In this study, the potential use of ancient plant ingredients in emerging bakery products based on possible prehistoric and/or ancient practices of grinding and breadmaking was explored. Various ancient grains, nuts and seeds  were ground using prehistoric grinding tool replicas. Barley-based sourdough prepared by multiple back-slopping steps was added to dough made from einkorn alone or mixed with the above ingredients (20% level) or commercial flours alone . Sieving analysis showed that 40% of the einkorn flour particles were >400 μm, whereas commercial barley and common wheat flours were finer. Differential scanning calorimetry revealed that lentil flour exhibited higher melting peak temperature and lower apparent enthalpy of starch gelatinization. Among all bread formulations tested, barley dough exhibited the highest elastic modulus and complex viscosity, as determined by dynamic rheometry; einkorn breads fortified with linseed and barley had the softest and hardest crust, respectively, as indicated by texture analysis; and common wheat gave the highest loaf-specific volume. Barley sourdough inclusion into einkorn dough did not affect the extent of starch retrogradation in the baked product. Generally, incorporation of ancient plant ingredients into contemporary bread formulations seems to be feasible. Recent archaeological research has revealed a wide range of plant materials, preserved mainly as charred plant remains, which have been most likely used for preparation of human food ,3,4,5,6.Triticum. monococcum, T. dicoccum and T. timopheevi, respectively). During the Bronze Age, spelt wheat (T. spelta) also appears for the first time at the end of the 3rd millennium B.C., while the free-threshing wheats  had a more limited occurrence in prehistoric times [T. aestivum and T. durum) have already been grown since the Neolithic period. These free-threshing cereals, over time, became the dominant cereals grown for food in Western Asia and Europe at the expense of the glume wheats, perhaps because the latter have a lower productivity per cultivated area, a higher bulk volume when stored in their glumes and a greater labour input required for dehusking before milling [Hordeum vulgare) grain and lentil (Lens culinaris) seeds, as well as seed of acorn nut (Quercus sp.), linseed (Linum usitatissimum) and poppy seed (Papaver somniferum) transformed into flour by grinding [The archaeobotanical record reveals that a wide range of cereals and pulses constituted the staple foods of prehistoric European communities ,8. In thic times . Nowaday milling ,11. An a milling . Apart fgrinding ,6.The transformation of cereals and other plant-derived food materials would have taken place with the facilities available at the time, which included stone grinding and pounding implements , cookingConsumer interest in ancient wheat species  with regard to their use in bakery products has recently emerged, particularly because these grains are rich sources of bioactive components and hence suitable for producing high value food products with enhanced nutrient content and health benefits, especially when used in the form of whole flours ,20,21. FIn this context, fortification of bakery items with ancient grains and seeds could have an impact to the wellbeing of individuals and contribute to the reduction of risk factors and/or management of chronic diseases, such as diabetes type 2 and cardiovascular diseases. Furthermore, adopting ancient breadmaking practices could give rise to new bakery products with improved nutritional attributes, health benefits and minimally processed food items, without additives and improvers, thereby satisfying the new consumer’s preferences and emerging life-trends related to ‘clean labelling’ of food products, healthy diets, and wellbeing.In the present study, experimental breads were prepared under controlled conditions using plant ingredients identified in the prehistoric record and resembling some of the prehistoric and/or ancient breadmaking practices  in an effort to establish in ongoing studies  a relevaT. monococcum), barley (Hordeum vulgare), acorn (Quercus sp.), lentil (Lens culinaris), poppy seed (Papaver somniferum) and linseed (Linum usitatissimum)—were ground using prehistoric grinding tool replicas constructed for the PLANTCULT project (T. aestivum), type T70 (white flour), and spelt wheat (T. spelta), whole flour, were also employed for breadmaking, whereas a commercial barley whole flour was also used for breadmaking and sourdough preparation. All the above three commercial flours were organic, provided by a local supplier .Five different plant raw materials—einkorn wheat , 5-mesh (0.80 mm), 6-mesh (0.65 mm), 8-mesh (0.50 mm), 10-mesh (0.40 mm), 16-mesh (0.24 mm), 20-mesh (0.20 mm), 30-mesh (0.13 mm), 40-mesh (0.10 mm) and 70-mesh (0.063 mm). For particle size analysis of einkorn flour, all of the above sieves were used, whereas for the commercial flours of barley and common wheat, only the last six sieves were employed. After shaking for 15 min by a mechanical vibratory shifter, the amount of flour retained on each sieve (WS) was weighed, and the contents of retained fractions were calculated as the percentage of the initial  flour weight as follows:The cumulative percent passing through the sieves was calculated by subtracting the cumulative percent of retained fractions from 100%.50 (median diameter) and d90 that are commonly used in the classification of ground materials were estimated, representing 50% and 90%, respectively, of the particles with diameters smaller than the specified values.The particle size parameters of dw/w solids were sealed hermetically into DSC aluminum pans. The pans were heated from 8 to 120 °C at a heating rate of 5 °C/min; samples were heated under a continuous flow of dry N2 gas (20 mL/min) to avoid moisture condensation during measurement. Three specimens from each flour preparation were analyzed by DSC. Parameters estimated from the DSC thermographs were the onset (To) and peak (Tm) starch gelatinization temperature, as well as the apparent starch gelatinization enthalpy (ΔΗ) calculated from the area of the endothermic (melting) peak, following calibration of the calorimeter with indium.The starch gelatinization properties of flours and flour mixtures  used forw/w) was used, following a recipe provided in a modern sourdough preparation book for the wider public [The sourdough used for breadmaking was prepared for project PLANTCULT by the traditional method of spontaneous back-slopped sourdough (Type I); all the preparation steps are presented in detail in r public . The user public . Followiw/v), followed by decimal dilutions in the same saline solution. Enumeration of microorganisms was carried out using the pour plate technique. Lactic acid bacteria were enumerated on De Man Rogosa and Sharpe agar (MRS agar)  containing natamycin, following incubation at 30 °C for 3 days, while for enumeration of yeasts, Yeast Malt Agar (YM agar) , containing tartaric acid was used after incubation at 25 °C for 5 days.For the determination of colony forming units (CFU/g) in mature mother sourdough and the final back-slopped barley-based sourdough used for breadmaking, 10 g portions of sourdough were homogenized with 90 mL NaCl  determinations, 10 g of sourdough were mixed with 90 mL of sterile distilled water, and the resultant suspension was kept under continuous stirring during both measurements. The pH was measured with a Bante 210 pH meter , and the TTA was determined by titration of the sourdough suspension with 0.1 N NaOH until a final pH of 8.4; the TTA values were expressed as mL of 0.1 N NaOH/g of sourdough.w/w), and followed by hand-kneading for 5 min. Before rheological testing, all dough samples were wrapped with a plastic membrane to avoid water loss and rested at room temperature for 20 min, allowing uniform moisture distribution and dough matrix relaxation before testing. The dough preparation procedure for each formulation was repeated in triplicate.For the study of dough rheological properties, flours obtained with the prehistoric grinding tools were passed through a coarse sieve (~1 mm) to remove all coarse particles of husk and bran and thus increase their homogeneity and improve their water retention capacity. Dough samples from all tested flours used for breadmaking  without Oscillatory measurements of doughs were performed by a rotational Physica MCR 300 rheometer  using a parallel plate geometry (50 mm diameter and 2 mm gap) with a solvent trap to avoid moisture loss during measurements; the plate had a sanded surface to prevent slippage of the measuring fixture. The temperature was regulated at 20 °C by a controlled temperature peltier system (TEZ 150P/MCR) with an accuracy of ±0.1 °C. After loading, the dough sample was left to further rest in the geometry for 15 min prior to measurement. Oscillatory measurements were performed, at which the storage (or elastic) modulus, G′; loss modulus, G″; and complex viscosity, η*, at a strain level of 0.01%, were monitored over an angular frequencies range of 0.1–50 Hz. The data of the rheological measurements were analyzed with the supporting rheometer software US200 V2.21.w/w on a flour basis (DY 200); i.e., the amount of flour from sourdough was 20g in 100g of total flour in the bread formulation. The level of added water in all dough formulations, including the sourdough water amount, was 80% (flour basis); the same water level was also added to the control sample.Breads were made from einkorn, spelt, barley and common wheat flours alone as well as from flour mixtures of einkorn with another grain or seed flour, i.e., spelt, acorn, lentil, barley, poppy seed and linseed (einkorn: other flour in an 80:20 ratio), with the addition of sourdough; all bread formulations are shown in For the breadmaking process, flours obtained with the prehistoric grinding tools were used without removal of the coarse particles of husk and bran in order to closely resemble the prehistoric practices. Actually, pieces of cereal bran and husk embedded in a cereal-based amorphous matrix (gelatinized starch) have been identified in several archaeological sites in Southeastern Europe, indicating that breadmaking and/or porridge making from cereal grains were possibly widespread food preparation methods in prehistoric times, by employing relatively simple processing tools and practices .The flow chart presented in Large deformation mechanical properties of the experimental breads were examined by a puncture test using a Texture Analyser  calibrated with a 5 kg load cell. The bread loaves were compressed with a spherical probe (0.635 cm diameter) up to rupture of the crust at a crosshead speed of 0.4 mm/s. Hardness of the crust was taken as the peak force of the force-displacement curve; two values obtained from the same loaf were averaged, and two loaf breads from each breadmaking process were averaged into one replicate.The volume of the bread loaves was determined with a homemade volume meter made from plexiglass and based on the rapeseed displacement method . Bread lSuspensions in ethanol of einkorn and commercial barley flours, barley sourdough, einkorn dough without and with sourdough, as well as crumb of fresh einkorn bread without and with sourdough were prepared under stirring. Aliquots of suspensions were taken by aspiration and examined using an Olympus BX51 microscope (Japan) equipped with dry lenses, a microscope digital camera Olympus DP70 and the Olympus micro DP70 software. The microscopic observation was carried out after staining of starch with iodine solution in bright field and cross polarized light in order to observe the starch granules and the changes in starch microstructure during dough preparation and breadmaking in the presence or absence of sourdough. At least ten captures for each sample were taken.w/w solids were hermetically sealed into DSC aluminum pans and heated from 8 to 120 °C at a heating rate of 5 °C·min−1. Three crumb specimens of each bread preparation from all the breadmaking processes were tested by DSC. The onset melting temperature (ToRET), the peak (TmRET) melting temperature and the apparent melting enthalpy (ΔHRET) of the retrograded starch were determined.Changes in starch physical state upon breadmaking and bread storage were also examined by DSC analysis of the crumb of fresh and staled einkorn bread without and with sourdough; for the staling events in the starch matrix, bread loaves were stored in sealed polypropylene bags at 3 °C for 6 days. Before analysis, crumb samples from fresh and stored bread were lyophilized and then ground into fine powder using liquid nitrogen. Aqueous slurries of the lyophilized samples (about 25–30 mg) containing 35% All physicochemical parameters of flours were tested in triplicates. Mean values of dough parameters, loaf specific volume and crumb retrogradation parameters were the average from three dough specimens, bread loaves and crumb samples, respectively (one from each dough or bread making procedures). For crust hardness, firstly, values from two different points of the same loaf were averaged, and the mean values presented in this study were obtained from two different breads that averaged into one replicate of the three breadmaking repetitions.Statistical analyses were performed by the IBM SPSS statistical software . All parameters of flour, dough and bread properties were analyzed by a one-way ANOVA, according to a generalized linear model, examining the effect of flours and their mixtures at all breadmaking stages. Differences between mean values were compared using the Tukey’s test at a = 0.05 significance level.50 and d90 values of einkorn, barley and common wheat flour were 287, 228, 123 μm and 879, 313, 252 μm, respectively. Thus, einkorn flour exhibited the largest particle size at all particle size distribution ranges among the three tested flours since einkorn grains were ground by prehistoric grinding tool replicas (stone grinding), in contrast with the other two commercial flours, which were finely ground by industrial mills. Additionally, between the two commercial flours, barley flour had larger particles as it was a whole flour compared with those from common wheat, which was a white flour (70% extraction rate); it is well-known that whole flours include bran and thus have higher mean particle size compared with flours originating mostly from the endosperm of cereal grains (white flours).Analysis of sieving classifies the flour particles by size as well by shape. The particles of flour are usually spherical-like, such that their diameters correspond to the sides of the square sieve opening. Particle size cumulative distribution curves of einkorn, barley and common wheat flour are presented in 50 = 350 μm) exhibiting increased stiffness and resistance to deformation and flow compared with that of a fine (d50 = 200 μm) barley flour preparation [According to previous studies, the particle size of flours significantly affects the rate of water absorption during dough making, as fine particles absorb water faster due to their greater surface area . Smallerparation . Howeverparation ; therefoparation ,22,23.w/w solids) of tested flour samples showed the typical endothermic peak of starch gelatinization at around 56.0–80.0 °C (o) of this endothermic transition and the disruption (melting) of starch molecular orders  upon gelatinization occurs at the peak temperature (Tm)  ; the areo (64.7 °C) and Tm (74.0 °C) and the lowest ΔH (0.9 mJ/mg) values among all tested flour samples, implying higher resistance of this legume starch towards gelatinization and a relatively lower amount of double helical structures (primarily involving the amylopectin component) compared with those of cereal starches , which have ~20–25% and 60–70%, respectively [o temperature (56.2 °C) followed by the common wheat (57.6 °C), while To values for flours of barley, einkorn and its mixture with lentil, spelt and barley displayed higher values, ranging from 58.9 to 59.4 °C . Moreover, barley flour showed significantly lower apparent enthalpy and higher onset temperature of gelatinization compared with common wheat flour , while the number of yeasts is limited . In our p < 0.05) higher elastic modulus and complex viscosity values compared with all doughs made from any wheat species flour , suggesting that the barley dough was the most resistant to deformation and flow. Barley non-starch polysaccharides, such as β-glucans and arabinoxylans, can provide a composite dough with increased structural strength, stiffness and viscosity because of their ability to bind large amounts of water [p > 0.05).The rheological properties of flour doughs are influenced by many factors, such as dough ingredients (composition), temperature, water uptake and type of mixing, but the most important is the type of flour used . Figure of water ,62,63. Op > 0.05) change in its rheological behavior  and flow  and cannot necessarily be related to improved dough and bread textural properties . High viQuality characteristics of breads, such as appearance, loaf volume and bread texture, are major determinants of the product acceptability by consumers. Despite the expected positive health implications of ‘ancient’ cereal grains consumption, their involvement in bread production usually leads to doughs characterized by high tenacity and low extensibility, while the resultant breads exhibit reduced loaf volume ,68. HoweThe appearance of the loaf cross-sections of all bread formulations is given in p > 0.05) from that of the control bread (einkorn without sourdough). On the other hand, incorporation of linseed into the einkorn bread decreased (p < 0.05) the crust hardness significantly; thus, einkorn bread fortified with linseed had the lowest crust hardness among all tested samples. In agreement with our findings, Marpalle et al. [p > 0.05)  a. Soft c > 0.05) a.Loaf volume is commonly considered as the most important indicator of bread quality. The bread from common wheat flour had significantly higher loaf specific volume compared with all other bread samples b. The hiIodine staining of the starch granules of einkorn and barley flour revealed the typical bimodal size distribution and the characteristic oval and round shape of wheat and barley starch granules a-left. AAs expected, shape, integrity, size and birefringence of einkorn and barley starch granules were preserved in the dough and sourdough preparations b. It is During baking, the rise of temperature up to about 95 °C in the crumb and the level of added water to the dough allows the gelatinization of starch to occur. Therefore, starch granules appeared larger than those in flour and dough due to extensive swelling and loss of their oval or round shape; nevertheless, the swollen granules still retained their identity c, left. Heat-moisture mediated disruption of the ordered structures in granular starch (gelatinization) is generally a prerequisite for its utilization because it changes the rheological properties of the system and has a major influence on the functionality and digestibility of starch-containing products , e.g., gw/w solids) showed the typical endotherm peak of starch gelatinization at around 55–80 °C, with the peak starch gelatinization temperature (Tm) occurring at about 65 °C, while the enthalpy (ΔH) required for the disruption (melting) of the ordered structures in native starch is estimated by the area under this peak (w/w solids) after storage for long time (6 days)  affect these three parameters; the respective values of ΔHRET, ToRET and TmRET were found to be 2.0 ± 0.6 mJ/mg (of dry bread), 38.0 ± 0.3 °C and 49.1 ± 0.6 °C for stored einkorn bread without sourdough, and 2.7 ± 0.7 mJ/mg (of dry bread) 38.1 ± 0.3 °C and 48.5 ± 0.9 °C for bread samples with sourdough.As previously mentioned, the DSC thermograms of einkorn flour slurries  . The endh chains . The incRET in common wheat bread (crumb) upon storage (0–5 days) was examined, and it was found that the typical endothermic peak of melting of retrograded starch appeared at approximately 40–60 °C with TmRET at 51 °C, and the ΔΗRET value for the 5th day of storage was 2.7 mJ/mg of dry bread [In a previous study, the evolution of ΔΗry bread ; these rIn the present study, the physicochemical properties of flours, doughs and breads made by using ancient plant ingredients  as raw materials and adopting prehistoric grinding tool replicas for flour milling and sourdough making by multiple back-slopping steps as a bread leavening process were investigated. The research findings indicated that the particle size of einkorn flour ground with prehistoric-like stone mills was largely higher than it was for commercial fine flours of barley and common wheat. The starch gelatinization properties among cereal-based  flours were similar, while lentil flour had higher gelatinization temperature and lower enthalpy values. Overall, variations in the thermal, rheological and textural properties of the tested flours, doughs and bread formulations were observed among the different plant genera. More specifically, the barley flour made more elastic and viscous doughs compared with those containing any of the various wheat species used ; additionally, inclusion of acorn into einkorn-based dough formulation resulted in similar rheological behavior with that of barley dough. Among the different wheat species tested, no significant variations in dough rheological properties were noticed. Similarly, the various tested cereal genera (barley and wheat species) did not differ in bread crust texture characteristics. However, fortification of einkorn bread with stone-grounded barley and linseed flour resulted in an increase and decrease in crust hardness, respectively. As expected, common wheat exhibited the highest loaf-specific volume among all bread formulations. On the other hand, the addition of barley-based sourdough into einkorn dough formulation did not affect either the textural properties and loaf volume, nor the extent of starch retrogradation of the final baked product. Overall, the use of ‘ancient’ plant materials in making of sourdough bread seems to be a promising method for delivering a ‘clean labelling’ feature in bakery items, satisfying the consumer’s demand for healthy and naturally produced breads, as well as exhibiting quality attributes comparable to breads made from conventional wheat flours.


Genome-wide DNA methylation profiling have been used to find maternal CpG sites related to the occurrence of gestational diabetes mellitus (GDM). However, none of these differential sites found has been verified in a larger sample. Here, our aim was to evaluate whether first trimester changes in target CpG sites in the peripheral blood of pregnancy women predict subsequent development of GDM. This nested case–control study was based upon an early pregnancy follow-up cohort (ChiCTR1900020652). Target CpG sites were extracted from related published literature and bioinformatics analysis. The DNA methylation levels at 337 CpG sites of 80 GDM cases and 80 matched healthy controls during the early pregnancy (10–15 weeks) were assessed using MethylTarget sequencing. The best cut-off level for methylation of CpG site was determined using the generated ROC curve. The independent effect of CpG site methylation status on GDM was analyzed using conditional logistic regression. Methylation levels at 6 CpG sites were significantly higher in the GDM group than in controls, whereas those at another 6 CpG sites were significantly lower (FDR < 0.05). The area under the ROC curve at each methylation level of the significant CpG sites ranged between 0.593 and 0.650 for the occurrence of GDM. After adjusting for possible confounders, the hypermethylation status of CpG site 68167324  and 24837915  was identified as more strongly associated with GDM; meanwhile, the hypermethylation of CpG site 157130156  and 89438648  might indicate lower risk of GDM. The methylation status of target CpG sites in the peripheral blood of pregnant women during the first trimester may be associated with GDM pathogenesis, and has potential as a predictor of GDM. Alongside the increasing prevalence of obesity and type 2 diabetes, the incidence of GDM has risen annually2. Specifically, in China, this prevalence has been estimated at 14.8% (12.8–16.7%)3. GDM in pregnant women can result in severe adverse pregnancy outcomes complications, including macrosomia, premature birth, and fetal malformations4. GDM also has serious consequences for the offspring, such as childhood obesity, insulin resistance5, and impaired neurodevelopment6. In addition, GDM can recur in future pregnancies and also increases risk for postpartum type 2 diabetes7.Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy8, inflammatory factors9, and oxidative stress10. Wu et al. have shown that GDM has a genetic component, and the differences in GDM among ethnicity may be due to the interaction between genes and the environment11. Epigenetics bridges the gap between genes and the environment at the molecular level13. DNA methylation is one of the most commonly studied epigenetic modifications, which is catalyzed by DNA methyltransferases and uses S-adenosylmethionine as the methyl donor to convert the CpG site dinucleotide cytosine to the 5′-methylcytosine. The CpG site density and methylation degree of the upstream promoter region of the gene directly affect gene activity and expression13. DNA methylation therefore plays a key role in regulating genome transcription. Emerging studies from placental tissue and fetal cord blood suggest that in utero exposure to GDM impacts the placental and fetal epigenome15. However, these non-invasive samples collected at delivery during childbirth cannot reflect the level of maternal methylation before the onset of GDM, and could not be used for the exploration of the pathogenesis of GDM by methylation.The mechanisms underlying GDM include but not limit to genetic background17. Kang et al. conducted genome-wide DNA methylation chip analysis of the peripheral blood samples in late pregnancy and revealed that the methylation levels of 200 CpG sites of 151 genes are different between the eight GDM patients and eight healthy controls19. Moreover, Wu et al. observed that the methylation levels of 100 CpG sites corresponding to 66 genes were different between the GDM group (n = 11) and the control group (n = 11), in the peripheral blood of pregnant women in their first trimester, further suggesting that the DNA methylation status of 5 CpG sites, in the COPS8, PIK3R5, HAAO, CCDC124, and C5orf34 genes could be used as clinical biomarkers of GDM17. Similarly, Enquobahrie et al.20 found 17 hypomethylated and 10 hypermethylated CpG sites in the GDM group with the help of genome-wide DNA methylation analysis.Peripheral blood samples of pregnant women can be used to reflect changes in DNA methylation levels during pregnancy. Current researches focused on GDM and DNA methylation are mainly based on the genome-wide DNA methylation analysis16, the reproducibility of the test is poor, and the difference sites found among studies are less overlapping. On the contrary, the differential DNA methylation detection of candidate genes is a targeted identification based on existing discoveries and potential mechanisms. Here, we evaluated the DNA methylation levels in the peripheral blood of women in early pregnancy using specific target gene DNA methylation detection in order to verify the relationship between the methylation status of the targeted CpG sites and the onset of GDM.Although genome-wide DNA methylation scanning can broadly mine differentially methylated CpG sites as potential candidate genes for disease diagnosis and prediction, prior studies on this topic have been limited by small sample sizes, ethnic differences, method of quantification and lack of verificationThis was a nested case–control study based on an early pregnancy follow-up cohort. The cohort was established in Hunan Province Maternal and Child Health Hospital (ChiCTR1900020652) between March 2017 and December 2018, and a total of 890 pregnant women were enrolled. All the eligible participants agreed to participate in this study and provided written informed consent. The study protocol was approved by the Medical Ethical Committee of the Hunan Provincial Maternal and Child Health Hospital in South China  and all methods were performed in accordance with the relevant guidelines and regulations. Pregnant women were recruited in their first trimester (10–14 weeks) and followed up for 42 days post-partum. The inclusion criteria were: (1) singleton pregnancy and natural conception; (2) diabetes-free at recruitment; (3) had not received any antibiotic treatment throughout the current pregnancy; (4) no acute infection in the 2 weeks before sample collection; (5) planned to attend for all obstetric examinations and delivery at the above hospital. We collected questionnaire data and venous blood samples, while additional patient information and data concerning their clinical examinations were collected through the hospital’s electronic recodes system. The venous blood samples (5 ml/person) were collected using blood collection tubes without anticoagulant during early pregnancy , by certified nurses in the morning following a 10-h overnight fast. Serum and blood cells were separated by centrifugation at 3500 rpm for 15 min and stored at − 80 °C until further use.21 in the hospital outpatient department at 24–28 weeks of gestation. The oxidase method was used to estimate blood glucose levels, with measurement completed using an automatic biochemical analyzer (Hitachi 7600) at the hospital. GDM was defined according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) standard. That is, GDM was considered to be present when at least one of the following blood glucose concentrations was obtained: ≥ 5.1 mmol/L (fasting), ≥ 10.0 mmol/L (after 1 h), and ≥ 8.5 mmol/L (after 2 h)21. The controls were selected from women in the same cohort who had normal blood glucose levels throughout the pregnancy. A 1:1 pair match for each GDM patient was identified, based on the age of the pregnant (± 3 years) and gestational week (± 1 week) at the time of enrollment, resulting in a final study population of 80 eligible GDM patients and 80 healthy controls.All subjects underwent a 2-h standard 75 g oral glucose tolerance (OGTT)22, 21 target CpG sites were collected from the differential loci found in previous GDM-related whole-genome methylation sequencing analysis literatures, another 6 CpG islands were generated from the promoter regions of four genes which were reported to be closely related to the pathogenesis of GDM24. Additionally, two target sites were identified from the methylation data of cervical cancer patient data sets in the The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) databases by T-test method, with the Q value obtained from Benjamini and Hochberg modified P value controlling the false discovery rate of multiple hypothesis testing. Through the target site, the 50–100 bp upstream or downstream of its location was selected as the sequenced fragment, and all the CpG sites in the fragment were sequenced. Overall, this procedure therefore identified a total of 29 target fragments, containing 337 CpG sites. The primers and their sources are shown in Supplementary Tables S1, S2.Candidate CpG sites were mainly selected from published studies, and supplemented by bioinformatics analyses (Supplementary Table S3). Through a systematic reviewGenomic DNA was extracted from frozen samples using Genomic Tip-500 columns  and from bisulfite-converted samples using the EZ DNA Methylation™-GOLD Kit  in accordance with the manufacturer’s instructions. Genomic DNA integrity was measured using agarose gel electrophoresis and quality control was ensured using a NanoDrop 2000 , which requires that the DNA concentration ≥ 20 ng/μL, and that the total amount of DNA ≥ 1 μg.The DNA methylation level of the target CpG site is defined as the number of methylated reads at that site  divided by the total number of reads at that site, and was obtained by MethylTarget sequencing , a method based on next-generation sequencing-based multiple targeted CpG methylation analysis. Primer design and validation were performed using bisulfate-converted DNA samples on the Methylation Primer software. The primer sets were designed to flank each target CpG site by 100–300 nucleotides and are summarized in Supplementary Table S1. After PCR amplification  and library construction, paired-end sequencing was performed  in accordance with the manufacturer’s protocol.In order to ensure the consistency of the DNA methylation level detection results of all CpG sites, all DNA samples were sent in the same batch and tested using the same test method. For the data results after sequencing, we evaluated the quality of the original data through Fast QC software. The main evaluation index was the basic quality index . During sequencing, Q20 indicates that the Q value is greater than or equal to 20, that is, the sequencing error rate (P) during sequencing is less than or equal to 1%; Q30 indicates that the Q value is greater than or equal to 30, that is, the sequencing error rate (P) during sequencing is less than or equal to 0.1%. Generally, Q20 ≥ 90% (ie 90% base sequencing error rate ≤ 1%), and Q30 ≥ 85% (ie 85% base sequencing error rate ≤ 0.1%) are considered as qualified sequencing results. At the same time, the lowest conversion rate of bisulfite in the DNA methylation level detection process was 98.88%. For details, see Supplementary Data QC (excel file).In this study, we collected information on maternal demographics, lifestyle, and pregnancy history through structured questionnaires during each follow-up. This included factors with the potential to confound the exposure-outcome relationship, including pre-pregnancy body mass index (BMI) (continuous), history of drinking , history of smoking (yes/no), parity (continuous), pregnancy order (continuous), polycystic ovary syndrome (PCOS) (yes/no), and waist circumference at enrolment (continuous). Many included participants were primiparas, so history of GDM was not considered in the analysis.2 test. P < 0.05 was considered statistically significant, and all statistical tests were two-sided. ROC curve analysis was used to assess the possible predictive value of the methylation level of individual CpG site for the occurrence of GDM. When the level of DNA methylation was positively correlated with GDM, GDM would be used as the value of the state variable for ROC curve analysis, conversely, the control would be used. Through the ROC curve, the methylation status (high or low) of the target CpG site was classified based on the best cut-off value, defined as the DNA methylation level with the highest Youden index. Conditional logistic regression analysis was used to determine the independent influence of target CpG site methylation status on GDM. The model variable selection criterion was αin = 0.05; the variable elimination criterion was αout = 0.10; the Wald forward method was used to establish a conditional logistic regression model to screen CpG sites with independent effects. All the statistical analyses were performed using SPSS software v25.0 .Continuous data and categorical data were represented by the mean ± standard deviation (SD) and frequency (percentage), respectively. Paired-samples T test were used to compare normally distributed continuous data, whereas Wilcoxon signed rank test were used to analyze non-normally distributed continuous data. Meanwhile, FDR  correction analysis was performed for CpG sites with differences in univariate analysis. Dichotomous variables were analyzed using McNemar χP > 0.05) was observed between the two groups in terms of gravidity, parity, PCOS, smoking history, alcohol intake history, age, or gestational age. The GDM group had higher fasting glucose, 1-h post-OGTT glucose, 2-h post-OGTT glucose, pre-pregnant BMI, and waist circumference than the control group.The participant characteristics are summarized in Table ARHGAP40, STAT1, C5orf34, RDH12, and YAP1 genes were higher in the GDM group than in the control group, whereas those at 6 CpG sites within the HAPLN3, IFNGR2, YAP1, NFATC4, and DNAJB6 genes were lower in the GDM group than in the control group. Brief introduction to the function of the genes where those differential CpG sites are located can be found in Supplementary Table S5.We assessed the DNA methylation levels at 337 CpG sites, and the results of the paired-samples T test or Wilcoxon signed rank test analysis for all the CpG sites are summarized in Supplementary Table S4. The mean imputation method was used to deal with missing data (Supplementary Table S4). There were 13 CpG sites with results differing significantly between the group .To clearly show the effect of DNA methylation at the target CpG sites on GDM occurrence, we classified the DNA methylation levels into hypermethylation and hypomethylation statuses based on the best cut-off value Table . Table 4Conditional logistic regression analysis was used to analyze the independent effect of the methylation status of the individual site on GDM occurrence. The independent variables included the methylation status of the eight significantly different CpG sites listed in Table We found that the methylation status of four CpG sites influenced GDM occurrence. Specifically, the hypermethylation of CpG site 68167324 , and CpG site 24837915  may indicate increased risk of GDM occurrence. In contrast, the hypermethylation of CpG site 157130156  and CpG site 89438648  may indicate decreased risk of GDM . Overall, we identified 13 CpG sites with significant differences in DNA methylation levels between the GDM and control groups based on quantitative analysis. The AUCs of the ROC curve for each methylation level of the significant CpG sites ranged from 0.593 to 0.650 predictive utility in relation to GDM. The methylation status of eight individual CpG sites were identified as differing significantly between GDM and control groups by qualitative analysis, and these were located in the promoter regions of RDH 12, HAPLN3, NFATC4, YAP1, and DNAJB6, and the intron region of C5orf34. Importantly, we found that the methylation statuses of four CpG sites were significantly associated with GDM occurrence, namely CpG site 89438648 (HAPLN3), 68167324 (RDH12), 157130156 (DNAJB6), and 24837915 (NFATC4), using conditional logistic regression analysis.An increasing number of studies have explored the pathogenesis of GDM from the perspective of epigenetics. However, most of these were small (< 30 GDM cases), and they mainly observed the associations between GDM occurrence and the DNA methylation level of cord blood or placental tissueHAPLN3, was found to suggest a lower risk of GDM . HAPLN3 codes for hyaluronan and proteoglycan link protein 3 (HAPLN3), and the connexin 3 belong to the hyaluronic acid and proteoglycan connexin (HAPLN) family, which plays roles in the aggregation of proteoglycans and hyaluronic acid, and in cell adhesion28. HAPLN3 is involved in the organization and stability of the hyaluronic acid (HA)-dependent extracellular matrix (ECM) in many tissues. HA is one component of the ECM within the islet tissue of humans and mice29. It can cause islet amyloid deposition, which is associated with decreased β-cell area and an increase in β cell apoptosis30. Hull et al. suggested that islet amyloid deposition could reduce the number of β-cells31. Hypermethylation of the CpG site 89438648 located in the HAPLN3 promoter region, could reduce the level of HAPLN3, in turn reducing the stability of the HA-ECM, and consequently reducing the impact amyloid deposition on β cells.In this study, hypermethylation of the CpG site 89438648, located in the promoter region of RDH 12, can increase the risk of GDM . RDH 12 encodes retinol dehydrogenase 12 (RDH12), a member of the short-chain dehydrogenases/reductases (SDRs) family32, which participates in steroid and retinol metabolism33. RDH12, a NADPH-dependent all-trans retinol dehydrogenase, is the key enzyme in the metabolism of retinoids34. Two oxidation products of retinoids, 9-cis-retinoic acid and all-trans retinoic acid, function to stimulate insulin secretion35. In adipocytes, retinoic acid induces the expression of the insulin signaling gene PDK-1 and that of the glucose transporter GLUT4. Activating retinoic acid induces the expression of genes involved in lipid and glucose metabolism, thereby improving insulin action36. Thus, hypermethylation of the CpG 68167386 located upstream of the promoter region of RDH12 may inhibit its transcriptional activity and reduce RDH12 levels in peripheral blood. Subsequently, the retinoic acid metabolic pathway would be inhibited, affecting insulin secretion, and reducing its effectiveness.We found that the hypermethylation status of CpG site 68167324 located in 37 and acts as a molecular chaperone for various cellular processes. While observing insulin resistant and diabetic patients, Kurucz et al.38 found that HSP expression was significantly changed without diabetes, and that the mRNA level of HSP72-inducible subtypes was significantly reduced in patients with type 2 diabetes. Additionally, the expression of HSP70 in the skeletal muscle of patients with type 2 diabetes is reduced and has been shown to correlate with the degree of insulin resistance39. These HSP molecular chaperones are related to diabetes40. However, the exact association between DNAJB6 and type 2 diabetes needs further study. In this study, hypermethylation of CpG sites 157130156, located in the promoter region of DNAJB6, was observed in the GDM group. This might result in increased DNAJB6 levels via the up-regulation of DNABJ6 transcription, thereby reducing the risk of GDM .The DNAJB6  protein is a member of the heat shock protein 40 (HSP40) familyNFATC4 codes the nuclear factor of activated T cells 4 (NFATC4), which is a member of the transcription factor family under the control of calcineurin (a Ca2+-dependent phosphatase)41. In adipose tissue, NFATC4 has been shown to promote the secretion of inflammatory factors42, and to act as a transcriptional repressor in regulating adiponectin gene expression, suggesting that adiponectin expression is down-regulated in obesity and type 2 diabetes43. In this study, hypermethylation of the CpG site 24837915 located in the promoter region of NFATC4, was associated with the presence of GDM .44. When insulin secretion fails to balance insulin resistance, impaired glucose tolerance develops, which might subsequently lead to GDM45. Therefore, impaired secretion by β cells is also a key factor in GDM pathogenesis. Here, we explored the pathogenesis of GDM from an epigenetic perspective and identified 13 CpG sites that had methylation levels showing associations with GDM pathogenesis. Furthermore, conditional logistic regression analysis showed that the methylation status of four CpG sites located in the promoter regions of four genes was associated with GDM pathogenesis. These CpG sites are located in genes that could contribute to the development of GDM. Of these four CpG sites, hypermethylation of CpG site 24837915 and CpG site 68167324 was shown to be associated with GDM, whereas that of CpG site 89438648 and CpG site 157130156 could indicate reduced risk of GDM. Thus, the methylation status of these genes may function as predictors of GDM. No publications reporting on the relationship between methylation of these four CpG sites and GDM have been found, so our suggestion of such a relationship is based on the known modes of action of the genes concerned.During pregnancy, early anabolism increases and mild insulin resistance occursHowever, the study also had some limitations. First, the selection of our target CpG sites was based on published literature, and we did not screen for differential CpG sites in the same population in this study, so there may be other CpG sites related to the pathogenesis of GDM that have not been verified. Second, probably due to the large total number of detected CpG sites in the background, as well as the limited significant different sites, no significant differences were found anymore after the FDR correction was used for the 337 tests. Third, the blood samples were centrifuged to separate serum and blood cells within 24 h after collection, but the cell type composition was not further separated. Which prevented us from considering DNA methylation on cell type composition or the adjustment for cell proportions. Finally, since that our sample size was relatively limited, these findings need to be further verified using a larger and broader population.In summary, by determining the DNA methylation of target CpG sites in the peripheral blood of women in early pregnancy, we found that the methylation levels of 13 CpG sites were related to GDM by quantitative analysis. After adjusting for possible confounding factors by conditional logistic regression, four CpG sites showed independent effects on GDM. These findings indicate that the methylation status of these CpG sites in the peripheral blood of pregnant women during the first trimester might be associated with was related to the pathogenesis of GDM. But the exact relationship still needs further research and verification.Supplementary Information 1.Supplementary Information 2.


Terrestrial vegetation growth activity plays pivotal roles on regional development, which has attracted wide attention especially in water resources shortage areas. The paper investigated the spatiotemporal change characteristics of vegetation growth activity using satellite-based Vegetation Health Indices (VHIs) including smoothed Normalized Difference Vegetation Index (SMN), smoothed Brightness Temperature (SMT), Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and VHI, based on 7-day composite temporal resolution and 16 km spatial resolution gridded data, and also estimated the drought conditions for the period of 1982–2016 in Jing-Jin-Ji region of China. The Niño 3.4 was used as a substitution of El Niño Southern Oscillation (ENSO) to reveal vegetation sensitivity to ENSO using correlation and wavelet analysis. Results indicated that monthly SMN has increased throughout the year especially during growing season, starts at approximate April and ends at about October. The correlation analysis between SMN and SMT, SMN and precipitation indicated that the vegetation growth was affected by joint effects of temperature and precipitation. The VCI during growing season was positive trends dominated and vice versa for TCI. The relationships between VHIs and drought make it possible to identify and quantify drought intensity, duration and affected area using different ranges of VHIs. Generally, the intensity and affected area of drought had mainly decreased, but the trends varied for different drought intensities, regions and time periods. Large-scale global climate anomalies such as Niño 3.4 exerted obvious impacts on the VHIs. The Niño 3.4 was mainly negatively correlated to VCI and positively correlated to TCI, and the spatial distributions of areas with positive (negative) correlation coefficients were mainly opposite. The linear relationships between Niño 3.4 and VHIs were in accordance with results of nonlinear relationships revealed using wavelet analysis. The results are of great importance to assess the vegetation growth activity, to monitor and quantify drought using satellite-based VHIs in Jing-Jin-Ji region. A warmer world is expected to have higher temperature variability, which will result in more drought events5. For example, China has experienced several severe drought events for past several decades, which profoundly affected the water resources and vegetation growth, and consequently brought greater challenges to agricultural development and water-energy-food security7. Drought has destructive impacts on crop yields in China, especially in plain regions with thriving agricultural development, which has caused great economic losses9.The Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) showed that the global average surface air temperature had increased by 0.85 ± 0.21 °C from 1880 to 2012. The special report of global warming of 1.5 °C revealed that human activities were expected to have caused approximately 1.0 °C of global warming with respect to pre-industrial levels. Global warming is likely to reach 1.5 °C between 2030 and 2052 if the warming trend continues. Temperature has risen faster at higher northern latitudes, where average temperature has raised nearly twice the rate of global average over past 100 years. More extreme weather and climate events have been observed since 1950s, some of which have been linked to human influences, including increasing trends of extreme high temperature and heavy precipitation events in a number of regions10. It is one of the most severe natural disasters that exert devastating influences on regional economic, agricultural, ecological and environmental conditions11. The severity and frequency of drought are expected to increase in the future, mainly results from decreasing trends of regional precipitation, increasing trends of temperature and evaporation driven by global warming12. Generally, drought originates from precipitation deficiency for an extended period and the influences of drought accumulated slowly in most cases. However, it is difficult to determine the onset, end and severity of the drought14. Various drought indices such as Palmer Drought Severity Index (PDSI), Crop Moisture Index (CMI), Drought Area Index (DAI), Normalized Soil Water Index (NSWI), Reconnaissance Drought Index (RDI), self-calibrating PDSI (sc_PDSI), Soil Moisture Anomaly Percentage Index (SMAPI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) have been proposed to monitor, quantify and analyze the drought events and their characteristics mainly from three aspects including intensity, duration and spatial coverage17. These indices were usually used to measure the departure from the normal condition in a moisture variable based on historical distribution19. However, different drought indices probably lead to some differences both in values and characteristics20. Especially, it is difficult to quantify the moisture, thermal and drought conditions merely based on station-based meteorological or hydrological indices.Drought is a recurring phenomenon occurred naturally with significant impacts on human, agricultural and environmental activities across the world26. For example, Kogan et al.5 studied the trends of global and regional drought area for several drought intensities based on the satellite-based vegetation health indices during the warmest decade, especially, two recent strongest droughts happened in Russia and USA in 2010 and 2011, respectively. Li et al.24 and Pei et al.25 investigated the changing characteristics of drought and also detected the vegetation responses to weather-related variations using VHIs, which provided references for vegetation growth activity monitoring and water resources management. The VHIs has been used as one of useful indices to detect drought characteristics, to quantify the impacts of drought, to monitor regional vegetation growth and assess agriculture production26. Besides, the VHIs were further used to estimate the extreme wildfires17, crop yield27, malaria cases28, and design insurance contracts for drought-related yield losses29.To avoid the weakness of conventional systems and drought indices to monitor drought, a new type of satellite-based drought index was proposed to describe the cumulative moisture, temperature and vegetation health conditions, including Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI), which have been widely used to monitor and detect drought-related vegetation conditions. The satellite-based Vegetation Health Indices (VHIs) have been used to characterize drought and vegetation productivity in many previous studies around the world31. It has been verified that ENSO had noticeable impacts on the vegetation growth in many regions around the world through global teleconnections. Many previous studies have investigated the relationships between vegetation growth and ENSO at different spatial scales and time periods. For example, Zhao et al.32 found that the ENSO is the leading climatic driver of interannual variability of Normalized Difference Vegetation Index (NDVI) during growing season to local and remote climate oscillations, especially in some regions such as southern and eastern African, northeastern Asia and northern South America for the period of 1982–2013. Asoka and Mishra33 developed a model to predict vegetation health using Niño 3.4 as predictor at one to three months lead time in India, which provides references for early warning and better planning in water resources and agricultural management. Erasmi et al.34 detected a close relation of ENSO warm events and periods of reduced vegetation greenness with a 12-month lag using annual NDVI during 1982–2010. The relationships between vegetation growth and ENSO provide the possibility to investigate the impacts of ENSO on vegetation growth using VHIs.To reveal the change mechanism of vegetation growth activity, the paper further studied the linear and nonlinear relationships between VHIs and El Niño Southern Oscillation (ENSO), which is one of the coupled atmosphere–ocean global climate anomalies. ENSO is a recurring global climate oscillation involving changes of water temperature in the central and eastern tropical Pacific Ocean. A warming of the ocean surface or above average sea surface temperature (SST) indicates El Niño event, and in reverse for La Niña event36. However, it is difficult to comprehensively assess the vegetation productivity together with drought characteristics using merely meteorological or hydrological monitoring data. The satellite-based VHIs provide advantages to assess the vegetation growth activity and analyze the drought characteristics. Therefore, the analysis of satellite-based VHIs is an important indicator to assess vegetation growth activity and help to monitor drought, which should be useful for the disaster prevention and reduction decision in Jing-Jin-Ji region.The superiority of satellite-based VHIs relative to other drought indices makes it one of the widely used indices to monitor drought and to assess vegetation growth activity, especially in large area with limited water resources. The Jing-Jin-Ji region includes Beijing city, Tianjin city and Hebei province which has thirteen cities including eleven prefecture-level cities and two county-level city, and it is the biggest urbanized region in North China Plain and one of the three largest regional economic communities in China. The rapid urbanization and economic development prompted the water demands to increase dramatically. Previous studies found that the Jing-Jin-Ji region had a dry tendency mainly located in the northern part for past several decades, which mainly affected the crop yield during growing seasonThe primary objectives of this study are: (1) to assess the spatiotemporal variations of vegetation growth activity using long duration (1982–2016) satellite-based vegetation health indices in Jing-Jin-Ji region; (2) to estimate the drought severity and vegetation condition using different categories of VHIs; (3) to reveal the vegetation sensitivity to ENSO based on VHIs using correlation and wavelet analysis. The paper is organized as follows: Material and methods are described in section "37. With the rapid development of economy and population, the Jing-Jin-Ji region has become one of the metropolitan regions with serious water scarcity, conflict between rapid economic development and sustainability38. To reduce above issues, the Programme on the Beijing-Tianjin-Hebei Coordinated Development has been approved in April 2015, providing one innovative solution for regional coordinated development. The hydrological and meteorological conditions varied for different regions and periods because of the multifarious physiographic characteristics37. There is a tremendous conflict between water supply and demands because the water resources are limited but the water demands had increased dramatically. The annual average temperature is about 12 °C at southern part to nearly 2 °C at northern part of Jing-Jin-Ji region. The average annual precipitation is about 540 mm for past several decades, and nearly 70% of the annual precipitation concentrates in the flood season from June to September35. With the combined effects of climate variables, the water security encounters enormous pressure especially in Hebei province, and therefore affects the vegetation growth and agricultural production within the Jing-Jin-Ji region.The Jing-Jin-Ji region located at 36.02° N–42.62° N latitude and 113.06° W–119.88° W longitude  and blended Visible Infrared Imaging Radiometer Suite (VIIRS) data (2013–2016), which can be obtained from the Center for Satellite Applications and Research of NOAA at http://www.star.nesdis.noaa.gov/.The satellite-based VHIs used to assess the vegetation growth activity and drought conditions were derived from the National Oceanic and Atmospheric Administration (NOAA) Satellite and Information Service Global Area Coverage (GAC) dataset, which were produced based on the Advance Very High Resolution Radiometer (AVHRR) daily reflectance and emission in the visible, near infrared and two infrared bands including IR4 and IR5. The AVHRR was flown in NOAA polar-orbiting satellites including NOAA-7 , NOAA-9 , NOAA-11 , NOAA-14 , NOAA-16 , NOAA-18  and NOAA-19 . The 7-day composite temporal resolution and 16 km spatial resolution VHIs data were aggregated from original daily data for the period of 1982–2016. The weekly VHIs were processed to remove high frequency noise, which should be more suitable for climatology analysis. The 16 km spatial resolution of VHIs were used since the time series were longer39, was used as a substitution of ENSO to reveal the vegetation sensitivity in Jing-Jin-Ji region. It can be downloaded from National Weather Service Climate Prediction Center (CPC) of NOAA available at https://www.cpc.ncep.noaa.gov/data/indices/.The Niño 3.4 indicating the regional average equatorial SST over the central tropical Pacific located at 5° S–5° N, 170° W–120° W40. It is one of the most popular vegetation indices for monitoring short and long term variations of terrestrial vegetation productivity and land cover because it reflects leaf area index (LAI) and vegetation biomass. Positive values of NDVI indicate vegetated conditions and near zero values indicate non-vegetated conditions. Bigger value of NDVI indicates that large difference exists between VIS and NIR band, which substantially indicates greener and denser vegetation. The BT was converted from the IR4 defined as the infrared reflectance at band of 10.3–11.3 μm. The smoothed Normalized Difference Vegetation Index (SMN) and smoothed Brightness Temperature (SMT) are derived from no noise NDVI and no noise BT because the NDVI and BT could be affected by noises resulted from atmosphere constituents, unusual events, pre- and post-launch calibrations. Since different sources of noise affect NDVI and BT, it is hard to propose individual procedure for each source of noise. In general, the noise in NDVI and BT were removed empirically by comparing satellite and in situ observations using methods of mathematical statistics and validation, such as, Empirical Distribution Function (EDF) and statistical methods. More details of noise removal can refer to Kogan et al.41. After smoothing, the SMN can be used to assess the start and senescence of vegetation, detect the growing season, and at the same time, the SMT can be used to estimate thermal condition and cumulative degree days for vegetation growth. The interannual differences in SMN and SMT fluctuated due to the weather variation after smoothing, which were estimated relative to the maximum and minimum intervals of both NDVI and BT variations.Five categories of VHIs including smoothed NDVI , smoothed Brightness Temperature , VCI, TCI and VHI were used as substitutions to investigate the change patterns of vegetation growth activity, to detect the change characteristics of drought and to reveal the impacts of Niño 3.4 on the vegetation growth activity. Definitions of VHIs are shown in Table 42. As shown in Table max and SMNmin (SMTmax and SMTmin) are multi-year absolute maximum and minimum values of SMN (SMT), respectively. Coefficient a is used to quantify contribution of VCI and TCI to total vegetation health. It was assumed that the share of VCI and TCI into the VHI is equal and a = 0.5, because the share is uncertain for a specific location and time.The VCI, TCI and VHI are used to assess the cumulative moisture, temperature and vegetation health conditions, respectively. The VCI is a proxy for moisture condition, based on the pre- and post-launch calibrated radiance converted to the SMN, which can be calculated as SMN anomaly relative to its multi-year climatology estimated based on bio-physical and ecosystem laws. The TCI is a proxy for thermal condition. It is calculated based on 10.3–11.3 µm AVHRR's radiance measurements converted to BT of land surface, which can estimate the hotness of the vegetation canopy. It is expressed as SMT anomaly with respect to multi-year climatology. Calculated based on the BT with completely removed high frequency noise, the value of TCI varies for hot or cold years. For example, in hot years with high temperature and insufficient water supply, the vegetation canopy can be overheating, which intensifies negative moisture deficit impacts on vegetation. The VHI indicates the combination of VCI and TCI to assess total vegetation health25. Higher values indicate better moisture, thermal or vegetation condition22. E.g., VCI < 40 indicates moisture stress, 40 < VCI < 60 for normal moisture condition, and VCI > 60 indicates favorable moisture condition. Decreasing trends of VHIs from 40 to 0 indicate intensification of vegetation stress, and vice versa for VHIs from 60 to 100. Further, drought-related stress can be assessed based on VCI, TCI and VHI if their values are less than 40. Taking VHI as an example, the VHI < 5 indicates exceptional drought intensity (D4), VHI < 15 for extreme-to-exceptional (D3-D4) drought intensity, VHI < 25 for severe-to-exceptional (D2–D4) drought intensity, and VHI < 35 indicates moderate-to-exceptional (D1–D4) drought intensity  to 100 (most favorable condition), with normal conditions during 40–60 corresponding to the average cumulative moisture, temperature and vegetation health conditionsptional D-D4 droug43. Details of CWT can be found in the previous studies45.To reveal the impacts of Niño 3.4 on the VHIs, the non-stationary relationships between Niño 3.4 and VHIs were investigated using wavelet analysis. Wavelet transforms including Continuous Wavelet Transform (CWT) and its discrete counterpart can expand time series into time and frequency space, to identify both the dominant modes of variability and localized intermittent periodicities. The Cross Wavelet Transform (XWT) consisting of two CWTs is used to detect regions in time and frequency space where both time series show high common power. The Wavelet Coherence (WTC) between two CWTs is used to identify regions with significant coherence even though two time series do not necessarily have high comer power, and show the significant level against red noise backgrounds44:, and The XWT used to analyze the covariance of two time series X and Y, is defined as follows45.The XWT is further defined as 46:45:scale indicates smoothing along the wavelet scale axis and Stime denotes smoothing in time44:1, C2 are the normalized constants, The WTC measures the coherence in time and frequency space between two CWTs, which is defined as follows44.The Monte Carlo method is used to estimate the statistical significance level of the WTC, with 5% significance level against red noise representing as a thick contour. The relative phase relationship is described as arrows with in phase (anti phase) pointing right (left). The cone of influence (COI) indicates the region of the wavelet spectrum in which edge effects become important, and it also signifies the wavelet power for a discontinuity at the edge drops that the edge effects are negligible beyond this point. However, it is noteworthy that the edge effects on and outside the COI cannot be ignored because the wavelet is not completely localized over the study periodt for the period of 1982–2016. The regression coefficient b is the linear slope of VHIs per year, and et is the residuals.The linear fitting regression and Mann–Kendall non-parametric methods were used to analyze the trends of VHIs. The linear trends of VHIs were calculated as follows:47. The Mann–Kendal test was used to analyze the trends of VHIs during growing season, and the Sen’s slope was used to investigate the trend magnitudes of regional average monthly VHIs for the period of 1982–2016 in Jing-Jin-Ji region.The nonlinear trends of VHIs were analyzed using the Mann–Kendall non-parametric test at a 5% significance level. The trend magnitude is estimated using Thiel-Sen approach, also known as Sen’s slope, defined as the median of all possible combinations for the time series. Details of Mann–Kendall non-parametric test and Sen’s slope can be found in previous studies–2 /year), indicating that the regional average SMN in May had increased 0.0574. Two periods from March to June and from September to November had statistically significant increasing trends at 5% significance level. Similar with regional average values of monthly SMN, the trend magnitudes of SMN in winter season were smaller than those in other seasons. Surprisingly, the trend magnitude decreased from May to August, which might be caused by the shapely decreased precipitation , followed by April (28.24 °C). However, the SMT during April and May had decreased for past 35-year period. The largest trend magnitude of regional average value of monthly SMT was detected in March with an annual rate of 0.05 °C, indicating that regional average value of SMT in March had increased up to 1.82 °C over past 35 years. The reasons for increasing trend of SMT might due to the global warming effects and urban heat island, since the urbanization and human activities for big cities such as Beijing city, Tianjin city and Shijiazhuang city have increased for past several decades. However, regional average values of SMT in several months had decreased, especially from October to December, which indicated that the warming trends had slowed down. No statistically significant trend was detected at 5% significance level  droughts were estimated based on regional average weekly VHI. Figure  droughtsThe percentage changes of area indicating vegetation stress (VHI < 40), normal vegetation condition (40 < VHI < 60) and favorable vegetation condition (VHI > 60) were further studied, as shown in Fig. st, especially after 2010. The urban area has increased from 6.92% in 2000 to 8.14% in 2010 and 12.11% in 2020. Thus, we should take into account the urbanization effects when analyze the drought condition. The results of Table To reveal the effects of drought on the vegetation growth activity at different areas of Jing-Jin-Ji region, the percentage of area with regional average weekly VHI at different ranges in Beijing and Tianjin cities were also investigated for the period of 1982–2016. It is noteworthy that drought in some regions such as Beijing and Tianjin cities might affected by the urbanization. Based on highly resolution NDVI data derived from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6 dataset, we have analyzed the change characteristics of NDVI for different land coverage. The results found that the urban area mainly increased in the 21We further investigated change patterns of different vegetation conditions and corresponding duration for each grid in Jing-Jin-Ji region based on weekly VHI at different ranges including D1-D4, 0–40, 40–60 and 60–100, respectively. Generally, the spatial distributions of average values and their corresponding duration of VHI at each range were scattered and complex. The results of average values and their average corresponding duration of weekly VHI at each category during 1982–2016 are shown in Table Based on the relationships between drought and VHIs, the drought can be predicted 4–8 weeks in advance. Figure The Niño 3.4 was used as a substitution to reveal the sensitivity of regional vegetation health during growing season to ENSO by correlating the Niño 3.4 with VHIs for each grid during 1982–2016 in Jing-Jin-Ji region, as shown in Fig. On the contrary, for TCI, 67.71% of grids were positively correlated to Niño 3.4, with the maximum correlation coefficient ρ = 0.1997. The grids that exhibited positive correlation coefficients were mainly detected in Zhangjiakou and Chengde cities, located in the northern part of Jing-Jin-Ji region, especially in the northwest part. The negative correlation coefficients were mainly detected in the south of Jing-Jin-Ji region, such as Handan city and Xingtai city. The correlation coefficients were irregularly distributed in the middle of Jing-Jin-Ji region, which indicated that the Niño 3.4 exerted inconspicuous impacts on the thermal condition  and negative 43.78%) correlation coefficients were approximately equivalent. Spatially, the grids had positive and negative correlation coefficients between Niño 3.4 and VHI distributed more uniform than those of Niño 3.4 correlated to VCI and TCI. The grids located in the southeast and northwest of Jing-Jin-Ji region were negatively dominated, and the grids located in Beijing city, Tianjin city and some other sporadic areas had positive correlation coefficients  were further investigated using correlation and wavelet analysis. Our findings are summarized as follows:Generally, the vegetation growth activity has increased for past 35 years, especially after 2000. The monthly SMN had increased throughout the year especially during growing season. Analysis of related climate variables including SMT and precipitation showed similar change patterns with that of SMN. However, the trend magnitudes varied in different months for SMT and precipitation. The correlation analysis between SMN and SMT/precipitation indicated that the temperature is the dominant variable that affected the vegetation growth.49, who found a greening pattern especially prominent in China and India using satellite data for the period of 2000–2017. The VHIs were proposed by Kogan42 and had been widely used to assess the terrestrial vegetation productivity, land cover and drought characteristics. It is noteworthy that the VCI and TCI are normalization of smoothed NDVI and BT, which are the proxy for moisture and thermal conditions. The VHI is a proxy characterizing vegetation health, and can be calculated based on a combine estimation of moisture and thermal conditions. However, it still needs to further investigate whether it is reasonable to include both NDVI and BT in the vegetation health index. For example, the Land Surface Temperature (LST) is related to the albedo of the soil and vegetation, an important parameter to monitor the state of crops and vegetation, and also an important indicator of greenhouse effect at both global and local scales. It might be more suitable to indicate the thermal of the vegetation condition with respect to brightness temperature, especially for urban areas50, which should be useful to combine the LST into VHIs in the future. Another issue should note that the algorithms of VCI and TCI are based on comprehensive processing of smoothed NDVI and BT that remove the high frequency noise, and the algorithm of VHI is based on the weighted sum of VCI and TCI, which again need to interpret in cautious.The spatiotemporal variations of VHIs for each individual grid showed that the VCI (TCI) was positive (negative) trends dominated during growing season, and the trend magnitudes varied for different VHIs and grids. The decreasing trends of VCI and VHI were mainly detected in big cities such as Beijing, Tianjin and Shijiazhuang, which should take into account the effects of urbanization since the urbanization level in the Jing-Jin-Ji region had increased a lot for past several decades, especially in large cities. The dominated positive trends of VCI and VHI revealed at Fig. 2, who developed an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta’s vegetation productivity using precipitation and temperature as predictors. However, the influence mechanism of vegetation growth was complex, which needs to be further investigated.The strong relationships between SMN and SMT/precipitation provided the possibility to predict SMN using climate variables such as temperature and precipitation as predictors. The results agree with previous study of Jiang et al.5, who used vegetation health indices for early detection and monitoring of drought. The VHIs have been successfully used for early drought detection and crop production losses estimation in 20 countries around the world. The intensity, duration, affected area of drought were estimated based on weekly VHI at different categories for both regional average values and each individual grid. In general, the percentage of area affected by drought at different intensities (D1-D4) had decreased, and the percentage of area with normal vegetation condition and favorable vegetation condition had increased from 1982 to 2016. However, the slopes of linear fitting of percentage of area affected by drought varied for different drought intensities, different regions and different time periods. It is noteworthy that the percentage of area affected by D2-D4 drought intensities and percentage of area with favorable vegetation condition had decreased from 1982 to 2016 in Tianjin city, indicating that the vegetation growth activity in some regions had become weaken, which might due to the urbanization effects and should be emphasized. The spatial distributions of average values and their corresponding duration of weekly VHI at each categories were mainly scattered and complex for each grid in Jing-Jin-Ji region.The relationships between VHI and drought intensity make it possibility of using VHI to monitor the start, duration and end of drought. As studied by Kogan et al.23 evaluated the sensitivities of different land ecosystems by correlating VHIs with Niño 3.4 globally. The results indicated that satellite-based VHIs provided a combined contribution of moisture and thermal conditions with respect to cumulative vegetation response, which delineated more precisely the affected areas and period compared to weather-based features such as precipitation and temperature.The Niño 3.4 was mainly negatively (65.25% of grids) correlated to VCI, which were predominated located in the northwest and southeast of Jing-Jin-Ji region. However, the linear relationship between Niño 3.4 and TCI was positively dominated and 67.71% of grid had positive correlation coefficients mainly located in the northern part of Jing-Jin-Ji region. However, the spatial distribution of positive and negative correlation coefficients between Niño 3.4 and VHI was inconspicuous with respect to VCI and TCI, which might be caused by predominantly opposite effects of Niño 3.4 to VCI and TCI. The results are mainly concurred with previous studies. Kogan et al.1. The relatively weak relationships between Niño 3.4 and VHI might because Niño 3.4 had mainly positively affected the VCI with respect to mainly negative impacts on the TCI. However, the results varied at different regions and period, and the influence mechanism of the impacts of Niño 3.4 on the VHI still need to be further investigated.The nonlinear relationships between Niño 3.4 and three VHIs revealed using cross wavelet transform and wavelet coherence were in accordance with linear relationships revealed using correlation analysis. The nonlinear wavelet common power and coherences were unstable and complex, changing both in time and frequencies. It is expected that Niño 3.4 extracts more influences on temperature than precipitation, given the fact that ENSO is associated with variations in SST in the tropical Pacific Ocean2, might be applied to predict vegetation growth and drought conditions based on revealed VHIs-climate relationships in Jing-Jin-Ji region.Our results in this study provide assessment of regional vegetation growth activity and drought estimation, which should be useful for the vegetation productivity assessment, drought detection and prevention, and also help to agriculture management for farmers and decision makers. Especially, the relationships between large-scale global anomalies and vegetation growth activity were further revealed using correlation and wavelet analysis, which expands our knowledge to predict vegetation and drought conditions using climate variables  and large-scale global climate anomalies  as potential predictors. However, this study has limitations which need to be further investigated. For example, the trends of SMT, SMT and precipitation, the percentage changes of area affected by drought and different vegetation conditions were applied to regional average values. It is gratifying that this does not affect our mainly conclusions revealed above. As an extension of this study, our future work will therefore focus on the vegetation growth and drought assessment for different vegetation types and different climate division, and hopefully, a linear and nonlinear combined predicted model, as developed by in previous study of Jiang et al.


Poly (ε-caprolactone) (PCL)/hydroxyapatite (HAP) composites represent a novel material with desired properties for various applications. In this work, PCL/HAP composites at low loadings were developed through melt-extrusion processing. The effects of HAP loading on viscoelastic, thermal, structural, and mechanical properties of PCL were examined. The morphological analysis revealed better dispersion of HAP at low loadings, while aggregation was noticed at high concentrations. The complex viscosity of the prepared composites increased with increasing concentration of HAP. In addition, a significant decrease in crystallinity was observed upon increase in HAP loading. However, the elongation at break increased with increasing the concentration of HAP, probably due to a decrease in crystallinity. The onset thermal degradation temperature of PCL was enhanced at low concentrations of HAP, whereas a decrease was observed at high loading. Overall, different degrees of HAP dispersion resulted into specific property improvement. In recent years, research effort has been diverted into developing environmentally benign materials. In this regard, the biodegradable polymers have received enormous interest to replace the conventional polymers, which are non-environmentally friendly ,2. Amongc) compared to neat PCL, indicating the nucleating effect of HAP in PCL. Further, the tensile modulus of the composites showed increasing trends when increasing the amount of HAP; whereas, the yield stress showed little dependence on the concentration of HAP suggesting better dispersion of the filler in the composites. Chuenjitkuntaworn et al. [Polymer composites represent the novel materials with specific functionality imparted by both the filler and polymer matrix. Usually, the intrinsic properties of a polymer can be altered by inclusion of filler in a polymer. Numerous filler materials have been incorporated into PCL to enhance its certain properties such as conductivity ,10, barrn et al.  developen et al.  and Zavřn et al.  preparedn) and weight average molecular weight (Mw) of PCL with increasing the amount of HAP. The reduction in Mn and Mw was associated with accelerated polymer chain scission by calcium phosphate such as HAP. At 25 wt.% HAP, the Mn reduced by approximately by 29% with respect to neat PCL. Generally, a reduction in molecular weight has detrimental effects on the properties of a polymer, which includes increase in melt flow index (MFI) and thus subsequent reduction in melt strength. Thus, it is important to minimize the amount of HAP in PCL composites to maintain or avoid huge reduction in molecular weight. For other applications, which do not necessarily need high loading of HAP, the composites can still be prepared and in such cases, the HAP will act as reinforcing agent and enhance some of the material characteristics at low amounts. In this study, compared to other studies which used high loadings of HAP, the effects of HAP nanoparticles incorporated at low concentrations on the properties of PCL were investigated. An insight on the resulting properties of PCL/HAP composites, particularly, the rheological, thermal, and mechanical behaviours is provided. The composites were prepared through melt mixing; a process that can be scaled-up to industry level and is suitable for other different process applications such as melt spinning, film blowing, etc. The concentration of HAP was varied from 1 to 7 wt.%, which is low compared to other studies that used high concentrations of 20 [Melt processing of polymer composites is crucial for a large-scale production since the process is continuous and free of solvents. However, the flow properties of the material need to be understood. This is important especially for continuous process applications such as film blowing and melt spinning, which require adequate melt strength for better processability and good dispersion and compatibility between polymer and filler for enhanced material properties. Post-melt processing of polymer composites such as melt spinning can be an alternative to the most used processes such as electrospinning of PCL/HAP composites. Nonetheless, the extent of filler dispersion, interactions, and the overall properties of the melt-processed composites require exhaustive understanding. It is worth mentioning that HAP is usually added at high concentrations and the prepared composites are intended to be applied for medical purposes. Generally, filler particles tend to agglomerate at high amounts and pose difficulties in material processability and harm the resulting properties. However, Akhbar et al.  preparedns of 20  and 60 wHAP  was purchased from Sigma Aldrich, South Africa. PCL (Celgreen PH-7) used in this study was obtained from Daicel Chemical Industries Co., Ltd., Tokyo, Japan. The material has melt-flow rate of 6 ± 0.4 g/10 min at 100 °C and weight of 2.16 kg, as determined according to ISO standard 1133 using CEAST MFM Multiweight instrument.The PCL pellets were dried for ~24 h at 30 °C in vacuum oven prior melt processing to remove moisture. The PCL/HAP nanocomposites, including neat PCL, were melt processed in twin-screw extruder  with L/D of 40. The materials were processed at temperature of 100, 100, 100, 100, 100, 60, 40 (die to feed zone). The extruded polymer strands were cooled in water bath, followed by palletisation, then drying at 35 °C for 24 h. The concentration of HAP was varied from 1 to 7 wt.% with respect to PCL, and the sample compositions were as follows: PCL0 (neat PCL), PCL1 (PCL/1 wt.% HAP), PCL3 (PCL/3 wt.% HAP), PCL5 (PCL/5 wt.% HAP), and PCL7 (PCL/7 wt.% HAP).The morphologies of cryo-fractured PCL/HAP composites, sputter coated with carbon, were determined using scanning electron microscopy (SEM)  at voltage of 3.0 kV.The level of HAP dispersion in PCL was investigated using polarized optical microscopy (POM). The samples were prepared into thin films, sandwiched between the glass coverslips, and then heated to 100 °C on the THMS hot stage  at heating rate of 20 °C/min. The samples were held isothermal at 100 °C for 10 min to ensure complete melting and the images were acquired using Carl Zeiss imager, Carl Zeiss, Germany).−1 at resolution of 4 cm−1.The chemical structures of neat PCL, HAP, and PCL/HAP composites were determined using attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy  at the wavelength range of 550 to 4000 cmThe crystalline structures of neat PCL, HAP, and PCL/HAP composites were determined through X-ray diffraction (XRD) X’Pert PRO diffractometer  producing Cu Kα radiation (λ = 1.54 nm), operated at 45 kV and 40 mA.The flow characteristics of the prepared samples were obtained using rheometer  operated at a temperature of 100 °C using parallel plates (25 mm) configuration, The frequency sweep tests were carried, with pre-determined strain amplitude of 1%.Thermal degradation properties of the PCL, HAP and PCL/HAP composites weighing approximately 8 mg were determined using PerkinElmer Pyris 1 TGA. The samples were heated from 30 to 800 °C in the steel pans, at a heating rate of 10 °C/min under nitrogen environment (flow rate = 20 mL/min).m)) occurring in PCL and PCL/HAP composites were investigated using differential scanning calorimetry (DSC) . The sample mass of approximately 5 mg was used for testing. The tests were run from −20 to 100 °C at a heating rate of 10 °C/min (both melting and cooling) under nitrogen environment with flow rate of 25 mL/min. The transitions occurring during melting were determined from the second heat curve.The thermal transitions  with load cell of 10 kN.The SEM micrograph of HAP particles is shown in −1. The other stretching peaks noticed at 2900 and 2800 cm−1 are attributed to asymmetric and symmetric –CH2 stretching, respectively. The other peaks could be noticed at 1293 and 1240 cm−1 and are attributed to C–O and C–C stretching in the crystalline phase and asymmetric C–O–C stretching, respectively [−1. The other peaks noticed at 590, 560, and 549 cm−1 are attributed to bending mode of P–O–P group [−1 and it could be attributed to the presence of HAP in PCL.ectively ,31,32. O–P group ,34. The The evolution of complex viscosity as a function of angular frequency is illustrated in η*, k, 0η, λ, n, and a represent complex viscosity, intrinsic material property, zero shear viscosity, relaxation time, power law index, and Newtonian transition factor, respectively.The viscoelastic behaviour observed in the prepared PCL-based composites was explained based on the complex viscosity modelling approach. The generalized complex viscosity equation (Equation (1)) is used to fit the experimental data and it can be expressed as follows ,36,37:–(4).In this Equation (1), depending on the boundaries of The Carreau–Yasuda model:The Cross model:The Berzin model:2 and G″ α ω. However, upon loading of HAP particles, the moduli increased over the entire range of angular frequency and followed similar behaviour as neat PCL. Comparing the two moduli, it is evident that the viscous response is dominant over the elastic behaviour (G″ < G′), showing the pseudo-liquid-like behaviour of the samples. Moreover, the increase in G′ with amount of HAP is of almost the same magnitude as of G″, suggesting equal effects of HAP on both viscous and elastic portion of PCL. At high amount of filler (7 wt.%), a clear plateau could be observed at the terminal zone (The dependence of both storage (G′) and loss (G″) moduli on the angular frequency is illustrated in nal zone b, which The transition from liquid to solid-like behaviour can be determined from damping factor (tan δ), which is the ratio of G″/G′. For ideal viscous behaviour, δ approaches 90° and tan δ approaches infinity, whereas the elastic behaviour is noticed when δ = 0° and tan δ is zero. Xc is percentage crystallinity, ΔmH is specific melting enthalpy of PCL, ΔH0m is specific melting enthalpy for 100% crystalline PCL and its value is 135 g−1, α is the weight fraction of HAP filler in PCL [r in PCL .Usually, nanoparticles tend to act as nucleating agents and increase the crystallinity and crystallization temperatures of a polymer; however, that behaviour could not be observed in this case. Looking at the crystallinity of PCL, it did not change significantly with varying the concentration of HAP. A slight decrease in crystallinity could be observed in PCL 7 and this is ascribed to poor dispersion of HAP , which rThe XRD is a powerful tool, which can be used to determine the crystallinity of the material and it is complementary to DSC. The thermal stability of the prepared samples was determined, and the results are shown in b) and ultimate tensile strength (UTS) as a function of HAP content. PCL is a ductile material with high Eb (>300 %) as observed in b did not change indicating no effect on the toughness of PCL. At 3 wt.%, a slight increase in toughness could be noticed. With increase in HAP up to 5 and 7 wt.%, the Eb further increased by 15 and 31 % with respect to neat PCL, respectively. Generally, the Eb is influenced by several factors including crystallinity, interaction between a polymer and filler, and degree of filler dispersion. Crystallinity is one of the governing factors for overall intrinsic properties of a polymer material. It can be deduced that a decrease in crystallinity contributed to increasing the Eb of PCL, though the expectation was that the Eb would decrease due to poor dispersion of HAP. However, A decrease in crystallinity as noticed from DSC and XRD suggests less confinement of PCL chains; hence, the increase in the mobility of chains and thus the toughness. Li et al. [b of electrospun PCL when HAP was introduced at 30 and 60 wt.%. It was concluded that the energy absorbed before the fracture increases in the presence of HAP. The UTS is important as it indicates the maximum stress the material can bear. The UTS of the composites is shown in The mechanical performance of the prepared PCL/HAP composites was investigated. The stress–strain tests were carried to determine the tensile properties of the samples and the results are depicted in i et al.  also obsb showed an increasing trend with increasing the HAP concentration. Although one would expect the composites containing high amounts of filler to have poor mechanical performance due to agglomerations noticed, the opposite trend could be noticed and was attributed other factors such as decreasing the crystallinity as noticed from the XRD and DSC analysis. The thermal stabilities, however, showed improvements only at low amount of HAP, possibly due to better dispersion. Overall, a new insight on the properties of melt processed PCL/HAP composites was provided. The developed materials can be used for different applications such as foams and films for packaging application, mulch films for agricultural applications, and textile applications such as short-lived geotextile applications.Environmentally benign materials with added functionality are desired for various types of applications. Herein, the effect of HAP loading at low concentrations was investigated. Numerous studies reported incorporated HAP at high amounts, and this can have significant influence on the reduction of molecular weight and properties of a polymer matrix. In this study, PCL/HAP composites were successfully prepared through melt processing technique. It was demonstrated that HAP disperse much better at low concentrations in PCL, whereas agglomerations could be noticed with higher loading of the HAP. The complex viscosity of the prepared composites increased significantly with increasing the amount of HAP in the composites. Increasing the complex viscosity can be an indication of increasing the melt strength of a polymer, and this can be crucial for further processes such as foaming that requires high/moderate melt strength and viscosity for bubble nucleation and growth, and for film blow process, which also requires high melt stability for bubble stability during blowing. On the other hand, the crystallinity of the composites decreased with respect to neat PCL as noticed from the XRD analysis and DSC, especially at high loadings. However, the tensile properties, particularly the E