined the region of the unknown X-linked 76, compared to an estimated 200,000 to 500,000 SNP markers in humans77, making GWAS in dogs both cheaper and easier to conduct78. Considering both the Illumina Canine SNP20 and the Affymetrix Canine V 2.0 Platinum Panel array, many GWAS in canines have been conducted with ~30 cases and controls. More complex traits80 obviously require more samples and hence the development of higher density arrays. Transmission distortion testing (TDT) has been successful with only 7–13 discordant sib-pairs in canine studies82. The feline array has also proven its utility within breeds and supported the genetic dissection of simple84 and complex traits86. The array clearly shows significant association power for traits under selection or recessive traits. Examples of successful GWAS for diseases include the frontonasal dysplasia in Burmese84, congenital myasthenic syndrome in Devon Rex83, and hypokalemia in Burmese75. Identifying the curly hair variant of Selkirk Rex and the variant for folded ears in Scottish Fold are examples of dominant traits that are under positive selection74. A comparable number of cases and controls have been used in these cat studies with minimal cases required for studies in the breeds with the highest LD, such as the Burmese. Many cat breeds are younger in breed development, such as Siberians, or still represent indigenous populations, such as the Manx cats on the Isle of Man, hence an association study in breeds with low LD more likely requires a higher number of samples or a denser array to provide a statistically significant association while analyses of random bred populations likely requires a significantly denser array.Preliminary predictions of the strength of population structuring and high LD in dog breeds suggested only 5,000 to 30,000 SNP markers were required to achieve complete coverage of the dog genome48 that has supported the newer genome assembly, (2) an understanding of genetic variation within and between cat breeds72, (3) high resolution descriptions of genomic consequences of the selective sweeps84, and (4) a more fully refined comparative model for human biomedical research84.Beyond the successful GWAS approaches presented here and published before, the feline SNP array enabled (1) the development of a high density linkage mapAll data generated in the project is available in Supplementary information files included in the article for download.Sampling of cats for this study was approved by the Animal Care and Use Committee (ACUC) of the University of California, Davis (protocol # 16991) and the University of Missouri (Protocol # 7808) and samples were collected in accordance with the guidelines and regulations. Samples were acquired by specialists in the field, such as veterinarians, or voluntarily donated by owners and breeders.Felis silvestris cafra)47. The re-sequencing efforts identified over three million polymorphisms with 964K common SNPs suitable for the design of a domestic cat genotyping array and 849K SNPs were likely to have an informative minor allele frequency >5% across cat breeds. Additional SNPs were identified from four pooled individuals representing six breeds, including Birman, Egyptian Mau (n = 1), Japanese Bobtail, Maine Coon (n = 5), Norwegian Forest cat and Turkish Van. Random bred cats with Eastern and Western origins, as well as two Felis silvestris and two Felis libyca, also assisted SNP identification47. Over nine million SNPs were identified from the deep re-sequencing of the cat genome.SNPs were identified from one cat of each breed representing American Shorthair, Cornish Rex, European Burmese, Persian, Ragdoll and Siamese, as well as one South African wildcat , was used to estimate spacing between SNPs. After exclusion of SNPs based on minor allele frequency (<0.25), near or within a sequence repeat, within a duplicated region, or with more than two alleles, approximately 1 million SNPs were submitted to Illumina for design of the DNA array. A vast majority of the SNPs have a one bead assay design and were mainly targeted as single copy, intergenic and intronic SNPs.Felis_catus_8.0, the following analyses were performed. For each SNP, 100 bp of upstream and downstream sequence was aligned to Felis_catus_8.0 using the program blat87. The entire Felis_catus_8.0 reference sequence was used in the alignment rather than performing multiple alignments with separate chromosome sequences. The program was run in default mode to generate alignments, with a minimum of 11 bp of matching sequence to initiate an alignment (tileSize = 11) and at least 90% matching bases required (minIdentity = 90). The number of tile matches was 2 (minMatch = 2), the minimum score was 30 (minScore = 30), and the size of the maximum gap between tiles in a clump was 2 (maxGap = 2). The best matches were selected to determine the location of each pair of sequences  in the assembly and coordinates obtained. The remapped map file is available in Supplementary Data Felis_catus_6.2 position and the Felis_catus_8.0 position.To determine the exact coordinate of each variant in 75. The individuals from most populations were selected with minimal relationships 52, Lykoi, and Tennessee Rex breeds, as well as the Oriental/Toyger pedigree and colony cross-breed groups51, contained related individuals. The research colony cats were used for the segregation analyses49. PLINK88 was used to obtain the genotyping rate for each sample. Coat color, texture and fur length information were available for the majority of the samples genotyped.A dataset comprised of 2,078 samples from 47 different groups/populations were genotyped on the Illumina Infinium iSelect cat array  as previously described88. A dataset comprised of 2,078 samples were genotyped on the Illumina Infinium iSelect SNP array. SNPs with genotyping rate >90% across the dataset were identified using the command–geno 0.1.Quality control analyses for SNPs data were conducted using PLINK49. Using the function–mendel, percent Mendelian errors per individual sample and per SNP were estimated. SNPs exhibiting ≥10% Mendelian errors were reported as significant errors. The distribution of SNPs with errors was investigated for each chromosome. Male-specific Mendelian errors or SNPs located in the pseudo-autosomal region of the X chromosome were determined by examining heterozygous X-chromosome genotypes in males (n = 52). SNPs exhibiting 10% or more of an X-chromosome in the males were reported as likely pseudo-autosomal SNPs.A multi-generational cross-bred pedigree comprised of 86 trios  was used to determine marker-specific significant Mendelian errorsidentical) in R base. The number of instances where a mismatch was detected were counted and presented. The number of discordant genotypes for each duplicated sample was determined across all SNPs , after removing SNPs missing 10% genotypes , and after removing SNPs missing 10% genotypes and with Mendelian errors .Genotypic differences between replicates were analyzed for 20 samples. The genotypes of the original and the replicate samples were determined to be identical using the function  the number of monomorphic SNPs, and (2) the mean and standard deviation of minor allele frequency (MAF). (3) The mean and standard deviation of observed were obtained using the function–hardy. The number and frequency of all polymorphic SNPs  for a dataset containing all domestic cat breeds combined was determined using the PLINK function (–freq). The numbers of SNPs within different minor allele frequencies bins are reported.For each population independently, the following summary statistics were calculated using PLINK–het command in PLINK (v1.9) and the mean of the values for each population were reported.The observed heterozygosity and the inbreeding coefficient were both calculated per individual using88 using the–genome function. The genetic distances obtained were used to generate a multi-dimensional scaling of the genetic distances between individuals (using the command–mds-plot). Three dimensions were used to visualize the genetic population structure of breeds. Each population was plotted in relation to all other populations in three combinations of dimensions . The entire dataset was plotted and open circles were used to show the position of the populations. The circles represent a qualitative depiction of the position of a population and drawn as follows. For each population (A), the position of the circle was determined by mean (dimension1), mean (dimension2), whereas the radius of the circle was chosen using the largest of the standard deviations of (dimension 1 or 2). Each of the three combinations of dimensions  was plotted separately.To depict the genetic relationships between populations and individuals within each population, pairwise genetic distances between all individuals in the dataset were calculated in Plink53 (version 1.0). To reduce the effects of uneven sample sizes between populations89, only unrelated samples from twenty breed and two wildcat populations (n = 519), which are equal in size  were used in the analysis. The autosomal SNPs of all samples were used and SNPs with a MAF less than 0.01 (n = 1198) were removed, which resulted in 57,690 SNPs to be used in the analysis. fastSTRUCTURE53 was run to determine the genomic contribution of K (K = 1–20) hypothetical populations. Two outputted metrics were considered to determine the appropriate values of K: (1) the K that maximizes the log-marginal likelihood lower bound and (2) the minimum value of K that accounts for 99.99% cumulative ancestry.Additionally, the utility of the array data in identifying levels of population admixture was examined via fastSTRUCTUREr2 value) of comparison between breeds. The pairwise squared correlation coefficient (r2) was used as a measure of LD between any two autosomal markers on the same chromosome as previously described73. To assess the effects of sample size on the measure of the extent of LD, a dataset of domestic random-bred (DOM) cats (n = 270) was examined by randomly selected (without replacement) individuals to represent five populations of different sample sizes . For each of the DOM subgroups, r2 were calculated, as described above. The effect of the sample size was measured by comparing the r2 values between the five subgroups.To unbiasedly measure the genome-wide extent of linkage disequilibrium (LD) in cat breeds, a number of criteria were considered including, (1) the LD statistic, (2) number of individuals per breed, (3) degree of relatedness among individuals within a breed, and (4) the statistical point  and analyses were performed using Haploview90.As an outcome of the assessment of the effects of sample size on LD measure (see results), only the breeds represented by 20–30 unrelated individuals were used in the LD analysis. To ensure unbiased measure of LD due to relatedness, the individuals representing each breed were selected based on the lowest identity by descent (IBD) values. IBD values were obtained using the commandr2 estimates between autosomal markers on the same chromosome were jointly categorized into distance bins of 50 Kb. The range of distances between markers included in the estimation of the extent of LD was 50 Kb–4 Mb. In each distance bin, the mean LD estimate was used as the representative of the statistic. The decay of LD was determined by connecting the statistic r2 mean at every distance bin. To objectively report the extent of LD, the maximum value of r2 found in the random bred population was used as the r2 value of comparison. This r2 value represented lack of LD or extent of LD smaller than 50 Kb that is seen in the random bred population (DOM).Pairwise Dense coloration54, Long hair59 and points allele (cs) for the Color locus56 and one sex-linked trait, the Orange coloration locus92, were analyzed. The number of cats used in each analysis is listed in Table −assoc) were performed with PLINK88 using subsets of samples from the available 2,078 sample dataset. The GWAS to localize Dense was performed on three different datasets: random bred cats, Burmese and Birman. Haplotypes for the locus were identified by exporting genotypes from position 216 Mb to position 221 Mb of chromosome C1 and analyzed visually. Haplotypes were exported for each trait using PLINK88 5 Mb 5′ and 3′ of each causal variant and visually inspected. Haplotypes for Dense were exported in Chartreaux, Korat and Russian Blue, haplotypes for Color were exported in Birman, Burmese and Siamese and haplotypes for Long fur were exported for Maine Coon, Norwegian Forest cat, Persian, Ragdoll, Siberian and Turkish Angora in the sample sets used for the GWAS associations or on the available cats in the dataset.Three autosomal recessive traits in cats: Orange association, samples from different breeds were selected. Two analyses were conducted for the cross-breed Orange association. The first analysis was performed using chi-square tests for allelic association with individuals from different breeds (–assoc) and the second analysis accounts for population stratification by applying the Cochran-Mantel-Haenszel (CMH) test (–mh). Cats were clustered for the CMH test on the basis of the pair-wise population concordance (PPC) test, with a p-value of 0.01 set for merging individuals . Only samples and markers with a genotyping rate >90% and markers with MAF ≥ 0.05 were selected for each association analysis independently. Genomic inflation in the association measures of the p-values was evaluated by calculating the genomic inflation factor (ƛ) using PLINK88 (–adjust). To determine significance, multiple testing correction was accomplished with 100,000 permutation using PLINK88 (–mperm). T-max permuted p-values were considered genome-wide significant at p < 0.05. A Manhattan plot of the genome-wide p-values and permuted p-values were generated using a custom R script. The haplotype for the Orange locus was explored by exporting genotypes from position 105 Mb to positon 109 Mb of the X chromosome and then analyzed visually.For the X-linked –r2) between the causative variants and nearby SNPs. The LD between the closest SNP and the causative was used to calculate the power of the current array to detect SNPs density and the sample size needed to detect a significant association93.Considering the presence of the causative markers of the three phenotypes on the array, the power of association using the array was calculated by measuring the LD (squared correlation coefficient Supplementary informationSupplementary dataset 1Supplementary dataset 2Supplementary dataset 3Supplementary dataset 4Supplementary dataset 5Supplementary dataset 6Supplementary dataset 7Supplementary dataset 8Supplementary dataset 9