Meloidogyne soil populations of soil disinfestation methods, such as agrochemicals, botanicals, or biosolarization were estimated on multiple field trials conducted over fourteen years in intensive horticultural crops. Soil nematode populations were reduced by 87 to 78% after fumigation with 1,3-dichloropropene + chloropicrin and dimethyl-disulphide, respectively. Non-fumigant nematicides such as azadirachtin, dazomet, fenamiphos, fluopyram, fosthiazate, metam-sodium, and oxamyl showed efficacies ranging from 51 to 64%, whereas the efficacy of natural products, such as abamectin, garlic extracts, or essential oils was 41 to 48%. Biosolarization with chicken manure had an efficacy of 73%. An economic cost-benefit study of nematode management methods was performed for seven vegetable–M. incognita pathosystems. Fumigation with 1,3-dichloropropene + chloropicrin and biosolarization with chicken manure were the only treatments able to reduce RKN populations above 1000 and 750 J2 per 100 cm3 of soil, respectively, to levels below the nematode economic damage threshold, keeping profitability. Fumigation was able to manage RKN soil densities up to 350 J2 per 100 cm3 of soil in most susceptible crops as aubergine or cucumber and up to 1000 J2 per 100 cm3 of soil for more tolerant crops, such as other cucurbits, pepper, or tomato. Other nematicidal treatments were not able to reduce RKN populations above 200–300 J2/100 cm3 of soil below the economic thresholds but were profitable when RKN densities were below the limits of 200–300 J2/100 cm3 of soil.Losses caused by phytoparasitic nematodes in crops depend directly on their soil densities at the start of the crop, so reducing their populations before planting is the main aim of nematological management. Efficacies in reducing  Intensive horticulture is a key agricultural industry in southern Europe. High-value crops such as cucurbitaceous  and solanaceous crops  are grown under plastic protection in unheated greenhouses during the autumn–winter months and are exported for fresh consumption to European markets and elsewhere. In south-eastern Spain, there are over 40,000 ha of protected vegetable crops producing around 4,000,000 tons, with a commercial value of about 3000 million € per year .Meloidogyne spp.) are seen as the most impacting and frequent limiting factor to achieving premium quality and economically sustainable yields in intensive horticulture [Plant parasitic nematodes are major limiting factors for vegetable production worldwide ,3. Estimiculture ,5. RKN piculture .Yield losses caused by nematodes are related to nematode densities at planting (Pi) and the relationship was mathematically described by the Seinhorst damage function model . This moSince nematode-caused yield losses are related to Pi, nematode management methods have been based on reducing the nematode densities before the crop is planted. To keep the profitability of the nematicidal treatments, their cost should at least be balanced with the yield losses that will be avoided. The nematode density at which the value of the yield losses are equal to the cost of management practice is known as the economic threshold (ET) , and it Bacillus firmus and Purpureocillium lilacinum) are approved for use against RKN in the European Union (EU) [Conventionally, the management of RKN has relied on soil disinfestation by chemical nematicides, which are classified as fumigant (gas) and non-fumigant (contact agents). All these nematicides are broad-spectrum pesticides effective in reducing RKN soil populations, but they may have undesirable side effects on beneficial soil organisms . Increasion (EU) . Other aion (EU) . Despiteion (EU) ,5,10. Neion (EU) . Physicaion (EU) . TherefoThe economic return of a crop is determined by its yield, costs associated with production, and the price obtained. Cost estimations assume a static price for land rent and average prices for input costs and management practices. This information is regularly available to growers through periodic publications by agricultural extension services of the regional government . HoweverIntegrated nematode management systems (INM) should be tailored for each crop-nematode pathosystem based on the scientific knowledge on the plant-nematode interaction, nematicidal efficacy, costs and profit balance, and environmental and health side effects of the management methods. However, due to the limited availability of data, a great deal of uncertainty exists as to how growers will deal with high RKN infestations in intensive horticulture. Nematicide performance models, including effectiveness, financial and environmental aspects, are therefore crucial tools for the design of INM and should be adopted to advise growers through agricultural extension activities .This paper examines nematicidal efficacies, economic cost, net returns, and profitability of current RKN management methods in the European Union based on field data and statistical sources for the main vegetable crops in intensive horticulture. The main objective of this study aims at generating information to help decision-making for nematode management decision making, according to the various attributes involved, such as nematode densities previous to any nematicidal treatment and at planting, the cost of the nematicidal treatments, their field efficacy, and their economic threshold.The most efficient treatments against RKN were soil fumigation with 1,3-dichloropropene + chloropicrin and dimethyl disulphide, which reduced RKN densities by 87% and 78%, respectively . Metam-sThe cost of the nematicidal treatments ranged from 160 €/ha for a single oxamyl treatment to 2700 €/ha for biosolarization with chicken manure .Production costs, yields, revenues, and net returns for vegetable intensive cultivation in south Spain are summarized in Tolerance limits (T) and minimum yields at the highest nematode densities (m) were calculated from the Seinhorst damage function models reported in the literature .3 of soil for aubergine, 51–218 for pepper, and 98–205 for tomato  that each nematicidal treatment could manage to get RKN soil densities at planting (Pi) at the economic threshold level were calculated according to the nematicidal efficacies obtained in field trials .3 of soil before planting are found in some fields dedicated to intensive horticulture in southern Spain [Negative net returns were obtained at the hypothetical highest RKN densities for all vegetable–RKN pathosystems, which proves the necessity for nematode management in these horticultural intensive crops, in case of high RKN soil infestations. Pi levels higher than 200 J2/100 cmrn Spain .Nematicidal efficacies, expressed as the reduction in RKN populations due to the nematicidal treatment after removing the natural RKN mortality in soil showed and ample range of variation from 40% to 87% .B. firmus or P. lilacinus could not be determined since their modes of action do not reduce RKN soil densities but protect the plant from nematode infection or parasitize the RKN eggs.The superior efficacy of soil chemical fumigation against RKN has been previously reported in vegetable crops ,9,10,11.M. incognita were tomato and pepper, with average tolerance limits of about 92 and 41 J2/100 cm3 of soil, respectively. These tolerance limits were averaged from the values reported in the literature in pots and field experiments and depend on the local conditions in which the experiments were carried out, soil type, and temperature, crop cultivar, and the RKN inoculum used, showing in some cases a high variability. Tomato tolerance limits to M. incognita varied between 2 and 400 J2/100 cm3 of soil [3 of soil [M. incognita with tolerance limits below 6 J2/100 cm3 of soil [3 of soil [M. incognita was aubergine (95%), but at the highest RKN densities, pepper and tomato can lose up to 52% and 69% of the yield, respectively. Cucurbit maximum yield losses varied between 57 and 80%.The most tolerant crops to  of soil ,28,29,30 of soil ,25. Aube of soil ,23,31,32 of soil . The cro3 of soil, depending on the susceptibility-tolerance of the crop to the RKN species, the yield revenue, and the cost of the nematicidal treatment. When nematode densities were above the ET, the nematicidal treatment would be economically justified since the increases in the net return obtained would be higher than the cost of the nematicide treatment. These nematode economic damage thresholds assumed yield losses from 0.6 to 27.1% of the net return, a highly variable range, depending on the susceptibility-tolerance of the crop to the RKN species, the yield revenue, and the cost of the nematicidal treatment. Most nematicidal treatments were under the cost of 1000 €/ha and showed economic thresholds from 6 to 110 J2/100 cm3 of soil, with associated net return losses 1–10%. More expensive treatments (1550–2700 €/ha) had economic thresholds from 51 to 306 J2/100 cm3 of soil and associated net return losses from 5% up to 27%. The assumption of these net return losses by farmers will depend on the total revenue obtained, being more acceptable in high-valued crops since the higher revenues obtained will compensate for higher nematicidal costs. All costs for non-fumigant nematicides in the study were based on one single application before planting, but some of them can be additionally applied several times in post-transplanting, when the crop is growing. In such cases, the costs would be increased, but also the efficacies in controlling RKN diseases and the net return obtained. These variations in the nematicide application should be considered when assessing the profitability of a nematicidal treatment if more than one application of the product is to be done.The nematode economic threshold varied between 6 and 346 J2/100 cmM. incognita for aubergine, 58–453 for cucumber, 137–1070 for pepper, 154–1203 for zucchini, 155–1211 for tomato, 160–1250 for watermelon, 181–1414 for melon , they still result in the treatments that can manage the highest RKN soil densities keeping profitability.The economic thresholds do not consider nematicidal efficacies in their calculation, but it is a critical issue to assess the economic profitability of treatment because more efficient treatments can reach the economic threshold from higher field RKN densities than less efficient treatments. Thus, the incorporation of nematicidal efficacies into nematicide performance models will allow us to estimate a range of field P0 that each treatment could afford to keep profitability. For example, such a P0 range for 1,3-dichloropropene + chloropicrin (87% efficacy) was 51–398 or melon . Even foM. incognita J2/100 cm3 of soil for aubergine, 103–381 for cucumber, 205–759 for tomato, 218–807 for pepper, 293–1085 for zucchini, 306–1133 for watermelon, and 346–1281 for melon. Biosolarization with chicken manure at the cost of 2700 €/ha, could still be profitable for the most susceptible crops, aubergine, and cucumber, providing RKN soil densities in field plots that were under 322–388 J2/100 cm3 of soil. In less susceptible crops, profitability was kept even at high RKN soil infestation (>750 J2/100 cm3 of soil).The most expensive treatment, biosolarization with chicken manure (2700 €/ha) and a 72–73% efficacy was profitable within a P0 range of 87–322 M. incognita J2/100 cm3 of soil for cucumber, 11–76 for aubergine, 16–209 in zucchini, 16–215 in watermelon, 22–247 in melon, 51–224 for pepper, and 98–294 in tomato. Therefore, the use of these nematicidal treatments would not be profitable in those field plots where RKN soil densities were above 76–81 J2/100 cm3 of soil if the crop to be planted were aubergine or cucumber and profitability of the treatment would also be compromised in less susceptible crops where RKN soil densities were above 300 J2/100 cm3 of soil.For less efficient nematicidal treatments (50–65%) with a cost below 1000 €/ha (160–970 €/ha), the P0 ranges in which the nematicidal treatment would be profitable were 6–81 3 of soil, some nematicidal treatments would not be able to reduce RKN densities below the economic thresholds in most susceptible crops, such as aubergine or cucumber, keeping profitability. In such cases, additional RKN management methods should be applied to further reduce RKN densities from P0 to Pi, or to reduce the yield losses caused by nematodes, i.e., the use of resistant cultivars or the use of biocontrol agents that can reduce multiplication of nematodes once the roots are infested.Overall, when the P0 are within these ranges, the Pi after the nematicidal treatment result in net return losses lower than the treatment costs, and therefore, treatments will be clearly profitable. In case of P0 densities below the lower limits of these ranges  of theseMeloidogyne incognita .Data on the relative efficacy of various nematicidal treatments in reducing RKN soil populations were obtained from a series of field trials carried out during the period 2007–2021 in the Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA) at a plastic greenhouse located at the IFAPA Chipiona experimental station, Cádiz, Spain (36°45′ N—6°24′ W) used for regular cultivation of vegetables and naturally infested with 2). Each single nematicidal treatment was replicated three times and included in four different field trials (n = 12).In each field trial, up to twelve different nematicidal treatments plus an untreated control were set in a randomized complete block design and distributed over thirty-nine plots  in cultivation lines, except for granular products that were distributed and mixed with the soil of the cultivation lines and watered by drip irrigation according to manufacturer instructions. All chemical treatments were applied at the doses indicated by the manufacturer 4–6 weeks before planting . Biosola3 of the mixed soil by the Whitehead and Hemming tray method [3 of soil were included in the respective trials. The relative nematicidal efficacies were determined using the Schneider–Orelli correction [To determine the nematicidal efficacy of each treatment, changes in RKN soil densities were recorded two times; pre-treatment (P0) and pre-planting (Pi). At each sampling time, ten cylindrical soil cores were taken per plot using an Auger sampling tool (2 cm in diameter to 30 cm deep) and the soil cores were mixed into a single composite soil sample. Nematodes were extracted from subsamples of 250 cmy method . Only plrrection , based oA cost-benefit analysis was conducted to assess the economic profitability of each nematicidal treatment. Cost and profits were estimated for each crop based on agricultural statistical data for the 2019–2021 seasons . The netYield losses caused by RKN were estimated according to the Seinhorst damage function models, which relate yield losses to the nematode soil densities at planting time (Pi) .Y = m +Nematicide prices were quoted from local vendors. From February to November 2020, a poll on the nematicide costs was carried out by face-to-face interviews with nine local vendors in intensive horticultural areas of south Spain. An average cost for each nematicidal treatment was calculated for a single application of the product before planting, according to the maximum dosage recommended by manufacturers for intensive horticultural crops. Nematode economic thresholds (ET), defined as the population density at which the value of the yield loss equals the cost of the management method, were calculated for each nematicidal treatment using the Seinhorst damage function models, with the relative yield (Y) expressed as net return values in €/ha . Maximum3 of soil, keeping profitability for all vegetable crops in intensive horticulture in South Spain. The only nematicidal treatments that could manage high RKN soil infestation, above 350–400 M. incognita J2/100 cm3 of soil, in all intensive horticultural crops while keeping profitability were 1,3-dichloropropene + chloropicrin and biosolarization with chicken manure.At current nematicide prices, most nematicidal treatments were able to manage RKN soil infestations of about 200 J2/100 cmEstimation of RKN soil densities present in the field before any nematicidal treatment (P0) is a valuable tool for decision-making in integrated nematode management since growers can decide nematicidal treatments to be used or crops and cultivar to be planted based on RKN soil population levels. A balance should be kept between the cost of nematicidal treatments and their efficacies in reducing RKN soil populations to maximize profitability in the field, even at high RKN soil infestations.


Aerococcus, Corynebacterium, Facklamia, and Psychrobacter taxa with more abundance in the high group of feed efficiency, whereas, in resilience to mastitis, only two genera of Mycoplana and Rhodococcus were more abundant in the low group. In addition, LEfSe analysis between the seasons showed significant differences in the abundance of Bacteroides, Lactobacillus, Corynebacterium, Escherichia, Citrobacter, Pantoea, Pseudomonas, and Stenotrophomonas. These findings indicate that the different genetic predisposition for feed efficiency and resilience to mastitis could affect the raw milk microbiota and, consequently, its quality. Moreover, we found more abundance of mastitis-associated bacteria in the milk of dairy cows with a higher feed efficiency index.The main objective of this study was to assess the microbiota diversity in milk samples collected from Holstein cows with different estimated breeding values for predicted feed efficiency, milk coagulation, resilience to mastitis, and consequently, to study its effects on milk quality. One hundred and twenty milk samples were collected in two seasons (summer and winter) from different commercial dairy farms in the Nord-east of Italy. For each trait, 20 animals divided into two groups of the high (10 cows) and the low (10 cows) were selected to study the microbiota profile using 16S rRNA metabarcoding sequencing. The alpha and beta diversity analysis revealed significant differences between the high and the low groups for feed efficiency and resilience to mastitis, while no significant difference was detected for milk coagulation. Moreover, remarkable differences among the taxa were detected between the two seasons, where the winter was more diverse than summer when applied the Chao1 index. Lastly, the linear discriminant analysis (LDA) effect size (LEfSe) indicated  Lactococcus, Lactobacillus, Pseudomonas, Micrococcus, Staphylococcus, and yeast2. Other microbial groups in raw milk belong to the Leuconostoc, Enterococcus, Streptococcus, Bacillus, Clostridium, Listeria, and Enterobacteriaceae. Besides, many gram-negative bacteria such as Acinetobacter, Alcaligenes, Flavobacterium, and Aeromonas species1. Milk is a vibrant food with high nutritional value since it contains all essential food ingredients like minerals, protein, fat, and lactose3. Based on recent studies, milk microbiota composition can contribute to milk quality to a great extent; notably, some bacteria, such as lactic acid bacteria, can play a critical role in high milk quality5.Milk contains different types of microorganisms. The most predominant microorganisms that compose milk microbiota include various species of 7. During the last years, molecular techniques such as 16S rRNA metabarcoding have become more popular since using the 16S rRNA sequencing method allows us to study both live and dead bacteria while we lose the dead cells by culture-dependent technique in that environment8.Milk can be examined with both culture-dependent and culture-independent approaches9.Nowadays, dairy consumers pay more attention to the safety and sensory quality of milk and dairy products compared to the past, which could depend on different factors such as cattle's diet and breed, milk processing , and milk preparation10). Feed efficiency improvement has resulted from high milk yield-oriented breeding programs and proper feeding and management of animals over the last years; however, its effect on milk microbiota and microbial milk quality has not been appropriately studied yet10.Feed efficiency is one of the most critical factors in milk production, which means, evaluation of the cow's ability to convert the amount of food that has been received to the amount of milk that has been produced  for feed efficiency, milk coagulation traits, resilience to mastitis, and consequently, to study its effects on milk bacterial community.Overall more than 70% of milk production is used to manufacture cheese in Italy, and 55% of the total milk production is processed for PDO (Protected Designation of Origin) cheeses like Grana Padano, Provolone, Gorgonzola, Parmigiano-Reggiano, Asiago, and Mozzarella19. Derakhshani et al.20also observed that udder inflammation related to mastitis was negatively correlated with milk microbiota; therefore, resilience to mastitis could affect milk microbiota composition as well. Hence, animal selection based on different genetic predispositions for feed efficiency and resilience to mastitis could be a possible indicator of final raw milk microbiota composition.In the current study, the microbial community of 120 raw milk samples collected in two different seasons (60 samples during summer and 60 samples during winter) from 60 Italian Holstein Friesian cows based on different EBVs for FE, RM, and MC, has been investigated. The average EBV for the high FE group was calculated at 1.48 ± 0.05 (kg of milk yielded/ kg of dry matter intake), while the low group was 1.19 ± 0.12 (kg of milk yielded/ kg of dry matter intake). Regarding the RM, the average EBV for the high group was determined at 111.41 ± 1.92, whereas the low group was 88.08 ± 2.42. Lastly, the average BLUP value for the high MC group was calculated at 114.74 ± 0.099, while for the low group was 85.61 ± 0.63. Animal selection for traits such as feed efficiency or resilience to mastitis may potentially affect final raw milk microbiota composition and its final quality. In several studies, strong positive genetic and phenotypic correlations between milk yield and feed efficiency have been reportedA total of 3,441,325 high-quality reads with an average read count of 28,677 per sample were obtained after removing low-quality and chimeric sequences using a 16S rRNA metabarcoding analysis of the V4 hypervariable region and MiSeq sequencing system. After clustering the OUT table based on classification at a 97% similarity using BLASTn, 407 OTUs were considered for analysis after removing low abundance and low variance features as explained in the “p < 0.05); however, no significant differences was detected in MC (high and low) as well as between summer and winter  only between the seasons, where the winter appeared as a more diverse season  and RM ; however, no significant difference  was recorded between the groups of MC , indicating a high beta diversity in microbial structure when two seasons were compared with each other  of FE   more abundant in the high group  at the genus level revealed significant differences between FE and RM's high and low groups. Regarding the FE, oup Fig. . On the oup Fig. .Figure 4Bacteroides, Lactobacillus, Corynebacterium, Escherichia, Citrobacter, Pantoea, Pseudomonas, and Stenotrophomonas  greater bacterial richness in winter which is in contrary with abovementioned studies. Indeed, another study by Olde Riekerink et al. reported that greater bacterial richness during winter could happen due to the appearance of psychrophilic milk spoilage and mastitis-related bacteria such as S. aureus and S. dysgalactiae30. In our study, Bacteroides, Lactobacillus, and Corynebacterium were more abundant in winter, whereas the Escherichia, Citrobacter, Pantoea, Pseudomonas, and Stenotrophomonas indicated the greater abundance in summer. These seasonal differences in milk microbiota might be related to milk exposure time to the environment, resulting in a greater diversity of bacteria in winter31. Other possibilities could also be related to the differences in housing and feeding during summer and winter31.In some studies, it has been reported the increase of bacterial richness in the summer season compared to winterAerococcus, Corynebacterium, Facklamia that are all related to prevalence of dairy cow mastitis. Based on collected results in this research, the dairy cows with the higher feed efficiency indicated more abundance of mastitis-associated bacterial taxa in their raw milk microbiota. On the other side, the low group of resilience to mastitis showed more abundance of Rhodococcus among the microbiota. This bacterium has also been reported frequently as a mastitis-associated microorganism. The current study's findings indicate that the different genetic predisposition for feed efficiency and resilience to mastitis could affect the raw milk microbiota diversity.This study characterized the bovines' raw milk bacterial community with different estimated breeding values for traits such as feed efficiency, milk coagulation, and resilience to mastitis. Although no significant differences were detected when the groups of milk coagulation were compared at different taxonomy levels with each other; however, regarding the feed efficiency, the outcome of microbiota diversity analysis revealed the increase of genera Sixty Italian Holstein Friesian cows from 28 commercial dairy farms, all located in Veneto Region, Italy, were selected based on their estimated breeding values for: (1) predicted feed efficiency (FE), (2) resilience to mastitis (RM), and (3) milk coagulation trait (MC). For each trait , 20 animals  were selected to be studied and compared regarding the raw milk bacterial diversity in two different seasons (summer and winter 2020).All animals rearing and handling procedures were carried out in accordance with the European Commission recommendation 2007/526/EC and Directive 2010/63/UE on revised guidelines for the accommodation and care of animals used for experimentation and other scientific purposes.Regarding resilience to mastitis, the index was created using four novel traits that have shown the strongest genetic correlation. These traits are the mean of somatic cell count (SCC) between 5 and 150 days in milk (DIM), the standard deviation of SCC within lactation, severity, defined as the ratio between the number of test-days with SCC greater than 400,000 cells/mL and the total number of test-days within lactation and peak defined as the number of peaks during lactation . The environmental effects considered in the model were herd-year-season, age at first calving, and the number of test-day records within lactation. The random effect was the animal effect, considering bulls and cows simultaneously, accounting for the genetic level of mating and using all available pedigree information. Heritability (h2) of the aggregate udder health index was 15%. The index was expressed on a scale with a mean of 100 and SD of 5. The genomic index was calculated following the following steps: genotyping individual animals, collecting phenotypes and estimating the value of the individual markers (SNP). The model used to estimate these effects includes the 68,000 markers obtained after the initial editing procedures and a so-called “polygenic” effect, which was included in the model through the kinship information of the animals. The part of the variability not explained by the genetic markers can be recovered by incorporating kinships between the animals into the model:.32 to give the aggregate index of milk aptitude to coagulate. Milk coagulation was introduced as a new standardized trait to summarise RCT and a30, with the same index importance (50%). The following linear model was used to predict EBV of milk coagulation trait:For milk coagulation properties, the main milk coagulation traits studied are milk rennet coagulation time  and curd firmness , but these two individual traits have been combined according to Penasa et alFinally, predicted feed efficiency recording data are available in the ANAFIBJ database, coming from milk recordings and type evaluations. Starting from this information and the age of the animal, the bodyweight (BW) was estimated based on the predicted BW and the fat corrected milk production (FCM). An estimation of the dry matter intake (DMI) was made. Predicted feed efficiency was calculated as the ratio between the energy corrected milk production  and the predicted DMI. The model was an animal model with repeated measures. The environmental effects (fixed effects) taken into account are the interaction between calving age and parity, the interaction between days in milk (gathered into classes of 30 days each) and parity and the herd-year-recording day effect. The animal and the permanent environmental effect were considered as random effects. The predicted feed efficiency index was expressed on scale 100 and SD of 5 just like for the other functional traits. The bulls with index greater than 100, transmit a higher feed efficiency than the average of the genetic base.One hundred and twenty raw milk samples were collected during the summer (60 samples) and winter (60 samples) in 2020. It is worth mentioning that the same animals were used for both seasons. The milk samples were collected through an automatic sampler installed in the milking parlor during the evening and after cleaning bovine teats with osmosis water and 70% ethanol.After the sample collection, 200 µL of Bronopol  was used in 40 mL of milk as the preservative, and the milk samples were transferred at 4 °C to the food microbiology laboratory of the University of Padova .g for 20 min at 4 °C and the pellet were used for gDNA extraction using the DNeasy PowerSoil Kit  according to the manufacturer instructions. Genomic DNA was sent for Next-generation sequencing (NGS) at Molecular Research DNA . The hypervariable region V4 of the the16S rRNA gene was chosen to study the microbial profile. The universal primers 515/806 were used to amplify the V4 region in a 30-cycle PCR using the HotStarTaq Plus Master Mix Kit  using the following conditions: 95 °C for 5 min, followed by 30–35 cycles of 95 °C for 30 s, 53 °C for 40 s and 72 °C for 1 min, after which a final elongation step at 72 °C for 10 min was performed. Later, 2% agarose gel was used to assess the amplification success and intensity of the bands. Multiple samples were pooled together in equal proportions based on their DNA concentrations and molecular weight, purified using calibrated Ampure XP beads, and the Illumina DNA library was prepared. Sequencing was performed on a MiSeq (MR DNA) following the manufacturer's guidelines, generating 250 bp paired-end (PE) reads.All 120 raw milk samples collected during summer and winter were thawed on ice, 10 mL milk was centrifuged at 5500Sequence data were processed using the Molecular Research DNA analysis pipeline (MR DNA). Briefly, sequences were joined, barcodes, sequences < 150 bp, and ambiguous base calls were removed. Then, sequences were denoised, operational taxonomic units (OTUs) generated, and chimeras depleted. Final OTUs were taxonomically classified at a 97% similarity using BLASTn. Raw reads were deposited in the Sequence Read Archive (SRA) database under the BioProject number “PRJNA789190”.33. The OTU table and the experimental design metadata file were imported into the MicrobiomeAnalyst server, where the low count/variance sequences removal and total sum scaling (TSS) conduction was performed using default parameters, and the OTU tables were rarefied to the minimum library of 12,837 sequences to avoid bias due to sequencing depth among different samples.The statistical analysis was carried out using the MicrobiomeAnalyst server35. In each trait , the high and the low groups were compared by setting the statistical approach on t-test/ANOVA. The same approach was used to compare the alpha diversity between the summer and winter seasons.The alpha diversity was calculated using Shannon and Chao1 indexesMoreover, the principal coordinate analysis (PCoA), the Bray–Curtis index, and permutational multivariate analysis of variance (PERMANOVA) were chosen as orientation, distance, and statistical methods to calculate the beta diversity among the groups.Finally, the significant OTUs differences between the high and the low groups in each trait  were analyzed using the linear discriminant analysis (LDA) effect size (LEfSe) tool using default parameters. The same approach was used to compare the significant OTUs differences between the summer and winter seasons.Supplementary Information 1.Supplementary Information 2.


After three decades of coral research on the impacts of climate change, there is a wide consensus on the adverse effects of heat-stress, but the impacts of ocean acidification (OA) are not well established. Using a review of published studies and an experimental analysis, we confirm the large species-specific component of the OA response, which predicts moderate impacts on coral physiology and pigmentation by 2100 (scenario-B1 or SSP2-4.5), in contrast with the severe disturbances induced by only +2 °C of thermal anomaly. Accordingly, global warming represents a greater threat for coral calcification than OA. The incomplete understanding of the moderate OA response relies on insufficient attention to key regulatory processes of these symbioses, particularly the metabolic dependence of coral calcification on algal photosynthesis and host respiration. Our capacity to predict the future of coral reefs depends on a correct identification of the main targets and/or processes impacted by climate change stressors. A combination of experimental and literature analyses indicate that heat stress is a greater threat for coral calcification than ocean acidification, stressing the need for further investigation on key regulatory processes. Coral reefs are particularly affected2, as thermal anomalies of +1−2 °C above the regional maximum average in summer are considered the principal driver of severe loss in coral pigmentation4 and symbioses function5. This phenomenon, known as coral bleaching, is responsible for massive coral mortality6, with dramatic consequences for coral reefs7. Coral bleaching is predicted to increase in both severity and frequency as a result of climate change6. The impact of OA, resulting from the doubling of pCO2 pre-industrial concentrations to 560 ppm, also predicts a 40% reduction in coral calcification by 21008. However, while there is a large consensus on the impact of thermal stress for the induction of coral bleaching11, the OA effects on coral physiology have not been clearly established  are considered the major global threats to marine ecosystemsed Table .Table 1SFv/Fm), and it is preceded by severe loss of coral photosynthetic performance including reductions in coral pigmentation and symbionts12. Although reductions in Fv/Fm and in coral pigmentation are commonly used as proxies for coral bleaching, the “bleached phenotype” only occurs at the end of the physiological disturbance14, and expresses a dysfunctional condition of the symbiotic association, detectable, as previously proposed, by full suppression of coral photosynthesis13. The onset of heat-stress is determined by a temperature threshold, known as the “Arrhenius break temperature” (ABT)15, above which coral photosynthesis17 and calcification18 diminish with temperature. Such a tipping point has not been documented for coral respiration at this temperature5. Below ABT, exposure to elevated temperatures is generally beneficial for all metabolic rates, as they accelerate the enzymatic processes. Values for ABT and for the Q10 temperature coefficient  of symbiotic corals are variable among species, metabolic processes, and the acclimatory phenotype of the organisms5.Coral bleaching is caused by a large accumulation of light-induced photodamage in the symbionts during heat-stress, evidenced by the reduction in the maximum photochemical efficiency (19 (Table 2 by the ocean surface modifies seawater chemistry leading to a reduction in pH and aragonite saturation state (Ωarag)19. Initially, OA was documented to affect the biomineralization process in a wide range of marine calcifying organisms20, including planktonic coccolithophorids20, foraminifera20, crustaceans21, molluscs21, coralline algae23, and corals25. Further studies, however, started to question such adverse effects of OA on marine calcification27. Changes in Ωarag were found to be positively correlated with the decline in coral calcification28, but later this decline was found to be associated with changes in seawater-pH and pCO2 rather than Ωarag per se29. Other studies have reported no reductions in coral calcification or even its stimulation under OA conditions 43. As a prerequisite to elucidate the direct physiological impact of each stressor on coral performance, the experiment was fully factored designed for temperature and pCO2, and maintained moderate levels of light stress throughout the experiment. We characterized the variation in coral physiology, coral optics, and structural traits at days 0 and 10. Daily changes in solar radiation, the magnitude of photodamage accumulation, and light absorption during the experiment were also monitored. Photodamage accumulation was quantified by daily dusk measurements of the maximum photosystem II, PSII, and photochemical efficiency (Fv/Fm).To resolve these diverse and often contradictory interpretations, we investigated here the physiological response of four Caribbean reef-building species, Fv/Fm declines in both treatments, whereas no significant change was observed in the OA treatment  and structural traits  showed no changes after exposure to control conditions. Only O. annularis presented a slight increase in Ci together with small reductions in calcification , whereas the smallest reductions (57%) were measured for P. strigosa , O. faveolata (52%) and O. annularis (69%). In M. cavernosa, such decrease was due to a 50% reduction in symbiont Ci, as symbiont losses were insignificant  was only measured for P. strigosa and M. cavernosa, whereas O. annularis still maintained 13% and 6% of the control photosynthetic activity in the two heat-stress treatments, despite its large Chla and symbiont losses  and O. annularis  ranged from 1.96 in O. annularis to 2.3 in O. faveolata, and did not change during the experiment in the control treatment and with increasing levels of CO2 under control temperature . For M. cavernosa, there was significant decalcification activity after exposure to the combined treatment , and one sample of O. faveolata. The second was represented by all four samples of P. strigosa and two samples of O. faveolata.  and no change in Pmax despite small reductions in pigmentation and symbiont content . Such decreases in pigmentation under low pH, however, were not significant for any species, when comparing the variability among species , whereas Gmax showed slight reductions  and the “stressed” coral phenotypes, as previously proposed14.Similar substantial impacts of heat-stress on coral photosynthesis and calcification were observed in the four species, despite the potential genetic variability of the dominant symbiont typesris Fig. . In this56 or even positive synergistic effects of the interaction with heat-stress33 , and only 30 studies (including anemones) document changes in photosynthesis, while only 13 characterize changes in symbiont and pigment content. In our review, only 19 studies measured OA effects on both coral calcification and photosynthesis, and even fewer, 5 studies, have considered the evaluation of the OA effect on symbiont and pigment content. Hence, this literature evaluation unveils the still incomplete physiological characterization of the coral response to OA. Likewise, our experimental analysis indicates that insufficient attention to coral respiration and to the metabolic-dependence of coral calcification on photosynthesis and respiration has also affected the understanding of this response. Coral biomineralization in addition to inorganic carbon requires oxygen and the energy supplied by both, algal photosynthesis and host respiration37. We observed increases in coral respiration, although not significant, at an enhanced DIC supply , an observation consistent with previous findings and with a transcriptomic analysis57. Furthermore, heat-stress caused severe reductions in the photosynthesis to respiration ratio (P/R) of all species, which may have led to a differential energy shortage (ATP synthesis) and reductions in oxygen delivered to support coral calcification37. Hence, attention to the variation in coral respiration and to the metabolic-dependence of coral calcification on photosynthesis and respiration rates, is key for disentangling this response under stress. In fact, coral respiration is not only an important source of metabolic energy (ATP) and respiratory carbon for maintaining coral calcification, but also a source of reactive oxygen species, ROS58. Moreover, the control of cellular redox balances and ATP synthesis relies on mitochondria59.Positive and negative effects of OA on coral calcification, photosynthesis and symbiont and pigment content have been documented, according to our literature review, as have the absence of any impact33 Table . This re61 and no limitation63. The finding of a large species-specific variability has led to the interpretation that DIC may have an environmentally unregulated control or the occurrence of an ‘intentional’ DIC limitation by the host. Our study cannot provide any insight into the biological mechanisms, but certainly uncovers a substantial species-specific component, and/or related to the coral condition/phenotype, of the coral response to OA. We could identify in this study two types of responses. One, represented by O. annularis and M. cavernosa and that also included a sample of O. faveolata, was characterized by slight enhancements in calcification and no impact on photosynthesis despite small reductions in symbiont and pigment content. The other, represented by P. strigosa and two samples of O. faveolata, was characterized by increases in symbiont density and photosynthesis, with minor adverse effects on calcification after 10 experimental days. Biological processes such as the downregulation of energetically expensive carbon concentration mechanisms and/or the reallocation of this energy into different ‘metabolic routes’, such as calcification or photosynthesis could explain this variability. In support of this interpretation, a recent transcriptomic analysis64 has documented the downregulation of genes involved in carbon acquisition by the symbionts under elevated pCO2. The present comparative analysis cannot explain the ‘variable’ coral response to OA observed, but allowed highlighting important gaps in the knowledge of the regulation of these symbioses that still need to be elucidated in order to understand it. This variability may have a species-specific component, as well as be regulated by the physiological condition of the holobiont , as observed for O. faveolata.It has been postulated that an increase in DIC supply under acidic conditions may favor coral calcification and photosynthesis. Evidence has been reported both in support of DIC limitation of coral photosynthesis65, as experimental analyses cannot reproduce the complexity of interactions that occur in nature. Global projections using meta-analysis, however, have concluded that OA will cause significant additional impacts under intensifying marine heatwaves35; or that the large declines predicted in net carbonate production of coral reefs are not direct impacts on organism’s calcification rates or bioerosion, but reduction in coral cover66. Our study does not support these conclusions, and show a minor effect of OA on coral calcification. Studies on carbonate dissolution, have also documented that OA will affect coral reefs through an increase in net sediment dissolution69. As the capacity to predict the future of coral reefs depends on a correct identification of the main targets and/or processes impacted by climate change stressors, experimental analyses cannot be neglected, as they are essential in isolating and thus elucidating both the individual impacts and possible interactions of environmental factors on organism performance. Nonetheless, the success of experimental studies depends on the level of knowledge of the system under analysis. Hence, a better understanding of the entirety of the physiological and cellular processes evoked or compromised under stress is fundamental to improve our capacity to predict the impact of climate change on the main reef builders.It has been argued that only meta-analyses can contribute to global projections of climate change impacts on coral reefs at the end of the 21st centuryin hospite exacerbation of light-stress in the symbionts, whereas ocean acidification induces moderate effects on coral metabolism, some of them even positive. The impact of heat-stress on coral calcification primarily relies on the dependence of the biomineralization process of algal photosynthetic products, a process traditionally known as ‘light-enhanced-calcification’70. However, coral respiration also plays an important role as another source of carbon and metabolic energy for coral calcification that needs to be understood. We hypothesize here that two contrasting pathways in holobiont carbon allocation, and in the flexibility of the regulation of the metabolic link between algal photosynthesis and host calcification, may contribute to explaining the large variability of moderate effects induced by ocean acidification on coral physiology. A better knowledge of this regulation is fundamental for elucidating the impact of climate change on symbiotic corals, and it may also allow explaining why a robust species to heat-stress such as Orbicella faveolata, also presents one of the largest adverse impacts of global warming on calcification.In summary, our study supports that global warming represents a more significant threat to coral growth and reef accretion than ocean acidification. Heat-stress directly affects coral performance through Pseudodiploria strigosa, Montastraea cavernosa, Orbicella annularis, and O. faveolata were collected at a depth of 4–5 m in the back-reef of Puerto Morelos, Mexico . Coral fragments were transferred to the UNAM mesocosm system and located in outdoor tanks with running seawater from the lagoon. Organisms were kept under natural solar irradiance dampened to 47% of the surface irradiance (Es) using neutral screening, which corresponded to the light intensity at the sampling depth. One day after sampling, corals were cut into smaller pieces of roughly equal size (2 cm2). Experimental organisms were allowed to recover for two days before gluing them onto PVC plates. For full recovery, coral nubbins were fixed onto tables, which were placed in the lagoon at 4 m depth. The nubbins used in this experiment were maintained in the reef lagoon of Puerto Morelos for 1 year.Fragments of three colonies of 22, at the UNAM mesocosm facilities of Puerto Morelos (see below). Natural variation in solar irradiance during the experiment presented an average in diurnal light exposure of 14.1 ± 1.14 mol quanta m−2 d−1. This variability simulated the natural light conditions to which organisms were pre-acclimated and recovered “in situ” in the reef lagoon of Puerto Morelos after their manipulation. From day 6 until day 10 of the experiment, we measured a 60% reduction in light exposure due to very dense cloud cover 22, represented +2 °C above the local summer maximum.We followed a similar protocol applied in Vasquez-Elizondo and Enríquezver Fig. . The temarag) ~40% lower (Ωarag = 2.29–2.44) in the OA treatment  than at ambient control-pH .Seawater chemistry in the experimental tanks showed aragonite saturation state values  for 10 days in August–September 2013. The temperature treatments were set based on the local summer maximum of the reef lagoon of Puerto Morelos (30 °C)5 achieved in August (ambient control temperature—CT), and a thermal anomaly of +2 °C above this summer average (32 °C) considered the heat-stress treatment (HT). Water temperature was independently regulated using titanium immersion heaters  in the main reservoirs, which supplied seawater to the experimental tanks. For the OA-treatments, the current seawater pH in the lagoon (pH 8.1) was used as a control condition (ambient pH – COA) and the changes expected by 2100 under the carbon emission scenario B142 or SSP2-4.543, with pH units in the National Bureau of Standards (NBS) scale of pH = 7.9, were used to determine the OA-treatment (OA). The seawater carbonate system of the OA treatments was automatically adjusted by bubbling with CO2 using electronic valves  until reaching the desired pH. Well-mixed water was continuously pumped from the main reservoir to the corresponding experimental tanks. Water temperature and pH were continuously monitored using a pH-electrode  and a thermocouple-based custom-made probes , respectively, both connected to a computer-based system equipped with a wireless data acquisition card . Throughout the experiment, we used natural solar irradiance and neutral screening to maintain the pre-acclimatory conditions (47% Es). Variation in solar irradiance was monitored using a cosine-corrected sensor LI-192 quantum sensor  connected to a data logger  and registered in 5-min intervals at the pier of the UASA. Due to the time required to perform the physiological determinations of four species, each treatment was initiated with a delay of one day in a progressive manner. This allowed finishing all physiological determinations of one treatment in one day, while maintaining the same duration of each treatment for all organisms. The experiment was started with the control treatment (CT-COA), followed by the CT-OA treatment on day 1, the HT-COA on day 2 and ultimately, HT-OA on day 3.To test the effects of heat-stress and OA, we used a fully orthogonal, two-factor design. Corals were maintained in a water table with a seawater flow-through system. Experimental 30-L tanks were supplied with constant seawater from four 1000-L header tanks, which had access to direct seawater flow from the reef lagoon. The average flow rate in the experimental tanks was 0.33 L sNBS, salinity, temperature, and total alkalinity  were used to calculate the components of the carbonate system in seawater  using the CO2SYS software in Microsoft Excel71. Validation of AT values was performed using certified reference material from the Andrew Dickson’s lab .Throughout the experiment, seawater carbon chemistry in the reservoirs was monitored every second day at solar noon (~1 p.m.). Values of pHFv/Fm) of Symbiodininaceae in hospite for each coral nubbin was recorded at dusk (7:30 p.m.–8 p.m.) using a DIVING PAM fluorometer , following the same protocol used in Scheufen et al.5. Variation in Fv/Fm describes the magnitude of the diurnal impact of light stress on PSII. Reductions in Fv/Fm with respect to the previous day imply photodamage accumulation, while increases imply PSII recovery. Therefore, Fv/Fm was used to assess the accumulation of PSII photo-inactivation in the symbiotic algae, in hospite, due to photodamage.Daily variation in the maximum photochemical efficiency of PSII (n = 4 replicates per treatment). Corals were placed into transparent acrylic, water-jacketed laboratory-made chambers equipped with Clark-type O2 electrodes . Chambers were filled with filtered seawater from the corresponding treatments. Water motion within each chamber was provided by magnetic stir-bars. The temperature within the chambers was kept constant during the physiological determinations at the corresponding experimental treatment using a circulating bath with a controlled temperature system . Light was provided by halogen lamps placed above the chambers at a particular distance to achieve an illumination of 500 µmol quanta−2 m−2. This irradiance allowed determination of the maximum photosynthetic rate, Pmax. Irradiance levels were measured using the light sensor of a DIVING PAM  previously calibrated against a cosine-corrected sensor LI-193 quantum sensor . The O2 electrodes were calibrated using air-saturated (100%) and nitrogen-bubbled seawater (0%) at the corresponding temperature and pH. Oxygen tension within the chambers was maintained between 20 and 80%. The rates of photosynthesis and respiration were measured continuously. As coral respiration rates were determined immediately after the organisms were exposed to Pmax, we called this descriptor light-enhanced respiration (RL). This parameter is a better descriptor of coral respiration, as oxygen and/or substrate availability does not limit mitochondrial activity36. Data were captured with a computer equipped with an A/D converter. Evolution/consumption rates within the chamber were determined from the slopes after transforming the electronic values into O2 concentrations and correcting for the electrode drift . Gross photosynthesis (Pmax) was calculated by adding light-enhanced respiration to the net photosynthetic rates (NPmax). Coral P:R ratios were also determined using Pmax and RL values.Oxygen evolution determinations were conducted at day 0 and after 10 days of exposure to experimental conditions (3)73. Experimental organisms (n = 4 replicates per treatment) were incubated for 1 hour, in 0.45 µm filtered seawater at the corresponding experimental temperature and pH. We used halogen lamps for the incubations to provide a saturating irradiance level of 500 µmol photons m−2 s−1. The temperature was maintained constant in the incubation chambers using a circulating water bath with a control temperature system . Magnetic stir-bars allowed sufficient water motion during the incubations. Alkalinity measurements were performed immediately after the end of the incubation using a modified spectrophotometric procedure as described by Yao and Byrne74 and Colombo-Pallotta et al.36. Briefly, the microtitration of each sample (n = 3–5) with 0.3 N HCL was performed at a flow rate of 8 µl min−1 using a gas-tight glass syringe  fitted to a syringe pump  into a 1 cm length cuvette. After gently bubbling the sample with N2 for at least 5 min, bromcresol green  was added and changes in light absorbance at 444, 616, and 750 nm were monitored with an Ocean Optics USB4000 spectrophotometer  and using a xenon light source . At the end of the titration, the temperature was recorded for each sample using a digital thermometer with high resolution (±0.1 °C).Calcification rates were measured using the alkalinity anomaly principle based on the ratio of two equivalents of total alkalinity for each mole of precipitated calcium carbonate  using a miniature spectrometer controlled by the manufacturer’s software . Coral absorptance (A) was calculated as follows: A = 1 – Reflectance (R), according to Enríquez et al.49 and Scheufen et al.13, as light transmission through the coral skeleton can be neglected75. After completing the reflectance measurements, samples were frozen and stored at −70 °C until subsequent pigment and symbiont density analyses.For reflectance (R) determinations, corals were submerged in seawater in a black container and illuminated with diffuse light reflected from a semi-spherical integrating sphere placed above the organism following a previous description76. The remaining algal pellet was resuspended in FSW, and three subsamples were taken for analysis of photosynthetic pigment concentration. Symbiodininaceae pigment extraction was performed using acetone:dimethyl sulfoxide (20:1 v/v)77, and pigment concentration was determined spectroscopically using the equations of Jeffrey and Humphrey78. In addition, aliquots were fixed with Lugol iodine solution for symbiont cell counts, which were performed using an Improved Neubauer hematocytometer (n = 8 replicate counts).For the determination of coral pigment content and cell density, coral tissues were stripped from the skeleton by air-brushing in 0.45 µm filtered seawater (FSW). Tissue slurry was homogenized using an Ultra-Turrax® T10 Basic homogenizer  and the homogenate was centrifuged at 2000 rpm for 10 min. Tissue protein content was spectrophotometrically determined on the supernatant based on the differential absorbance between 235 and 280 nmFv/Fm between successive days. Assumptions of normality and homogeneity of variances were tested using a Shapiro-Wilks test and a Levene test, respectively. If required, data were subjected to transformations to obtain normal distributions and homogenous variances. All data were analyzed in SPSS Statistics 20.0 .Two-way analyses of variance (ANOVA) and Tukey HSD post hoc tests were used to determine significant effects of each experimental treatment  on coral physiology and pigmentation and to identify significant differences between treatments, respectively. Welch t-test were used to assess the difference in Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Previous studies have reported that maternal smoking during pregnancy and breastfeeding may affect the occurrence of hypertension, but whether early life factors modify the impact of the offspring’s genetic risk on hypertension is still unknown. The aim of this study was to investigate the relationships among maternal smoking and breastfeeding with adult-onset hypertension and the modified impact of offspring genetic susceptibility.This study included 437,185 participants from the UK Biobank who were initially free of hypertension and provided a prospective cohort of individuals aged 40 to 69 years. The association of maternal smoking during pregnancy and breastfeeding with hypertension was examined by using the Cox regression model. Then, a polygenic risk score (PRS) for hypertension was used to test the gene–environmental interaction on hypertension.During a median follow-up period of 8.7 years, a total of 68,148 cases of hypertension were identified in this study. The hazard ratios (HRs) and 95% confidence intervals (CIs) of hypertension for maternal smoking and breastfeeding were 1.11  and 0.96 , respectively. However, no evidence of an interaction between maternal smoking and breastfeeding was observed. Across all levels of genetic risk, including high genetic risk, maternal smoking and nonbreastfeeding had higher hypertension hazards than nonmaternal smoking and breastfeeding, respectively. The adjusted HRs (95% CIs) of hypertension were 1.80  in those who had high genetic predisposition plus maternal smoking and 1.67 (1.60–1.74) in those with nonbreastfeeding and high genetic risk. There were significant additive interactions between maternal smoking or breastfeeding and genetic factors on the incidence of hypertension.Maternal smoking and nonbreastfeeding were associated with a higher risk of hypertension in adulthood and may attenuate the risk of hypertension related to genetic factors. These results suggested that adherence to nonmaternal smoking and breastfeeding was associated with a lower risk of hypertension among participants with all gradients of genetic risk.The online version contains supplementary material available at 10.1186/s12916-022-02648-y. Adverse intrauterine exposure may result in permanent developmental inhibition of the structure and function of the cardiovascular system and increase susceptibility to various cardiovascular metabolic diseases later in life , 2. ReceIn addition, data on the early-life determinants of cardiovascular risk suggest that breastfeeding has a protective effect on cardiovascular health , 13. BreGenome-wide association studies (GWAS) and mixed mapping studies in populations of European ancestry have identified more than 200 genetic loci , 22. PolBased on a cohort of ~500,000 persons from the UK Biobank, we extracted information on breastfeeding and maternal smoking during pregnancy and examined their interaction on offspring adult hypertension using Cox proportional risk models. Considering the importance of genetic factors , stratifwww.ukbiobank.ac.uk). The UK Biobank study was approved by the research ethics committee of the UK Biobank, and all participants provided informed consent forms.The UK Biobank is a large, population-based, multicentre prospective cohort study that has collected a series of data on lifestyles, physical measures, biological samples, and health data , 28. Thehttps://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=100033).Information on “maternal smoking around birth (Field ID 1787)” and “breasted as a baby (Field ID 1677)” was collected based on the ACE touchscreen question. Participants were asked, “Did your mother smoke regularly around the time when you were born?” or “Were you breastfed when you were a baby?” The answer options were “yes”, “no” and “don’t know”. The relevant information can be found in the “Early life factors” (Field ID 100033) category of the UK Biobank’s website (Q statistic19 filtered at P ≥ 1 × 10−4). All SNPs included in the current study were available in the UK Biobank imputed dataset. The related information is presented in Additional file The detailed procedures for genotyping, imputation and quality control in the UK Biobank project have been described previously . Based oβ1 × SNP1 + β2×SNP2 + … +βk × SNPk + … + βn×SNPn, where n is the total number of SNPs and βk is the value of the natural logarithm of the odds ratio for hypertension associated with SNPk. The effect size estimates of βk were obtained from a previous study [A weighted method was applied to calculate the hypertension PRS. According to the number of risk alleles, each SNP was redefined as 0, 1, or 2. The PRS values were calculated using the formula: PRS = us study . The parus study , 31.Hypertension existing at baseline was defined using self-reported information and data from hospital episode statistics with the date of event preceding the date of attendance at the assessment centre. Hypertension was confirmed by hospital admissions with International Classification of Diseases, 9th and 10th Revision codes of 401. X, I10, 20002, 6150, and 6177 . Follow-Additional information was collected by self-reporting or historical diagnoses from national hospital registers. We included age; sex ; UK Biobank assessment centre; maternal smoking around birth (yes/no); breastfed as a baby (yes/no); Townsend Deprivation Index (TDI); body mass index ; physical activity ; smoking status (never/previous/current); alcohol consumption (never/previous/current); diabetes at baseline (yes/no); and cardiovascular disease at baseline (yes/no).2), overweight (25–29.9 kg/m2), or obese (≥ 30 kg/m2) according to the World Health Organization (WHO) criteria. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by trained nurses at baseline using standard procedures. All of the above procedures were conducted by skilled professionals, and the mean values of two automated or manual measurements were used.Based on items obtained from the short International Physical Activity Questionnaire (IPAQ), the number of metabolic equivalent task (MET) minutes was adopted to assess physical activity. Smoking and alcohol consumption status were grouped as never, previous or current. In addition, BMIs were generated from height and weight data, which were measured by trained nurses during the baseline assessments. BMIs were classified as normal . A detailed flow diagram for the included participants is shown in Fig. Among the 502,507 individuals with available data for the current study, participants were excluded for the following reasons: 1) prevalent hypertension cases (defined by the date of a hypertension event preceding the date of enrolment or self-reported history of hypertension at baseline); (2) missing relevant exposure data such as maternal smoking or breastfeeding; or (3) missing genotyping data. Finally, there were 437,185 participants with complete hypertension follow-up data after excluding prevalent hypertension. Then, we included 399,531 and 356,079 participants to investigate the association between maternal smoking and breastfeeding with adult hypertension, respectively, after excluding participants with missing related exposure data. After excluding those who are missing all of exposure data, 318,425 participants involved in the analysis of the association of interaction between maternal smoking and breastfeeding with hypertension. Moreover, we further investigated the interaction of maternal smoking or breastfeeding and genetic factors (PRS) on hypertension when limiting participants to individuals with white British ancestry with genetic information available   and 95% confidence intervals (CIs). The proportional hazards assumption was examined using Schoenfeld residuals. The data generally met the conditions of the proportional hazards assumption for performing subsequent regression analyses ; and model 3: model 1 further adjusted for TDI, alcohol consumption, smoking status, BMI, physical activity, and diabetes at baseline (yes/no). The main analysis was performed based on model 2, and we used model 3 instead when considering genetic factors. Due to possible confounding factors, we performed subgroup analyses by age , sex  taking the maternal smoking status as a subgroup to assess the association of breastfeeding with hypertension; (2) taking the maternal smoking status as a subgroup to assess the association of own smoking status with hypertension; (3) taking the maternal smoking status as a subgroup to assess the difference in sex status on hypertension; and (4) taking participants who were not exposed to maternal smoking but were breastfed as a reference to assess the HR of increased risk factors on hypertension.Sensitivity analyses were conducted to examine the robustness of the results based on model 3 by (1) limiting nonsmokers to avoid confounding effects caused by self-smoking; (2) limiting participants with follow-up times equal to or greater than 2 years to capture more hypertension events; and (3) limiting participants without prevalent CVD to avoid the possible confounding bias generated by CVD.Additionally, we tested gene–environmental interactions by setting variable cross-product terms of the environmental exposures  with the hypertension PRS in model 3. The additive interaction term was constructed from two indices: the relative excess risk due to the interaction (RERI) and the attributable proportion (AP) . The 95%P values < 0.05 were considered statistically significant.All missing covariates were included in the imputation equation. The missing category variables were imputed with multiple imputations based on latent class (MILC), and the missing continuous variables  were imputed with multivariate imputation by chained equation (MICE) by using predictive mean matching , 37. All2, and 2531.7 min/week, respectively. In brief, participants who suffered from hypertension were more likely to be obese and engaged in less physical activity. Additionally, participants with hypertension had a higher prevalence of CVD and diabetes. Hypertensive patients had higher maternal smoking rates and genetic risks but lower breastfeeding rates.The baseline characteristics of the final analytical cohort are presented in Table P < 0.001). Participants who were breastfed presented a lower risk of hypertension . However, after stratification by maternal smoking, the association between breastfeeding and hypertension was not significant  . It is well known that cigarette smoking in adulthood exerts a hypertensive effect. We found that participants exposed to both maternal and personal smoking were associated with a higher risk of hypertension  than participants without maternal and personal smoking histories .A total of 68,148 cases of hypertension were identified during a median follow-up of 8.7 years, and the associations between maternal smoking or breastfeeding and incident hypertension are shown in Table P = 2.44 × 10−12). However, no evidence of an interaction between maternal smoking and breastfeeding was observed (P for interaction = 0.256).Figure P < 2 × 10−16), and a per unit increase in PRS was significantly associated with a 7% increased risk of hypertension   as well as high genetic risk . The sensitivity analyses also showed that the relationships among maternal smoking, breastfeeding, and genetic factors and the incidence of hypertension were robust after excluding participants with cardiovascular disease at baseline  in subgroups of approximately 20,000 and 10,000 subjects who were assessed twice after the first and second follow-up; and (3) the proportion of maternal smoking in our study (29.1%) was close to the estimated prevalence of smoking during pregnancy in the UK (23.3%) [There are several limitations to this study. First, the UK Biobank participants were predominantly white, which limits the generalizability of the results to other ethnic groups. Second, the data for maternal smoking during pregnancy and breastfeeding were self-reported retrospectively, which can lead to recall bias. However, this might not be a significant problem because (1) previous studies have confirmed that reports by offspring of maternal smoking during pregnancy and breastfeeding are reasonably valid when compared to the mothers’ own reports , 52; (2) (23.3%) . Third,  (23.3%) , 55, we In summary, based on a large cohort study, we found that participants who were exposed to maternal smoking during pregnancy or who were not breastfeeding had higher risks of high blood pressure in adulthood. A higher genetic risk for hypertension is also associated with a higher risk of developing hypertension. Notably, individuals with low genetic risk still need to pay attention to the risk of hypertension if they were exposed to maternal smoking during pregnancy or nonbreastfeeding. Additional efforts should be made to clarify the role of maternal smoking and breastfeeding in the aetiology of adult-onset hypertension.Additional file 1: Table S1. The main information for genetic variants associated with hypertension in the UK biobank. Table S2. Descriptive characteristics of participants in the UK Biobank study by hypertension. Table S3. The association of breastfeeding and maternal smoking on hypertension. Table S4. Subgroup analysis for the association of hypertension and maternal smoking or breastfeeding by specific characteristics. Table S5. The association of maternal smoking and sex with hypertension by sex . Table S6. The association of maternal smoking and own smoking history with hypertension . Table S7. The association of maternal smoking and own smoking history with hypertension by sex . Table S8. Adjusted hazard ratios and 95% confidence intervals for hypertension polygenic risk scores with the risk of hypertension . Table S9. The association of maternal smoking and breastfeeding with hypertension in participants with different genetic risks after excluding participants with cardiovascular disease at baseline . Table S10. The association of maternal smoking and breastfeeding on hypertension in participants with different genetic risks after excluding participants with follow-up times less than 2 years in the UK Biobank . Table S11. The association of maternal smoking and breastfeeding with hypertension in participants with different genetic risks among participants who never smoked . Figure S1. The proportional hazards assumption using Schoenfeld residuals.


We present an unprecedented effort to map anthropogenic emissions of air pollutants at 1 km spatial resolution in the contiguous United States (CONUS). This new dataset, Neighborhood Emission Mapping Operation (NEMO), is produced at hourly intervals based on the United States Environmental Protection Agency (US EPA) National Emission Inventories 2017. Fine-scale spatial allocation was achieved through distributing the emission sources using 108 spatial surrogates, factors representing the portion of a source in each 1 km grid. Gaseous and particulate pollutants are speciated into model species for the Carbon Bond 6 chemical mechanism. All sources are grouped in 9 sectors and stored in NetCDF format for air quality models, and in shapefile format for GIS users and air quality managers. This dataset shows good consistency with the USEPA benchmark dataset, with a monthly difference in emissions less than 0.03% for any sector. NEMO provides the first 1 km mapping of air pollution over the CONUS, enabling new applications such as fine-scale air quality modeling, air pollution exposure assessment, and environmental justice studies.  The World Health Organization (WHO) estimates that exposure to ambient air pollution has been associated with 7 million premature deaths per annum, making it the single largest environmental risk today4. In the United States, over one third of the population lives in areas not attaining the health-based National Ambient Air Quality Standards (NAAQS) for ozone (O3) and/or fine particulate matter (PM2.5)5. Air quality and public health managers have an important task to protect public health by alerting the population when forecasts predict the exceedance of the NAAQS, which critically depends on the accurate prediction of the timing, location, and severity of unhealthy air quality episodes7.Emission, or the release of gases and particles from the Earth’s surface into the atmosphere, is the starting point of many Earth system processes responsible for some of the greatest environmental challenges today, such as air pollution, acid deposition, and climate change8. While PM2.5 can have both primary and secondary origins, O3 is mostly formed through photochemical reactions in the troposphere10. Consequently, the collocation of emitted pollutants and their precursors, that affects the chemical transformations, localized dispersion and deposition, is a major factor controlling the variability of concerned atmospheric constituents13. Better spatially resolved emission data allows further improvement of atmospheric composition prediction for air quality early warning and management16. Chemical transport models can provide the knowledge of vertical atmospheric constituents as priori for the retrievals of satellite products20. As technology advances, the instruments are able to observe at higher spatial resolution, requiring a priori information at a finer resolution as well. Similarly, fine resolution emission and concentration data can provide new insight into population exposure to air pollution23.Air quality models used for forecasting and policy studies rely on detailed mapping of emission sources to predict spatio-temporal variations of air pollution. Air pollutants can be directly emitted (primary) or formed in the atmosphere through chemical/physical processes (secondary)2 emission dataset with 1 km resolution was developed by using the satellite observed nighttime lights24, and a global ammonia emission inventory with 0.1° resolutions was created with updated emission factors and data products25. Within the US, the DAtabase of Road Transportation Emissions (DARTE) provides annual emissions of on-road CO2 over the contiguous United State (CONUS) at 1 km resolution based on the roadway-level and emission factors26. The Vulcan v3.0 CO2 emissions data was generated at 1 km and hourly resolution which includes various anthropogenic source sectors27. Additionally, a sub-neighborhood (~100 m) surface NO2 dataset over the CONUS was presented using land-use regression (LUR) models along with observational and modeling data29. These datasets usually focus on a single emission species, and are often inadequate for some applications, such as air quality modeling. A high-resolution emission dataset that contains all co-emitted major air pollutants is desirable to support various applications in air quality modeling, public health, and environmental management.Numerous approaches have been utilized to map greenhouse gases and air pollutants at high spatial resolution. A global CO30 and the Weather Research and Forecast with chemistry (WRF-chem) model. The data are available in the NetCDF format, and annual data are also provided in the shapefile format for VOCs, NOx, CO, SO2, NH3, and PM2.5. In addition, a web-based data portal has been set up to provide online emission data services for interested users. This, to our knowledge, is the first effort to map all major air pollutants at 1 km resolution for the entire CONUS. The dataset, along with the data access, is expected to enable new applications such as fine-scale air quality modeling, air pollution exposure assessment, and environmental justice studies.In this study, we present a new high‐resolution anthropogenic emission dataset, called the Neighborhood Emission Mapping Operation (NEMO), that maps all major sources in the CONUS. It includes the emissions from nine sectors, accounting for 854 individual source types, based on the 2017 National Emissions Inventory (NEI). The emission data are mapped at 1 km spatial gridding and at hourly intervals. These air pollutants are further split into chemical species consistent with the Carbon Bond 6 (CB6) chemical mechanism, so that the data can be used to drive air quality models such as the Community Multiscale Air Quality (CMAQ) model31, and the configuration files  used for the allocation and speciation in SMOKE can be generated with various tools or provided by the ancillary datasets from US EPA. The information of the emission inventory and other data and tools is described as follows.The anthropogenic emissions in this dataset are generated based on the 2017 National Emissions Inventory (NEI2017) from the US Environmental Protection Agency (US EPA). Since the NEI only provides aggregate emissions for each county, four steps were taken to generate the high-resolution emission dataset, including 1) spatial allocation; 2) chemical speciation; 3) temporal allocation, and 4) merging. These four steps are implemented by the Sparse Matrix Operator Kernel Emissions (SMOKE) model2.5 and PM10), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), and volatile organic compounds (VOCs). Point sources are represented as the individual facilities , usually at specific latitude/longitude coordinates, rather than as county or tribal aggregates. In NEI2017, all point sectors are treated as elevated sources, so in this dataset we only consider the airports sector, which has surface level of emissions and can be processed into two-dimension gridded files. The Motor Vehicle Emissions Simulator (MOVES) version 2014b generates county-level emission factors from on-road mobile sources, which include monthly county-level emissions from motorized vehicles that are normally operated on public roadways. In addition, emissions from nonroad sources, such as nonroad engines and equipment, construction equipment, and agricultural engines, are also calculated by the nonroad component of the EPA’s MOVES model (MOVES-Nonroad). For the estimated emission records, quality assurance (QA) has been implemented and reviewed by EPA and state, local, and tribal agencies. Detailed information about the emission inventory is provided in the NEI2017 Technical Support Document (TSD)32 and all the inventory files, as well as the emission processing platform, can be downloaded from EPA FTP33.The NEI2017 (version 2017gb) compiled by US EPA is used to develop the high-resolution emission dataset. There are hundreds of individual emission sources in NEIs, which are grouped into nine emission sectors, including six nonpoint sectors, two mobile sectors, and the point sector  in the emission inventory cannot be directly used by chemical transport models, unless distributed into model species of a specific chemical mechanism. The model species can be individual chemical compounds (explicit species) or groups of species (lumped species). In the NEI2017, we use the Carbon Bond 6 (CB6) chemical mechanism34 to split gaseous pollutants (NOx and VOCs), and the Aerosol 7 (AERO7) aerosol mechanism35 to split particulate pollutants (PM2.5 and PM10) into required model species.Some of the pollutants  inventory. The other VOC speciation approach, called Integration or “INTEGRATE”, is used for onroad, offroad and some area source sectors. This approach aims to integrate two NEIs, NEI2017 and HAP NEI, for select VOC HAPs. For these HAPs, the HAP NEI is generally considered a better data source than speciated VOC in NEI2017. Five VOC HAPs, including naphthalene (NAPH), benzene (BENZ), acetaldehyde (ALD2), formaldehyde (FORM) and methanol (MEOH) collectively called NBAFM, are explicitly represented in the CB6 chemical mechanism. The “INTEGRATE” profiles are used to subtract NBAFM from the total VOC during the speciation processes to avoid double counting emissions. For instance, in the airports sector, the NEI2017 provides the total VOC emission named as “VOC” and no integration is needed for the chemical speciation. All the model species are speciated from the “VOC” in the NEI2017. In contrast, the onroad and offroad emission inventory provides specific emissions for HAP species  and the VOC emissions that exclude those species. Therefore, these HAP will be removed from the criteria VOC mass, and the profiles are generated by removing the specified HAP species from the “CRITERIA” profiles, and then renormalizing. Detailed information of the use of HAP along with NEI VOC, called “HAP-CAP integration”, and the integration status for each emission sector can be found in the Table 37 on the basis of SPECIATE database38 which is developed and maintained by the Office of Research and Development (ORD) of US EPA. The only exception is that the speciation profiles of the mobile sources  are generated by the Motor Vehicle Emissions Simulator (MOVES)39. Similar to the VOC, the speciation information of PM is also supported by the SPECIATE and can be generated using the Speciation Tools and MOVES. For NOx, the speciation is based on a NO2 weight factors, speciating total NOx into NO, NO2, and/or HONO. The speciate profiles for different emission sources and locations are differentiated by the SCC and county/state, managed through a cross-reference file that links SCC for each county/state to a specific speciation profile. In NEI2017, the speciation profiles for the CB6 mechanism are already prepared by EPA, which are created based on the SPECIATE5.0 database33.The speciation profiles for most emission sectors can be created by the Speciation Tools33. The temporal profiles for each source and county/state are assigned using a cross-reference file that links Federal Information Processing System (FIPS) code/SCC/pollutant to different monthly/weekly/diurnal temporal profiles.NEI provides annual totals but models require the information of finer temporal variations . Distributing aggregated emissions to a finer (hourly) temporal resolution to meet the model requirement is realized by the temporal allocation process. For the source sectors with annual emission records Table , three t2 square in this case). As the area of a given county may fall into several grid cells, spatial surrogates need to be used to indicate the fraction of the county’s emissions assigned to each grid cell. These surrogates are created based on geographic information systems (GIS) shapefiles which include the geographic information, such as population/housing, roadways, and land cover . A spatial surrogate ratio is a value greater than zero and less than or equal to unity that specifies the fraction of the emissions in an area  that should be allocated to a particular model grid cell (a 1 km40. The procedures can be summarized in five steps: (1) Install the Spatial Allocator41 along with PostgreSQL software, and collect shapefile data from the EPA42 or commercial vendors; (2) Activate PostgreSQL server, create a database and load the shapefile data into database; (3) Generate a table representing the modeling grid in the database; (4) Generate surrogate files using SA tools; (5) Gap-filling, normalization, and quality assurance. For the contiguous United States (CONUS), a total of 108 spatial surrogates were prepared, including 12 U.S. census-based surrogates, 24 transportation surrogates , 17 landcover surrogates, 20 surrogates for building footprints, 23 surrogates that describe oil and gas well production, 6 surrogates for shipping and ports, and 6 for other industrial and commercial activities like refineries and tank farms, airports, golf courses, mines, and timber. The surrogate information and relevant shapefile data used for our dataset are provided in Supplementary Table In this study, the spatial surrogates for the 1 km × 1 km grids were generated using a surrogate generating tool Spatial Allocator (SA) coupled with the PostgreSQL database management system. The SA, developed by the University of North Carolina Community Modeling and Analysis System (CMAS), is a suite of tools to create input files for weather and air quality models. More specifically, the surrogate tools of SA were used to create a large set of spatial surrogates, and to merge and gap-fill these surrogates when necessary. The source code and scripts, as well as detail documentation of the SA tools can be downloaded from the CMAS centerx, VOCs, PM2.5 and PM10 into required chemical species for each source/location. Next, all emission records are distributed to 1-hour intervals from the 2017 annual or monthly total emissions using SCC‐specific temporal profiles. Third, the spatial surrogate ratios are used to distribute county-level emissions into 1 km × 1 km grids. Finally, all gridding, speciation, and temporal matrices are combined to create model-ready emission data at 1 km horizontal resolution and hourly intervals in the netCDF format.With the base emission inventories, chemical speciation, temporal profiles and spatial surrogate ratios, we generate the 1 km emission dataset using the SMOKE model version 4.7 for all nine anthropogenic emission sectors. This process takes four steps. First, the chemical profiles are used to speciate NOFor each of the emission sectors, the above processes are repeated, so that the combined datasets are generated for each sector. The gridded emission will be stored by sectors and can be merged using a SMOKE tool (mrggrid) as needed, depending on the needs of the model simulation. The flow chart in Fig. 2 resolution. The NetCDF format contains hourly, monthly, and annual data while the Shapefiles only include annual emissions. Additionally, the NetCDF provides the model species for CB6 mechanism in the hourly and monthly data files while the shapefiles include integrated species like VOCs, NOx, PM2.5, and three inorganic gases, SO2, CO, and NH3. Figures 2.5, along with their frequency diagrams, as well as the diurnal variations and the proportions of each speciated model species from VOC and PM2.5. The datafiles of monthly and annual emissions that are available on figshare43, while the hourly emission data are stored on our data server at George Mason University44 because of the large file sizes.Table Here we compared the NEMO dataset against the 12 km × 12 km emission, generated using the spatial surrogates provided by US EPA in NEI2017, a benchmark dataset widely used in research and regulatory modeling. Figure x emissions over five metropolitan areas to that of the benchmark dataset. NOx is a key precursor to tropospheric ozone and particulate nitrate. Figure x from 12 km and 1 km dataset. We overlay the emission map with geographical information including roads, airports, ferries, and main cities as a measure to validate the accuracy of the spatial allocation. The results show that the NEMO dataset can capture high emissions in urban areas that follow the benchmark pattern. The 1 km distribution can also reflect the fine features of emissions over highways and other major roads. At airports, ultra-high NOx emissions are shown at corresponding locations. In addition, the 1 km distributions create much clearer coast-pattern emissions over cities like New York City and Los Angeles compared to the benchmark. These results show that the spatial distribution of the 1 km emission dataset is more consistent with the geographical features in the real world. The increase of resolution (144 times finer than the benchmark) in comparison to the 12 km product provides the desirable information to map air pollutant emissions at neighborhood level.Next, we compare the NO45 to prepare anthropogenic emissions within the CONUS domain according to the user’s requirements.The NEMO data are available in the NetCDF format at hourly, monthly and annual intervals. The shapefile format of NEMO is only available for the annual aggregated emissions, although finer temporal resolution can be generated from the NetCDF files. Each hourly emission file includes 5397 columns, 3177 rows, 35 gas species, 20 aerosol species, and 25 time steps which needs a longer time for processing. We recommend using double precision for data analysis and processing. For convenience, we also provide a web-based data portalSUPPLEMENTARY INFORMATIONSupplementary Information Table 1


Generally, the standard surgical route of Morrow begins with the incision of median sternal, which leads to more trauma, pains and discomforts to patients with hypertrophic obstructive cardiomyopathy (HOCM). It is more difficult and rough to perform the competed resection of hypertrophic myocardium due to complicated anatomical morphology of left ventricular outflow tract and limited visual field of left ventricle during surgery. As the novel surgical strategy, firstly, under the guiding of 3D printing technology, the platform of effective preoperative evaluation focusing on how to resect the hypertrophic myocardium is established. Then, combined with assisted Da Vinci robotic surgery system, the outcome of patient with HOCM is positive and promised. It has been demonstrated that HOCM is a kind of the autosomal dominant inherited diseases, characterized mainly as left ventricular hypertrophy, especially for ventricular septum . After tMale patient, 50-year-old, admitted with the clinical symptoms of persistent syncope and dyspnea and was diagnosed as HOCM. Through regular medical intervention by cardiotonic and diuretic agents and after comprehensive evaluation, the patient was indicated for septal myectomy with the aim of releasing the severe obstruction of LVOT. Preoperatively, both heart function and structural abnormalities were assessed using transthoracic echocardiography (TEE), and based on the findings under which, the asymmetric hypertrophy of interventricular septum and severe mitral regurgitation (MR) complicated with systolic anterior motion of mitral leaflet (SAM) were confirmed. The measured peak pressure difference, maximum flow rate and septal thickness of LVOT were 137 mmHg, 5.84 m/s and 22.9 mm, respectively  was established via artery−venous cannulation from femoral artery, vein as well as right internal jugular. After activation of CPB, the mechanical ventilation was ceased to expose the surgical field completely. Carbon dioxide was blew continuously to fulfill the right thoracic cavity via working port. The ascending aorta was clamped by Chitwood and regular perfusion with cardioplegia of Del Nido was processed when temperature < 35 ℃. The left atrium was opened through the incision of groove and suspended by retractor. It was observed that chordae tendineae locating at P3 of posterior leaflet was ruptured and prolapsed. The margin of prolapsed leaflet was sutured with 8-character-pattern suturing. Scattered distribution of white debris was found around the hypertrophic interventricular septum through the incision of anterior mitral leaflet. The depth, thickness and size of hypertrophic myocardium had been preset in accordance with referred information obtained from the individual 3D model. During the surgery, the preset hypertrophic lesion was resected completely and white debris were washed repeatedly Fig. A, B. ImpPostoperatively, based on the findings of echocardiography, both regurgitation and SAM were not found. The measured peak pressure difference, maximum flow rate and septal thickness of LVOT were 15.8 mmHg, 1.99 m/s and 16.7 mm, respectively  obstruction once the conservative treatment is ineffective or the obstruction-related symptoms recur frequently . Median Recently, 3D printing technology, as an emerging industry, has been the favored and preferred medical mode free from the traditional medical system. Currently, compared with cardiovascular surgery, 3D printing technology has been used widely in thoracoscopic surgery –9. PreviThe combination strategy of assisted Da Vinci robotic surgery and 3D printing technology is a relative effective and safe surgical option for patients with HOCM with the various advancements including accurate pre-evaluation, procedural stability, complete resection of lesion and minimal invasion.