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One hundred percent pure FJ is a beverage that contributes no added sugar but nevertheless contains free sugars. According to the WHO, the intake of free sugars should be limited to less than 10% of total daily energy for weight control purposes. Sugar-containing beverages are often specifically blamed for inducing passive energy over-consumption, facilitating weight gain, and displacing healthy foods and beverages from the diet . Unlike many other popular sugar-containing beverages, however, FJ also contributes to diet quality and is associated with a number of positive health factors . The present analysis of a recent nationally representative survey in French adults casts light on a number of nutritional and health correlates that can assist in the evaluation of the role of FJ in the contemporary diet.
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About half of French adults consume 100% pure FJ. The daily consumption is modest: 116 ± 4 mL per day, representing about 46 kcal. Of note, the daily consumption of sugar-containing sodas in French adults is even more modest: 77 mL/day. More women than men consume FJ and larger proportions of consumers are found in younger than older adults. Proportions of consumers increase with education level and income, although the food budget does not appear a significant factor. Many other aspects of lifestyle are unrelated to FJ consumption: the number of persons in the household (particularly the presence of children), daily time spent watching screens or doing physical activity, smoking status, dieting, and body weight status.
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The levels of FJ consumption reported in the CCAF 2016 sample agree with other reports. The recently published 3rd INCA study , a major nationally representative survey of the French population, using a different methodology (three 24 h recalls rather than a seven-day food diary, among other differences), reports 50.3% consumers who ingest 127 mL/day of fruit + vegetable juices. A previous CCAF survey conducted in 2010 reported 52% consumers with a mean daily FJ intake of 115 mL. FJ consumption thus appears stable in French adults, both in terms of prevalence and amounts consumed.
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In our sample, consumers of FJ consumed more of a large number of other foods and had a higher daily energy and nutrient intake than had non-consumers. The absence of association of FJ consumption with the BMI suggests that FJ consumers were more active individuals. The measures of physical activity and sedentary time (screen watching) did not reveal any significant difference between FJ consumers and non-consumers, however. It is possible that the assessment questions were too crude to detect actual differences. One obvious factor contributing to the significant differences in daily intake is age: FJ consumers were significantly younger than non-consumers. It is known that energy and sugar intake decreases with age in adults . Some residual under-reporting cannot be ruled out.
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The absence of a positive association between FJ consumption and BMI is consistent with other observations . For example, Wang et al. reported that, among the US adults participating in a cross-sectional NHANES survey, consumers of pure orange juice had a lower BMI, waist circumference, and body fat percentage than non-consumers. The level of juice intake in the American population (over 200 mL/day) was much higher than in the French sample, as was the level of soda intake (over 300 mL/day). As in the present survey, American consumers of juice reported lower alcohol consumption. The comparison between the NHANES and the CCAF data suggests that FJ consumption either is unrelated to BMI (in populations where FJ intake is modest) or shows an inverse association (in populations where FJ intake is higher). It may be that consumption of a food or beverage has to reach a certain level before its correlates can be detected, or that the NHANES survey was simply better able to detect a difference.
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FJ is one element in a complex, varied diet. Per se, FJ consumption contributed 2% of daily calories in FJ consumers and a higher proportion of the daily input of many vitamins and minerals. It also contributed free sugar. FJ consumers in our study reported higher intakes of many nutrients, both in absolute values and in nutrient density: CHO, simple and free sugars, many vitamins (notably C and E), and minerals. By contrast, their diet was significantly less dense in starch, protein, cholesterol, B12, zinc, sodium, and phosphorous. It could be that FJ consumption is one element in the diet of individuals who are attracted to sweet tasting options (whole fruits and pastries being other examples), while non-consumers could be mainly interested in savory items, accounting for their higher intakes of protein, cholesterol, vitamin B12, and sodium.
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Consistent with this hypothesis, daily energy from free sugar exceeded the 10% limit recommended by the WHO in FJ consumers (average 11.3%), while non-consumers had an average daily intake of free sugar amounting to 8.7% of total daily energy. FJ consumption contributed about 10 g of free sugar daily (19% of total daily free sugar intake). It is slightly more than the contribution from sodas (7 g/day). In French FJ consumers and non-consumers, however, the main sources of free sugar are not beverages but rather solid sweet foods such as candy, chocolate, cookies, and pastries . In the context of the WHO recommendation, the issue of different sources of added sugars, with different contributions to nutritional status, calls for a discriminative analysis of positive and negative impacts .
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In France, as well as in other areas of the world, the daily intake of fruit and vegetable is lower than recommendations (the classic “five-a-day” or about 400 g/day). An expert panel in North America recently suggested that FJ consumption could be one useful tool to satisfy America’s fruit gap . This is consistent with the nutritional guidelines for the French population that allow one portion of 100% FJ to count as one of the five recommended servings. In our study, FJ consumers did actually consume more whole fruits and vegetables than non-consumers, although their total intake (about 206 g/day) remained below the recommended amount.
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The strengths of the present study include the very demanding seven-day food record method used to obtain intake data and the large nationally representative sample. A potential limitation of the present design is the fact that participants were recruited as members of households, which may have decreased the variability of dietary responses. The exclusion of many overweight/obese under-reporters also possibly limits the sensitivity of the observations at the higher end of the body weight spectrum. The data are cross-sectional observations and thus do not allow any demonstration of causal effects.
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The analysis of physical activity and sedentary time is limited in the present study. Clearly screen watching time is a crude proxy for sedentary behaviors. Of note, the arbitrary 2 h/day cut-off for defining lower versus higher physical activity appeared to be close to the median and partitioned the whole sample in two sub-samples of approximately equal numbers. The self-declaration of body weight is particularly prone to under-declaration. However, in the present study, weights were reported during face-to-face interviews with highly trained staff, and possibly in the presence of other members of the household. Although this does not guarantee a highly precise report of body weight, it makes it likely that any gross misreporting would have been detected at this early stage. Of note, the overall proportions of overweight (31.9%) and obese (17.5%) individuals, including under-reporters, was very close to the proportions observed in the 2017 INCA 3 study (34% and 17%, respectively), in which body weight and height were measured rather than declared in a comparable representative population over the same time period .
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Future studies should address longitudinal changes in participants’ food choices as they age. The very significant age effect raises the question of whether there is a “generation effect” that predicts that the younger respondents in the 2016 study will maintain their intake of FJ as they age, or whether they will progressively adopt the lower prevalence of consumption seen in older respondents of the present study. If longitudinal surveys reveal that FJ consumption is progressively replaced by intake of other foods as the individual ages, the ensuing changes in diet composition or quality should be assessed. Associations between FJ consumption and BMI and other health indicators should also be investigated in longitudinal cohort studies.
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This nationally representative survey shows a lack of association between FJ consumption and BMI, no sign that FJ displaces other healthy foods, and that FJ contributes to the intake of valuable nutrients as well as free sugar in the diet. In the context of the WHO recommendation to limit the consumption of free sugar, the issue of different sources of free sugars, particularly beverages, with different contributions to nutritional status, requires discriminative analysis of positive and negative impacts, preferably in longitudinal follow-up studies.
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The Nanhai No. 1 shipwreck is located in the city of Yangjiang in Guangdong Province which lies on the Chinese southern coast. The ship sank off the coast during the Southern Song Dynasty (1127–1279 AD) and was discovered in the summer of 1987. In 2007, several authorities cooperated to salvage the whole shipwreck from the seabed that was over 20 metres underwater (Supplementary Fig. 1) and moved it to the Marine Silk Road Museum, where it was stored in a tank with seawater. The successful recovery is an unprecedented achievement and a historical landmark for Chinese underwater archaeology. A full-scale excavation of the Nanhai No. 1 shipwreck began on land in November 2013. A number of archaeological materials including ornaments, wood combs, bronze mirrors and even plant seeds have been recovered from the shipwreck. Further excavation revealed that the coastal trader carried a cargo of gold, silver, and copper coins, in addition to a significant number of porcelain items for ocean-going trade1. The remains of the ancient vessel are expected to yield critical information on ancient Chinese shipbuilding and navigation technologies. As the most important underwater discovery in Chinese archaeological history, the 800-year-old shipwreck is of great significance to the history of overseas trade and is also an important resource for Maritime Silk Road research.
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Over the past 100 years, several shipwrecks have been excavated, raised and conserved. For instance, the Viking-period Oseberg ship was found embedded in waterlogged clay at a land site excavation in Norway in 19022. Additionally, the warship, Vasa, was raised in 1961 after 333 years in the brackish and cold waters of the Baltic Sea3. King Henry VIII’s warship Mary Rose was discovered in 1971 and raised in 19824. Now in the final stages of conservation, it takes its place in a stunning and unique museum. However, another example is the Late Bronze Age Uluburun ship that sank off the Turkish coast at approximately 1335–1320 BC5. Under waterlogged and anaerobic conditions, archaeological wood is relatively well protected from biological decomposition compared to the degradation that occurs in most terrestrial environments. Some organisms, however, including bacteria and soft rot fungi, can still degrade waterlogged wood that is of significant archaeological value.
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There are two main bacterial groups that degrade waterlogged wood: erosion bacteria and tunnelling bacteria. Experimental laboratory research and investigations of degraded archaeological wood from various terrestrial and aquatic sites worldwide have confirmed the presence of these two groups6–8. Erosion bacteria can degrade wood under very low oxygen concentrations, while tunnelling bacteria are widespread in nature, occurring in both terrestrial and aquatic environments and can tolerate a wide range of temperatures and humidity9. The more commonly reported bacteria associated with wood-decay environments are cellulolytic aerobes such as Cytophaga, Cellvibrio, anaerobic genera such as Clostridium, and cosmopolitan taxa such as Bacillus, Pseudomonas, Arthrobacter, Flavobacterium, and Spirillum10,11. Fungi that cause soft-rot usually belong to the Ascomycota and are able to degrade wood in terrestrial and aquatic environments, having low oxygen requirements. Fungal taxa associated with soft-rot wood decay in terrestrial environments, include among others, the genera Cladosporium, Acremonium, Fusarium, and Chaetomium12. In the marine environment, wood is degraded by marine fungal taxa that have been recorded since 1944 by Barghoorn and Linder and shown to have lignocellulolytic capacity under laboratory conditions as revealed by Gareth Jones since 197113,14.
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As the excavation progressed on land since November 2013, the upper deck of Nanhai No. 1 has been exposed to air, while the integral hull remained immersed in seawater. Non-woven fabrics were used to cover the areas directly exposed to air, and a spraying system was also used to maintain moisture. Meanwhile, borate buffer solution (BBS) was applied every week to prevent potential microbial contamination. However, in October 2014, fungal mycelia, in addition to salt precipitates, started to develop on some areas where the fabrics did not cover the wood appropriately. By using high-throughput sequencing techniques and culture-based methods, the aim of this study was to identify potential wood-degrading microorganisms that are responsible for the degradation of the Nanhai No. 1 shipwreck during storage. For this purpose, microbial community analysis was undertaken specifically on wood exposed to air. Furthermore, we investigated the enzymatic characteristics of the dominant fungi that were implicated in wood degradation. Finally, antimicrobial efficacy in inhibiting the growth of the dominant fungi was assessed with five kinds of biocides.
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The detailed sampling spots and sample characteristics are shown in Fig. 1a–b. Samples were observed under a transmission light microscope and exhibited typical morphological characteristics of filamentous fungi (Fig. 1c).Figure 1Specific locations of the sampling sites on the Nanhai No. 1 ship’s hull. (a) The platform of the shipwreck. T0101–T0602 are different excavation areas. We gratefully acknowledge Jian Sun from the National Center of Underwater Cultural Heritage for providing the image. (b) Images of six samples that were taken from the ship’s hull. Samples were taken by using sterile scalpels and placing them into 2 mL micro-centrifuge tube for subsequent microscopic observation, DNA extraction, and cultivation. (c) Transmission light microscope images showing fungal hyphae that had penetrated the wood structure. Red circles indicate fungal hyphae.
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Specific locations of the sampling sites on the Nanhai No. 1 ship’s hull. (a) The platform of the shipwreck. T0101–T0602 are different excavation areas. We gratefully acknowledge Jian Sun from the National Center of Underwater Cultural Heritage for providing the image. (b) Images of six samples that were taken from the ship’s hull. Samples were taken by using sterile scalpels and placing them into 2 mL micro-centrifuge tube for subsequent microscopic observation, DNA extraction, and cultivation. (c) Transmission light microscope images showing fungal hyphae that had penetrated the wood structure. Red circles indicate fungal hyphae.
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Bacterial community composition (via 16S rRNA V4 region sequencing) was assessed by amplicon sequencing using the Illumina HiSeq2500 PE250 platform for the six samples. A total of 284,855 high-quality reads were obtained after filtering low-quality reads, chimaeras and trimming adapters, barcodes and primers. All 16S rRNA gene sequences were assigned to 43 bacterial phyla. The ten most abundant taxa at the phylum level are summarized in Fig. 2. Firmicutes were the most abundant phylum and represented between 6.30 and 82.92% of each sample’s reads with an average relative abundance of 42.65%. Proteobacteria were the second most abundant phylum with an average relative abundance of 38.00%. Bacteroidetes comprised 38.09% of all reads in sample NHI.12 but only 3.60 to 7.30% in the other five samples. Other major phyla consisted of the Actinobacteria (1.54–11.58%, average of 4.22%), Chloroflexi (0.13–2.66%, average of 1.04%) and Acidobacteria (0.02–2.13%, average of 0.90%) (Fig. 2a, Supplementary Table 1).Figure 2Relative abundance of the ten most abundant microbial phyla and Venn diagrams showing shared OTU diversity among the six samples. (a) Relative abundance for each sample is shown out of 100%. Bacterial phyla are coloured according to the legend on the right. (b) Relative abundance for each sample is shown out of 100%. Fungal phyla are coloured according to the legend on the right. (c) Bacterial OTUs and (d) fungal OTUs that were shared among samples.
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Relative abundance of the ten most abundant microbial phyla and Venn diagrams showing shared OTU diversity among the six samples. (a) Relative abundance for each sample is shown out of 100%. Bacterial phyla are coloured according to the legend on the right. (b) Relative abundance for each sample is shown out of 100%. Fungal phyla are coloured according to the legend on the right. (c) Bacterial OTUs and (d) fungal OTUs that were shared among samples.
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There was a total of 323,029 fungal community reads after filtering low-quality reads, chimaeras and trimming adapters, barcodes and primers. Four fungal phyla were present in the six samples (Fig. 2b). Ascomycota was the most dominant phylum, accounting for 99.45 to 99.98% of each sample’s reads, with an average relative abundance of 99.71%. The remaining phyla including the Basidiomycota, Chytridiomycota, and Zygomycota (Mucoromycota, Zoopagomycota) accounted for 0.29% of the remaining fungal reads. The genus-level relative abundance of the fungal communities is summarized in Table 1. Dominant fungal genera were similar among all samples. Among the ten most abundant fungal taxa, Fusarium and Aspergillus were present in all samples, and Fusarium was the most abundant genus across all samples, accounting for between 84.89% and 98.41% of the total community, with an average abundance of 93.53%. At the species level, F. oxysporum and F. solani were the dominant Fusarium species, and F. solani was the more abundant species of the two (Table 2).Table 1Relative abundance of dominant fungi among samples at the genus level.Dominant GenusNHI.1 (%)NHI.4 (%)NHI.8 (%)NHI.9 (%)NHI.11 (%)NHI.12 (%) Fusarium 96.8997.0792.3598.4084.9191.58 Candida 0.660.380000 Aspergillus 0.100.310.120.480.150.21 Wallemia 00.510000 Olpidiaster 0.31%00000 Meyerozyma 00.260.020.020<0.01 Acremonium 0.15<0.010<0.0100 Conocybe 0.1300000 Monascus 000.120.040.110.08 Monographella <0.01000.0600.10Table 2Relative abundance of dominant Fusarium species among samples.Dominant SpeciesNHI.1 (%)NHI.4 (%)NHI.8 (%)NHI.9 (%)NHI.11 (%)NHI.12 (%) Fusarium solani 92.8186.5792.3298.4084.8991.57 Fusarium oxysporum 4.0810.50<0.01<0.01<0.01<0.01
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Sample overlap was assessed to identify shared diversity among the six samples (Fig. 2c–d). A total of 71 bacterial operational taxonomic units (OTUs) were shared among samples (a total richness of 6,072 OTUs). Additionally, the six samples shared four fungal OTUs out of a total fungal richness of 280 OTUs. These results indicated that microbial communities overlapped to some extent among different sampling sites. This was particularly evident for fungal communities, in which Fusarium was the major genus and comprised a major proportion of all communities.
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The high-throughput sequencing results revealed a dominance of Fusarium among the six samples, and we therefore attempted to isolate the dominant fungi to study their enzymatic characteristics. Incubation on potato dextrose agar (PDA) plates at 28 °C for 6 days resulted in the presence of two colonies (Fig. 3a–b). Two strains, ‘NK-NH1’ and ‘NK-NH2’, were isolated from the samples. NK-NH1 grew rapidly with abundant aerial mycelia on PDA, and colonies sometimes exhibited a purple colour on the upper surface with some cream coloration in the centre of the colony. Microconidia were present, varying from sparse to abundant and were generally single-celled and oval to kidney-shaped. Macroconidia were abundant, stout, and generally cylindrical, with the dorsal and ventral surfaces parallel for most of their length. Chlamydospores were also observed for NK-NH1. NK-NH2’s growth was rapid with white aerial mycelia. Microconidia were abundant and generally single-celled, oval to kidney-shaped and were produced only in false heads. Macroconidia were abundant and only slightly sickle-shaped.Figure 3Colony and micro-morphology features of two fungal isolates at 400x magnification. (a) NK-NH1. Scale bar is 10 μm. (b) NK-NH2. Scale bar is 10 μm. (c) Neighbour-joining phylogenetic tree of Fusarium sp. NK-NH1 and Fusarium sp. NK-NH2 ITS gene sequences (~520–540 bp sequence used for each). Representatives of the most closely related strains and additional members of the genus Fusarium are included for taxonomic context. Bootstrap values at nodes are given as a percentage of 1000 bootstrap replicates. Scale bar indicates the expected number of substitutions/site.
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Colony and micro-morphology features of two fungal isolates at 400x magnification. (a) NK-NH1. Scale bar is 10 μm. (b) NK-NH2. Scale bar is 10 μm. (c) Neighbour-joining phylogenetic tree of Fusarium sp. NK-NH1 and Fusarium sp. NK-NH2 ITS gene sequences (~520–540 bp sequence used for each). Representatives of the most closely related strains and additional members of the genus Fusarium are included for taxonomic context. Bootstrap values at nodes are given as a percentage of 1000 bootstrap replicates. Scale bar indicates the expected number of substitutions/site.
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We then sequenced the 28S rRNA genes and the ITS1–5.8S rRNA-ITS2 gene regions for both colonies. NK-NH1 displayed 99% and 100% sequence similarity to the 28S rRNA gene and ITS sequences of F. solani, respectively. NHI.2 displayed 100% and 99% sequence similarity with 28S rRNA gene and ITS sequences of F. oxysporum, respectively (Supplementary Table 2). Phylogenetic analysis based on the ITS region sequences of the two isolates (~520–542 bp for both isolates) is shown in Fig. 3c. The results from these analyses confirmed that strain NK-NH1 (KY410238) belongs to the Fusarium genus and belongs to the same clade as F. solani, whereas strain NK-NH2 (KY410239) is related to F. oxysporum. In addition, two representative sequences of high-throughput sequencing were OTU_1 and OTU_3, which were identified as F. solani and F. oxysporum respectively. The two OTUs could match ITS region sequences of the two isolates (Supplementary Fig. 3). Based on phylogenetic relationships, DNA sequencing results, morphological and micro-morphology features, and spore morphology in particular, we deduced that strains NK-NH1 and NK-NH2 were the major Fusarium species that exist in the hull surface samples and that NK-NH1 was the dominant fungal community member.
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Fusarium sp. NK-NH1 and Fusarium sp. NK-NH2 were cultured on PDA plates containing 0.04% (v/v) guaiacol at 28 °C for 6 and 12 days (Fig. 4a–b). Fusarium sp. NK-NH1 significantly degraded guaiacol on PDA-guaiacol plates, whereas Fusarium sp. NK-NH2 did not exhibit such activity. Fusarium sp. NK-NH1 thus exhibited a high capacity for lignin degradation.Figure 4Colony appearances of two isolates on PDA-guaiacol, CMC, and CMC Congo red plates. (a) Fusarium sp. NK-NH1 grown on PDA-guaiacol plates for 6 (left) and 12 days (right). (b) Fusarium sp. NK-NH2 grown on PDA-guaiacol plates for 6 (left) and 12 days (right). (c) Isolates grown on CMC plates for 4 days. (d) Isolates grown on CMC Congo red plates for 6 days.
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Colony appearances of two isolates on PDA-guaiacol, CMC, and CMC Congo red plates. (a) Fusarium sp. NK-NH1 grown on PDA-guaiacol plates for 6 (left) and 12 days (right). (b) Fusarium sp. NK-NH2 grown on PDA-guaiacol plates for 6 (left) and 12 days (right). (c) Isolates grown on CMC plates for 4 days. (d) Isolates grown on CMC Congo red plates for 6 days.
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We then cultured the isolates on medium with Congo red and carboxymethylcellulose (CMC) to assess whether they could degrade cellulose. The two isolates were cultured on CMC plates for 4 days and CMC Congo red plates for 6 days. After flooding the plates with Gram’s iodine for 5 minutes, both isolate colonies showed clear and distinct zones, indicating that they were both capable of cellulose degradation (Fig. 4c–d).
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To determine the efficacy of various antibiotics towards inhibiting Fusarium spp. NK-NH1 and NK-NH2 growth, we assayed growth in the presence of five kinds of biocides on PDA media. Agents containing isothiazolinones, such as Preventol® D7, BIT 20N, P91, and Euxyl® K100, suppressed the growth of Fusarium spp. NK-NH1 and NK-NH2 at a 0.1% concentration (v/v), as indicated by inhibition rings. However, Borate buffer solution (BBS) at a 1% concentration (w/v) did not suppress the growth of either strain (Fig. 5). Of the two strains, Fusarium sp. NK-NH1 was more sensitive to isothiazolinones.Figure 5Photograph showing the inhibition of fungal growth by biocides. (a) The disks on each PDA plate were loaded with the same concentration of different biocides. Clearing zones indicate where the growth of Fusarium spp. NK-NH1 and NH2 was inhibited. (b) The inhibition efficiency of different biocides. The ordinate is the diameter of antibiotic inhibition zone. Vertical lines indicate standard deviations of three replicate tests for each.
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Photograph showing the inhibition of fungal growth by biocides. (a) The disks on each PDA plate were loaded with the same concentration of different biocides. Clearing zones indicate where the growth of Fusarium spp. NK-NH1 and NH2 was inhibited. (b) The inhibition efficiency of different biocides. The ordinate is the diameter of antibiotic inhibition zone. Vertical lines indicate standard deviations of three replicate tests for each.
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Mitigating the degradation of important cultural artefacts is of critical importance in the maintenance of important cultural heritage items. Here, we analysed the microbial communities present in an excavated 800-year-old shipwreck in order to inform future conservation efforts. Microscopic observations showed that the hull of the Nanhai No. 1 shipwreck suffered from fungal colonization.
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High-throughput sequencing of the 16S rRNA V4 regions revealed that the most abundant bacterial phyla of the shipwreck samples were Firmicutes and Proteobacteria, which were found in all of the samples. The Firmicutes genera Gracilibacillus and Alicyclobacillus were present in all samples and are often associated with earthy and drought environments but have also been detected on salt-degraded monuments15,16. Gracilibacillus are halotolerant bacteria that may not be able to degrade cellulose but can produce xylanases17. Proteobacteria was the other dominant bacterial phylum and was represented by the genera Marinobacter, Halomonas and Azoarcus. Marinobacter are frequently identified as hydrocarbon-degrading organisms in a wide variety of marine environments, including lignin-enriched environments18. Halomonas are halophiles that require high NaCl concentrations for growth. Halomonas are highly versatile and can successfully grow in a variety of temperature and pH conditions and, importantly, produce cellulases and xylanases19,20. Azoarcus are usually found in contaminated water, as they are involved in the degradation of some contaminants, commonly living in soil21. Thus its presence on the ship might be associated with the contaminants of sea water. The other bacterial phyla that were found, Bacteroidetes, Actinobacteria, Chloroflexi, Acidobacteria, and Gemmatimonadetes, are all also frequently detected in cultural heritage artefacts and humid environments22.
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Waterlogged wood from shipwrecks often displays bacterial or soft-rot decay. Cryo-sectioning and scanning electron microscopy indicated that the wood recovered from the Tektaş Burnu shipwreck was undergoing extensive degradation caused by erosion bacteria, tunnelling bacteria, and marine borer activity23. Wood samples from the rostrum of an excellent workmanship were analysed by 16S rRNA gene-based community sequencing techniques, revealing the presence of Pseudomonas spp., Sphingomonas spp., Xanthomonas spp., Marinobacter spp. and Desulforudis audaxviator24. Microbial decay is also a problem that was encountered in the preservation process of King Henry VIII’s Tudor Warship, the Mary Rose. Alicyclobacillus and Acidiphilium spp. were detected in this particular setting (66% and 34% of the communities, respectively). In addition, clone-library based 16S rRNA gene analyses indicated the presence of Acidobacteria and Bacteroidetes25. While samples collected from the Nanhai No. 1, the genera Idiomarina, Aquiflexum, Gracilibacillus, Bacillus, Halomonas, Marinobacter, Alicyclobacillus and Azoarcus were the main bacterial taxa. One possible reason for the differences of bacterial community in these cases is the different conditions of preservation. Although the shipwreck was stored in a tank with seawater, the upper deck has been exposed to the air. This brief increase in available oxygen may promote the growth of these dominant bacteria. Soft-rot decay seems to play the largest role in the deterioration of the shipwreck at present. Although a number of fungal genera were detected in wood from the Nanhai No. 1 shipwreck, the Fusarium genus was dominant in all samples and accounted for more than 90% of the total fungal communities.
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Fusarium is not typically known as an important wood-degrading fungal genus but rather a litter- and soil-associated fungus that is important for degrading plant detritus. However, the genus is often isolated from wooden materials and is known to possess high lignocellulolytic activity26,27, which was demonstrated by the plate assays in the study. Furthermore, Lavin et al. demonstrated that Fusarium spp. isolated from document collections were able to form biofilms, produce pigments, and decrease pH, which resulted in structural damage and thus constituted a hazard for document preservation28. In this case, the Nanhai No. 1 shipwreck has been waterlogged from approximately 1100 AD until 2007; it is possible that the long time on the sea bottom has changed the structure of the wood so that it is more susceptible to fungal colonization. Moreover, the new conservation conditions, including the increase in oxygen concentration and the reduction in moisture content, will play an overriding role in the colonization of the fungus after the ship is exposed to the museum’s atmospheric environment. Therefore, Fusarium spp. should be regarded as a potential threat to the wooden ship. As a follow-up, we will try to prove that Fusarium spp. actually degrades waterlogged wood of the Nanhai No. 1 shipwreck under laboratory conditions by measuring the weight loss of waterlogged wood samples or their chemical alteration after inoculation of the fungus.
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To properly conserve waterlogged archaeological wood, environmental control is essential because environmental conditions greatly influence microbial growth and their decomposition of wooden cultural properties. Destructive microorganisms are usually active in environments that are wet, oxygenated and warm29. Optimal humidity levels for white-rot and brown-rot fungi are in the range of 40%–80%30. The waterlogged state of the integral hull could impede microbial activity and confer some protection for the ship. Oxygen concentration is crucial for fungal growth, and the upper deck of Nanhai No. 1 is exposed to air. Due to the large size of the Nanhai No. 1 shipwreck and the intermittent entrance of archaeological workers into the museum to perform archaeological excavation and protection work, maintaining an oxygen-free environment may be problematic, but reducing the oxygen concentration around the ship should be considered. Moreover, the temperature inside the museum is suitable for the growth and survival of most microorganisms and thus likely contributes to the microbial colonization on the ship. The storage of waterlogged excavated wood should be at low temperature (the Mary Rose spraying system is approximately 5 °C)31.
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In addition to controlling the environment as a means to inhibit growth, efficient monitoring and protective measures should be conducted. First, sampling sites should be constantly monitored to assess whether microbial communities are changing, and microbial contamination on shipwreck surfaces should be physically removed on a regular basis. Second, and more importantly, it is necessary to use efficient and low-toxicity biocides to repress the growth of dominant fungi in order to mitigate biodeterioration. Existing commercial agents used to prevent microbial damage to cultural heritage artefacts often involve considerable amounts of potentially hazardous agents and even materials that are poisonous to artefact-protecting personnel and the environment32. The high value of cultural heritage protection is such that the risks of any inhibitory agents that are used should be assessed. Biocides that are used should be non-toxic to personnel and to the public and cause no harm to the cultural materials. Although isothiazolinones proved effective to inhibit the activity of Fusarium in our laboratory analyses, further testing will be required to ensure that their usage meets the above criteria and will also be compatible with future conservation methods and materials.
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Microorganisms clearly play an important role in the biodeterioration of cultural property. However, analysing the microbial communities, as reported here, can be helpful in selecting optimal biocides against microorganisms, thus reducing the biodegradation and destruction on the Nanhai No. 1 shipwreck and other valuable cultural artefacts that are an important part of our historical heritage.
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The annual temperature and relative humidity in the Marine Silk Road Museum are 16.4–30.7 °C with an average of 25.6 °C and 63.1–97.2% with an average of 84.1%, respectively. The ship is 30.4 metres long and has a maximum width of approximately 9.8 metres. The types of wood used in the construction of the ship included Pinus massoniana, Fokienia hodginsii, Terminalia catappa (Hainan), Mischocarpus sundaicus and Alnus trabeculosa.
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| 99.9 |
We sought to investigate the nature of the microbial contamination of the shipwreck at two periods. In April 2015, two samples (NHI.1 and NHI.4) were taken using minimally invasive sampling techniques with sterile scalpels (Supplementary Fig. 2). These samples were collected from areas showing visible mycelia on the outside of the ship’s hull. In October 2015, four samples (NHI.8, NHI.9, NHI.11, and NHI.12) were collected using the methods described above. All samples were placed into 2 mL micro-centrifuge tube and brought to the laboratory in an icebox for subsequent microscopic observation and DNA extraction. The sampling locations on the Nanhai No. 1 ship’s hull are indicated in Fig. 1. Permission to sample was issued by the Marine Silk Road Museum of Guangdong. Yue Chen and Jie Liu from the Chinese Academy of Cultural Heritage (CACH) supervised the sampling process.
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| 100.0 |
Total genomic DNA was extracted from scrapings of the wood surface using the MoBio PowerSoil® DNA Isolation Kit (MO BIO Laboratories, Inc., CA, USA) following the manufacturer’s protocol. Extracted DNA was diluted to 1 ng/μL using sterile water and then stored at −80 °C for subsequent analyses.
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| 99.2 |
Amplification of the bacterial 16S rRNA gene V4 region and the fungal ITS1 region was performed using the universal prokaryotic and fungal primers 515F/806R and the ITS5–1737F/ITS2-2043R (Supplementary Table 3), respectively, with barcodes attached that were unique to each sample33,34. All PCR reactions were carried out using Phusion® High-Fidelity PCR Master Mix with GC Buffer (New England Biolabs, UK). Amplifications were carried out in a 50 μL reaction mixture including 25 μL of Master Mix (2X), a 0.5 μM final concentration of the forward and reverse primers, 10 ng of template DNA and nuclease-free water to 50 μL. The PCR conditions were 98 °C for 1 min, followed by 30 cycles of 10 s at 98 °C, 30 s at 50 °C for 16S rRNA gene amplification or 55 °C for ITS region amplification, and 30 s at 72 °C, with a final extension of 5 min at 72 °C.
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| 100.0 |
To visualize PCR amplification success, an equal volume of 1X loading buffer (containing SYBR green) along with PCR products were loaded on a 2% agarose gel. Samples with amplicon bands in the range of 400–450 bp were chosen for further analyses. PCR products from different samples were pooled with equal molar amount. Then, mixture PCR products was purified with Qiagen Gel Extraction Kit (Qiagen, Germany).
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| 99.75 |
The purified amplicons were prepared for Illumina sequencing by constructing a library using the TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) following the manufacturer’s recommendations. The final library concentrations and quality were checked using a Qubit@ 2.0 Fluorometer (Thermo Scientific) and an Agilent Bioanalyzer 2100 system, respectively. Lastly, the library was sequenced on a Hiseq2500 PE250 platform at the Novogene Bioinformatics Technology Co., Ltd. (Beijing, China), and 250 bp paired-end reads were generated.
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| 78.56 |
Paired-end reads were assigned to samples based on unique barcodes and then trimmed of barcode and primer sequences. Paired-end reads were merged using FLASH (v. 1.2.7)35, and the resultant sequences were used as raw tags. Quality filtering of raw tags to obtain high-quality clean tags was performed according to the QIIME (v. 1.7.0)36 quality control protocol. Fungal tags were compared with the Unite database (v. 20140703), bacterial tags were compared to the SILVA Gold database (v. 20110519) using the UCHIME algorithm (v. 4.1)37 to detect chimaera sequences, and sequences flagged as chimaeras were then removed. The resultant high-quality sequences were used for further analyses. OTU clustering analysis was performed using the Uparse software (v. 7.0.1001)38. Sequences with ≥97% similarity in nucleotide identity were assigned to the same OTUs (Operational Taxonomic Units). Representative sequences for each OTU were then used for taxonomic annotation. For each representative fungal sequence, BLAST analysis was performed against the Unite database (v. 20140703)39 in QIIME (v. 1.7.0) to taxonomically annotate OTUs. For bacterial OTUs, the Greengenes database (http://greengenes.lbl.gov) was used with the RDP classifier (v. 2.2)40 algorithm for taxonomic annotation. Multiple sequence alignments were then conducted using MUSCLE (v. 3.8.31)41 in order to compare OTU distributions among samples. Read numbers were normalized to the sample with the least amount of sequences in order to standardize sequence numbers among samples.
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| 100.0 |
Samples were aseptically inoculated onto potato dextrose agar (PDA) plates directly at the sampling sites and then incubated at 28 °C for 4 days and observed daily. Colonies were transferred to fresh plates to obtain pure isolates. Fungal isolates were identified on the basis of microscopic morphology and gene sequencing.
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| 99.94 |
Pure cultures of isolates were grown on PDA plates for 5 days at 28 °C prior to DNA extraction. DNA was extracted from cultures using the CTAB method42. Fungal 28S rRNA genes were amplified using the primers LR0R/LR743. Fungal ITS1-5.8S rRNA-ITS2 genes were amplified with the primers ITS1/ITS444. PCR reaction mixtures consisted of a total volume of 50 μL containing: 1~2 μL of genomic DNA, 5 μL of 10× Reaction Buffer, 4 μL of 2.5 mM dNTP mix, 2 μL of 10 μM ITS1 primer, 2 μL of 10 μM ITS4 primer, 0.5 μL of 5 U/μL Transtaq-T DNA polymerase (TransGen Biotech), and ddH2O to 50 μL. PCR products were sequenced by GENEWIZ (Beijing, China), and sequence identities were analysed using the National Center for Biotechnology Information (NCBI) BLAST program (https://blast.ncbi.nlm.nih.gov/Blast.cgi) and the GenBank database. Each isolate was compared against known taxa present in the database. Phylogenetic analyses were conducted using the Molecular Evolutionary Genetics Analysis software (MEGA, v. 6.06) using the neighbour-joining method. Confidence in tree topology was estimated using the bootstrap method (1,000 bootstrap replicates).
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| 100.0 |
Lignocellulose degradation by fungi was visualized on plates containing guaiacol, which indicates the activity of lignocellulolytic enzymes that catalyse the oxidative polymerization of guaiacol and result in reddish brown zones in the media45. Lignocellulose degradation ability by fungi is directly proportional to the size and depth of the reddish-brown zones. Furthermore, the larger and deeper the reddish-brown zones, the stronger the lignocellulose degrading ability.
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| 100.0 |
Two different media were prepared to assess the ability of isolates to utilize cellulose: (i) CMC agar medium and (ii) CMC Congo red agar medium. CMC agar consisted of 0.2% NaNO3, 0.1% K2HPO4, 0.05% MgSO4, 0.05% KCl, 0.2% carboxymethylcellulose (CMC) sodium salt, 0.02% peptone, 1.7% agar and 1 L of tap water. CMC Congo red agar consisted of 0.05% K2HPO4, 0.025% MgSO4, 0.188% carboxymethylcellulose (CMC) sodium salt, 0.02% Congo red, 0.2% gelatin, 1.7% agar and 1 L of tap water46.Gram’s iodine consisted of 2.0 g of KI and 1.0 g of iodine dissolved in 300 mL of distilled water. Discs of fungi were cut with a 7.5 mm diameter from the actively growing colony margins of isolates. The discs were then transferred onto CMC and CMC Congo red agar plates and incubated at 28 °C for 4 and 6 days, respectively. After incubation, 5 mL of Gram’s iodine was added to CMC and CMC Congo red agar plates, and the plates were incubated at room temperature in the dark for 5 minutes. All media were autoclaved for 20 minutes at 121 °C.
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| 100.0 |
The application of biocides is one of the most important means to control microbial deterioration. Commercial agents such as Biotin® T and Preventol® RI 80 (isothiazolinones) have been widely applied on inorganic substrates that comprise cultural heritage materials, such as stone and mortars47,48. The active agent, isothiazolinones, is considered to be not only effective but also preventive when applied to paper and stone materials49. Borate buffer solution (BBS) has also been used to mitigate microbial deterioration following the full-scale excavation of the shipwreck that began in 2013. In addition to these biocide agents, we selected four additional agents, whose main component is isothiazolinones, in order to test their biocidal efficacy in the laboratory (Supplementary Table 4).
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| 99.94 |
Discs of fungi were inoculated onto PDA plates and incubated at 28 °C for the preparation of a spore suspension. After five days’ incubation, 10 mL of 0.1% tween−80 was added to each PDA plate, and conidia were scraped by a glass spreading rod and transferred into a 50 mL concentrator bowl. The spore suspension was filtered with three layers of sterile gauze, followed by centrifugation at 4,500 rpm for 10 minutes. The supernatant was discarded, and pellets were re-dissolved in sterile water. Then, 2.4 × 1011 conidia from a spore suspension of each strain were then inoculated on to PDA plates and were dispersed with a glass spreading rod. Filter paper was cut to make a circle of 0.7 cm diameter, loaded with 30 μL of different biocides and placed in the centre of PDA plates. The plates were examined for clear zones after incubation at 28 °C for 3 days. The presence of any clear zone that formed around the filter paper was recorded as an indication of inhibition against the fungal species. Each antimicrobial agent test comprised three replicates, and sterile water was used as a control.
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| 100.0 |
The segmented nature of the genome of influenza viruses is responsible for its ability to reassort its eight RNA genes when cells are co-infected with two different influenza virus strains1. Influenza A virus of swine origin (IAV-S) circulating in pigs have been known to occasionally reassort with avian and human influenza viruses to generate novel genotypes and establish new swine influenza virus lineages. Importantly, these new lineages might represent influenza pandemic threats for humans, as highlighted by the last 2009 H1N1 influenza virus pandemic2. Despite several introductions of avian influenza virus genes into swine strains by reassortment, only viruses from H1, H2, and H3 subtypes out of the 16 possible avian influenza virus subtypes have been known to circulate in pigs in the last century, mirroring the same subtypes that have been known to circulate in humans.
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| 99.94 |
At present, there are H1N1, H1N2, and H3N2 major viral subtypes circulating among pigs worldwide. Among the swine H1N1 influenza lineages, the classical H1N1 swine (CS) lineage is related to the H1N1 virus that caused both the 1918 and 2009 human pandemics3,4. The Eurasian avian-like (EA) H1N1 viruses have been prevalent in European pigs since 19795 and had contributed its neuraminidase (NA) and matrix (M) segments to the H1N1/2009 pandemic virus6. In 1997–1998, double or triple-reassortant H3N2 strains emerged containing genes from classical swine H1N1, seasonal human, and avian viruses, and they became prevalent in swine in North America7–9. Following the outbreak of H1N1/2009 pandemic in humans, reassortant viruses derived from H1N1/2009 pandemic viruses and enzootic IAVs-S have been continuously detected from pigs in many regions globally10–19. Furthermore, sporadic human infection with EA H1N1 viruses and with EA-H1N1/2009 pandemic reassortant viruses has been reported20–23. Also, human infections with the swine-derived H3N2 variant (H3N2v) have been reported and they are associated with a IAV-S reassortant H3N2 strain that contains the M segment of the H1N1/2009 pandemic virus24, emphasizing the important role of pigs in the generation of reassortant influenza viruses with the potential to infect humans. Importantly, humans lack herd immunity to EA H1N1 viruses11,25, individuals aged ≤14 years show broad susceptibility against H3N2v viruses, and the seasonal human vaccine does not induce neutralizing antibodies against these IAVs-S26. The insufficient immunity in humans against H1 and H3 IAVs-S raises concerns over whether they could be the source of the next influenza pandemic.
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| 70.56 |
Southern China has diverse IAV-S ecosystem, where H1N1, H1N2, and H3N2 subtypes and different lineages IAV-S are co-circulating in pigs6,11,27; however, IAV-S surveillance in this region of the world is still very limited. In this study, we performed surveillance in pigs in Guangxi. We found that novel triple-reassortant EA H1N1 and human-like (HL) H3N2 reassortants, which carried H1N1/2009 pandemic internal genes (PB2, PB1, PA, and NP) and CS H1N1 NS genes have been circulated in pigs for some time and they are predominant in the pig population in Guangxi. This is the first evidence that such novel IAV-S reassortants have been established in pigs in Guangxi, China. These viruses demonstrated variable levels of virulence in mice with some isolates being lethal in this animal model. We suggest that these novel IAV-S lineages should be closely monitored for their potential to cause human infections.
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| 100.0 |
From 2013 to 2015, disease outbreaks with severe respiratory signs, including fever, coughing, sneezing, nasal discharge, and low appetite occurred in ten pig farms in Guangxi. A total of 600 nasal swabs and 135 lung tissues with or without any respiratory signs were collected from farms or slaughterhouses in Nanning, Qinzhou, Chongzuo, Laibin, Liuzhou, Bobai, Guigang, Dongxing, and Baise of Guangxi (Table 1).Table 1Details of samples collected for SIVs testing in GuangxiCityFarmStrains abbreviationSampling siteTimeClinical signsaNanning1#NN1994Farm(500 sows)2013.09Difficulty breathing; PRRSV positive2#NNLXFarm (900 sows)2014.10Fever, coughing, sneezing, nasal discharge3#NNXDFarm(600 sows)2013.01Difficulty breathing; PRRSV and PCV positiveNNXD2023Farm(600 sows)2013.09Coughing, sneezing, nasal discharge4#JGB4Farm(1000 sows)2013.09Coughing and sneezingJG1Farm(1000 sows)2014.03Coughing and sneezingChongzuo5#CZ7Farm(1000 sows)2014.10Fever, coughing, sneezingQingzhou6#QZ5Farm(3000 sows)2014.08Fever, coughing, nasal dischargeLaibing7#LB9Farm(800 sows)2014.10Fever, coughing, sneezingLiuzhou8#LZA3Farm(500 sows)2015.06Coughing and sneezingBobai9#BB2Farm(1000 sows)2014.09Coughing and sneezingGuigang10#GG6Farm(800 sows)2013.05Coughing and sneezingDongxingN/ADX24Abattoirs2013.12N/ABaiseN/ABS30Abattoirs2014.11N/AN/A not available, PCV porcine circovirusaPRRSV represents porcine reproductive and respiratory syndrome virus
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| 99.94 |
As previously described28, RNA was directly extracted from nasal swabs or lung tissues. Reverse transcription was carried out under standard conditions with universal 12 primer. One pair of universal M gene primers was used to amplify M gene. M gene positive samples were inoculated onto monolayers of Madin–Darby canine kidney (MDCK) cells maintained in DMEM containing 1 μg/mL trypsin for 48 h at 37 °C. After three serial passages, virus isolates were identified by RT-PCR and the whole genome was amplified by eight pairs of primers and sequenced.
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| 100.0 |
Individual gene segments were aligned and analyzed with the Seqman and Megalign program (DNASTAR, Madison, USA). Phylogenetic trees were generated by applying the method of the Clustal IW alignment algorithm from MEGA 7.0 software (http://www.megasoftware.net/), and bootstrap values of 1000 were used.
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| 99.94 |
A total of 1170 swine serum samples were collected from different cities of Guangxi and treated with receptor-destroying enzyme (Sigma, USA) for 18 h at 37 °C, followed by heat inactivation at 56 °C for 30 min before being tested for the presence of hemagglutination-inhibition (HI) antibodies with 1% (V/V) guinea pig erythrocytes29. HI assays were performed on all treated serum samples with two contemporary viruses, EA H1N1 virus (A/swine/Guangxi/BB2/2013) and HL H3N2 virus (A/swine/Guangxi/JGB4/2013). HI antibody titers ≥20 were determined as serological positive.
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| 100.0 |
Groups of nine 6-week-old female BALB/c mice were anesthetized with CO2 and inoculated intranasally (i.n.) with 5 × 104 TCID50 of selected influenza viruses in a volume of 50 µL. Mock-infected mice were inoculated i.n. with 50 µL phosphate-buffered saline. Three mice were killed on days 3 and 5 post infection (p.i.), respectively. Organs (spleen, kidney, intestine, brain, and lung) were collected to assess for viral replication in MDCK cells. The remaining mice were monitored for clinical signs, body weight, and survival for 14 days. Mice were killed when the body weight loss was above 25% of their pre-challenge weight.
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study
| 99.94 |
A total of 735 nasal swabs or lung tissue samples were collected from pigs on farms and in slaughterhouses in Guangxi province of southern China from 2013 to 2015, containing 55 positive samples identified by RT-PCR. Fourteen strains were successfully isolated by serial passages on MDCK cells, including 11 H1N1 and three H3N2 IAVs-S. These results indicate that different subtypes of IAVs-S co-circulating in Guangxi. The nucleotide sequences of the 14 IAVs-S determined in this study will be deposited in the GenBank database under numbers KM061010 to KM061025 and MF927789 to MF927884.
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| 100.0 |
To determine the genetic characteristics of these viruses, all eight gene segments of the viruses grown in MDCKs were sequenced and phylogenetic analyses were performed. Phylogenetic analysis of the hemagglutinin (HA) reveals that the 11 H1 HA genes are separated into EA-like H1N1 lineage and H1N1/2009 pandemic lineage (Fig. 1a). All ten H1 HA genes clustered into the EA H1N1 lineage share 95.9–100% identity at the nucleotide level, whereas the interlineage homology is <74%. All the three H3 HA genes share over 98.6% of identity and are clustered into HL H3N2 lineage (Fig. 1c). Similar to the HA genes, ten N1 NA genes and one N1 NA gene are clustered into the EA H1N1 lineage and H1N1/2009 pandemic lineage, respectively (Fig. 1b). The N1 intralineage virus homology is over 94.7%, while the homology between the lineages is <89.4%. Three N2 NA genes are clustered into HL H3N2 lineage, which share over 98.7% of homology within the lineage (Fig. 1d).Fig. 1Phylogenetic trees of the HA H1 (a), NA N1 (b), HA H3 (c), NA N2 (d), M (e), NS (f), PB2 (g), PB1 (h), PA (i), and NP (j) genes of the H1N1 and H3N2 influenza lineages. The unrooted trees were generated with the MEGA 7.0 program by using neighbor-joining analysis and reliability of the tree was assessed by bootstrap analysis with 1000 replications. Neighbor-joining bootstrap values ≥70 are shown at the major branches of the trees. The 12 trees were rooted to A/Brevig_Mission/1/18(H1N1). Viruses shown in black were downloaded from available databases. The isolates in our study were marked in different color, consistent with Fig. 2
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| 100.0 |
Phylogenetic trees of the HA H1 (a), NA N1 (b), HA H3 (c), NA N2 (d), M (e), NS (f), PB2 (g), PB1 (h), PA (i), and NP (j) genes of the H1N1 and H3N2 influenza lineages. The unrooted trees were generated with the MEGA 7.0 program by using neighbor-joining analysis and reliability of the tree was assessed by bootstrap analysis with 1000 replications. Neighbor-joining bootstrap values ≥70 are shown at the major branches of the trees. The 12 trees were rooted to A/Brevig_Mission/1/18(H1N1). Viruses shown in black were downloaded from available databases. The isolates in our study were marked in different color, consistent with Fig. 2
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| 100.0 |
The PB2, PB1, PA, NP, and M genes share 83–100%, 84.2–100%, 81.2–100%, 82.4–100%, and 92.5–100% identity, respectively, at the nucleotide level. The PB2, PB1, PA, and NP genes show the same clustering pattern. Specifically, 12 out of 14 viruses are clustered with the H1N1/2009 pandemic lineages, while the remaining two viruses belong to the EA H1N1 lineage (Fig. 1g–j and Figure S1). The intralineage homologies are above 97.1%, 96.7%, 95.3%, and 96.9%, respectively. However, the interlineage homologies are <84%, 84.8%, 84.7%, and 83.5%, respectively. The M gene is also clustered into two lineages: nine H1N1/2009 pandemic and five EA H1N1 (Fig. 1e). The homology of the M genes within lineages is over 96.2%, and the homology of the M genes is <95.6% between the lineages. Remarkably, the NS gene shows distinct diversity, forming three different lineages: EA H1N1 (2), H1N1/2009 pandemic (2), and CS H1N1 (10) (Fig. 1f). The NS intralineage virus homology is over 97.4%, while the interlineage homology is <91.4%. All of these NS genes belong to the alpha lineage nomenclature of NS gene, as shown in Fig. 1f.
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| 100.0 |
Based on phylogenetic analyses of the gene segments, the viruses isolated and grown in MDCKs can be divided into six distinct genotypes (A, B, C, D, E, and F) (Fig. 2). The IAVs-S in genotypes A and B are prototypical EA H1N1 and H1N1/2009 pandemic, respectively. Notably, the IAVs-S in genotype C are novel triple H1N1 reassortants containing the HA and NA genes from EA H1N1, the NS gene from CS H1N1 and the rest of the gene segments are from H1N1/2009 pandemic. The genotype D IAVs-S is similar to the genotype C, except for the M gene, which is derived from EA H1N1. Genotype D IAVs-S were first reported in Tianjin, China in 201330 and caused human infection in Hunan, China in 201522, this genotype was detected continuously from 2013 to 2015 in Guangxi in our study. The three H3N2 viruses in genotypes E and F have HA and NA genes derived from swine HL H3N2 viruses. It should be noted that two viruses in genotype E are also novel triple reassortants containing five H1N1/2009 pandemic-origin internal genes and the NS gene derived from CS, similar to H1N1 viruses in genotype C. One virus in genotype F possessed six internal genes from H1N1/2009 pandemic viruses, which was soon detected after the spillovers of H1N1/2009 pandemic from human to pigs12. Our data demonstrate that there are EA H1N1 and HL H3N2 IAVs-S which have reassorted to acquire the CS H1N1 NS gene and H1N1/2009 pandemic internal genes. The similar pattern of internal gene reassortment acquired by multiple H1N1 and H3N2 IAVs-S from different times and locations in Guangxi suggest that H1N1/2009 pandemic ribonucleoprotein complex genes (PB2, PB1, PA, and NP genes) and CS H1N1 NS gene constellation is selectively advantageous and compatible with different surface genes. Importantly, IAV-S has the potential to cause epidemics in humans. The triple reassortant, A/swine/Guangxi/NNXD2023/2013(H1N1) (Sw/GX/NNXD2023/2013) in genotype D, shared 98.6–99.3% similarity at the nucleotide level with the recent human isolate A/Hunan/42443/2015 (HuN/42443/2015)22, as shown in Table 2, highlighting the potential ability of these new IAV-S reassortants to infect humans.Fig. 2Genotypes of H1N1 and H3N2 IAVs-S from Guangxi during 2013 to 2015.Origin of each gene segment is colored for representing the different lineagesTable 2Genome similarity of H1N1 novel reassortant viruses compared with the human isolated A/Hunan/42443/2015(H1N1)GenotypeName of virusGene segment (%)aHANAPB2PB1PANPMNSCA/swine/Guangxi/NN1994/201398.797.898.099.198.498.995.097.9CA/swine/Guangxi/CZ7/201498.098.097.999.097.898.792.897.7CA/swine/Guangxi/LB9/201498.097.997.999.097.898.794.897.6CA/swine/Guangxi/BS30/201498.098.097.998.997.898.694.897.7CA/swine/Guangxi /NNLX/201498.697.798.098.698.598.694.897.9DA/swine/Guangxi NNXD2023/201399.098.998.899.398.799.199.698.6DA/swine/Guangxi /QZ5/201498.698.197.999.098.598.697.398.0DA/swine/Guangxi /LZA3/201598.897.998.098.598.398.799.497.6aThe genome similarity was generated based on the open reading frame (ORF) sequences
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We next analyzed if these IAVs-S possessed key molecular features associated with receptor-binding ability, increased virulence, transmission, and antiviral resistance (Table 3).Table 3Amino acid substitutions in the novel H1N1 and H3N2 reassortant isolates compared with human isolatesLineage/subtypeHostVirusNo. of glycosylation sites in HAHAaPB2M2NAc13819022522622827159059162770131274EA H1N1HumanA/Hunan/42443/20157ADEQGAGREDNYEA H1N1SwinebSw/GX/NNXD2023/20136******S*****EA H1N1SwinebSw/GX/QZ5/20146******S*****EA H1N1SwinebSw/GX/LZA3/20156******N*****EA H1N1SwinebSw/GX/NN1994/20137******S*****EA H1N1SwinebSw/GX/CZ7/20146******S*****EA H1N1SwinebSw/GX/LB9/20146******S*****EA H1N1SwinebSw/GX/BS30/20146******S*****EA H1N1SwinebSw/GX/NNLX/20146******S*****EA H1N1SwinebSw/GX/BB2/20136*****T*Q*N**EA H1N1SwinebSw/GX/GG6/20136*****T*Q*N**Early H1N1AvianA/duck/Schleswig/21/19796*EG**T*Q**S*Pandemic H1N1SwinebSw/GX/DX24/20136**D***S*****Pandemic H1N1HumanA/California/04/20096**D***S*****HL H3N2SwinebSw/GX/NNXD/20136S*DIS*S****HHL H3N2SwinebSw/GX/JGB4/20136S*DIS*S****HHL H3N2SwinebSw/GX/JG1/20146S*DIS*S****H*indicated the identical amino acids with A/Hunan/42443/2015; Sw represents swine, GX represents GuangxiaH3 numberingbisolates in this studycN2 numbering
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| 100.0 |
It is generally accepted that receptor-binding preference to human-type receptor is the initial key step for a novel influenza-virus-causing-pandemic. For H1 HAs, 190D and 225D/E are known to allow efficient binding to human-type α2–6 sialic acid linked receptors (H3 numbering, which is used throughout this work)31,32. All the EA H1N1 IAVs-S were highly conserved in the HA receptor-binding site, containing 138A, 190D, 225E, and 226Q, consistent with binding to α2–6 linked sialic acid. The H1N1/2009 pandemic-like swine isolate, Sw/GX/DX24/2013 contains 138A, 190D, 225D, and 226Q, also consistent with binding to α2–6 linked sialic acid. The α2–6 linked sialic acid receptor-binding specificity of H3 HAs is known to be determined by leucine and serine at positions 226 and 228. Our HL H3N2 viruses possessed characteristic residues found in human-adapted seasonal H3N2 viruses, such as 190D, 226I, and 228S32.
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| 100.0 |
The presence of multiple basic amino acids at the HA cleavage site is characteristic of highly pathogenic influenza virus. All of our IAVs-S possessed a single basic amino acid (PSIQSR↓G or PEKQTR↓G) in the HA cleavage site. In addition, HA protein glycosylation is known to vary during influenza virus evolution33. One of the swine H1N1 isolates, Sw/GX/NN1994/2013, has seven HA potential glycosylation sites (Asn-X-Ser/Thr), which is the same number as the EA human strain (HuN/42443/2015). The remaining 13 IAVs-S had six HA potential glycosylation sites.
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| 100.0 |
Among influenza virus proteins, PB2 is considered a major viral determinant of influenza viruses in mammals. The 627K and 701N amino acid residues in PB2 are known to play an important role in mammalian fitness for influenza viruses34–38. Most of IAVs-S with EA H1N1-origin PB2 genes have 701N. For H1N1/2009 pandemic virus, it was reported that 271A in PB2 enhances the transmissibility of influenza A in ferrets and 591R is important for H1N1/2009 pandemic in mammalian adaptation38–40. In our study, the IAVs-S with H1N1/2009 pandemic PB2 contain 271A and 591R.
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| 100.0 |
Antiviral compounds are the first line of defense against novel influenza viruses until vaccines become available. Currently, two classes of drugs, adamantanes and NA inhibitors are available for treatment of influenza infections. The molecular marker of amantadine resistance, 31N in the transmembrane region of the M2 protein, is observed in all the isolates (Table 3)41. Meanwhile, H274Y mutation conferring resistance to NA inhibitors is also found in all the H1N1 swine isolates. However, the H3N2 IAVs-S do not have any known resistant mutation in the N2 against NA inhibitor.
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| 100.0 |
Of the 1170 serum samples collected from different cities in Guangxi, 170 (14.6%) were positive solely against EA H1, while 219 (18.8%) of the sera were positive solely for HL H3 swine virus (Table 4). Additionally, 126 (10.8%) were reactive against EA H1 and HL H3 according to the HI assay, indicating the possibility of a high frequency of coinfection, successive infection over time, and/or multiple infections by different IAV-S subtypes in the pig population. We did not test serologically for antibodies against other influenza A virus strains, our data are conservative estimates of the seroprevalence of IAV-S in the pig population.Table 4Seroprevalence of antibodies against different swine influenza virus in pigs in Guangxi from 2009 to 2013LocationSerum samplesaNo. (%) of sera positiveEA H1N1HL H3N2EA H1N1 + HL H3N2Nanning28386 (30.4)166 (58.7)45(15.9)Beihai6024 (40.0)29 (48.3)8(13.3)Fangchenggang403 (7.5)5 (12.5)1(2.5)Liuzhou10128 (27.7)17 (16.6)8(7.9)Laibin13031 (23.8)4 (3.1)2(1.5)Guilin13533 (24.4)33 (24.4)16(11.9)Hezhou6718 (26.9)17 (25.4)9(13.4)Hechi809 (11.2)3 (1.3)1(1.3)Baise606 (10.0)1 (1.7)0(0)Yulin13444 (32.8)51 (38.1)25(18.7)Guigang8014 (17.5)19 (23.8)11(13.8)Total1170296 (25.3)345 (29.5)126(10.8)aEurasian avian-like (EA) H1N1, A/swine/Guangxi/BB2/2013(H1N1); Human-like swine (HL) H3N2, A/swine/Guangxi/JGB4/2013 (H3N2)
|
study
| 100.0 |
We selected ten H1N1 and three H3N2 influenza viruses, which included examples of each genotype and evaluated their replication and virulence in BALB/c mice. Groups of nine 6-week-old BABL/c mice were inoculated i.n. with 5 × 104 TCID50 of virus in a volume of 50 µL. The average body weight loss of mice caused by two H1N1 viruses (Sw/GX/CZ7/2014 (genotype C) and Sw/GX/NNXD2023/2013 (genotype D)) reached to 15.5% and 15.1%, respectively (Fig. 3a), especially Sw/GX/CZ7/2014 caused 33% of mice death (Fig. 3b). The rest did not show obvious weight lost or death during the 2-week observation. All 13 viruses replicated in lungs of mice with titers ranging from 3.1 to 5.0 log10 TCID50/mL and 4.5 to 7.3 log10 TCID50/ml at days 3 and 5 p.i., respectively (Fig. 3c), with higher titers observed by the lethal Sw/GX/CZ7/2014 virus. There was no detection in spleens, kidneys, intestines, or brains of any mice. These results indicated that novel triple-reassortant viruses could replicate well in mice lung without prior adaptation and two H1N1 IAVs-S causing sign of disease and lethality. Interestingly, although it is known that H3N2 human viruses replicate to low levels in mice without adaptation42, this was not the case for the IAV-S H3N2 viruses isolated in our surveillance.Fig. 3Weight variation (a), survival rates (b), and replication (c) of novel reassortant IAVs-S in mice. Mice in each group were infected intranasally with 5 × 104 TCID50 of virus in a volume of 50 µL. The body weight and survival rates of mice were measured over 14 days. Virus titers of lung on 3 and 5 days post infection (d.p.i.) were shown as the mean titers of three mice
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study
| 100.0 |
Weight variation (a), survival rates (b), and replication (c) of novel reassortant IAVs-S in mice. Mice in each group were infected intranasally with 5 × 104 TCID50 of virus in a volume of 50 µL. The body weight and survival rates of mice were measured over 14 days. Virus titers of lung on 3 and 5 days post infection (d.p.i.) were shown as the mean titers of three mice
|
study
| 100.0 |
Here our surveillance study reveals that EA H1N1, H1N1/2009 pandemic, and HL H3N2 IAVs-S have been co-circulating in pigs in Guangxi, China from 2013 to 2015 (Table 1). Serological data suggested that multiple subtype exposure occurred (Table 4). Co-circulation of parent viruses in pigs could facilitate reassortment events. After the repeated introduction of pandemic H1N1 virus into pig population, novel reassortant viruses with H1N1/2009 pandemic-origin genes have been isolated frequently from pigs globally19,43–45. Notably, in this study, the predominant strains are novel triple EA H1N1 and HL H3N2 reassortants, which contains the CS H1N1 NS genes and the remaining five or four genes originating from H1N1/2009 pandemic. Swine triple-reassortant viruses with H1N1/2009 pandemic internal genes and CS H1N1 NS gene may have then become established in Southern China.
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study
| 100.0 |
The transition from avian-type to human-type receptor-binding preference is a crucial step for influenza viruses to replicate efficiently in humans46,47. However, the specific amino acids that determine receptor-binding specificity vary among the different HA subtypes. For the H1 virus subtype, substitutions at E190D and G225D/E are important for the change in preference from avian to human receptors31,48. Yang et al.25 showed that pandemic H1N1 and EA H1N1 IAVs-S preferentially bind to human-type receptor. For the H3 virus subtype, substitutions Q226L and G228S are important for the change in preference from avian to human receptors31,48. Swine H3N2 displayed binding preference for α2, 6-SA receptors, if they possess the 190D, 226V/I/L, and 228S32. Our H1N1/2009 pandemic and all EA H1N1 IAVs-S possess 190D and 225D/E, and HL H3N2 IAVs-S contain 190D, 226I, and 228S, which indicates that these isolates possibly bind to human-type receptors with high affinity.
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study
| 100.0 |
In recent years, concerns about low-pathogenic influenza has been increased, since it may be able to replicate well and possibly cause disease in humans, as observed during the 2009 H1N1 pandemic4,49,50. Mutations in the polymerase complex are critical for influenza viruses to infect and adapt mammals; notable examples of such mutations are 271A, 627K, 701N, and 591R36–39,51,52. Our prototypical EA H1N1 IAVs-S have 701N. For H1N1/2009 pandemic PB2, it is reported that 271T and 591R confer efficient replication and transmission in mammals38,39. All of our strains with H1N1/2009 pandemic PB2 bear 271A and 591R. These indicate that 14 IAVs-S in our study have mammalian-adapting mutations in their PB2 genes (Table 3). Importantly, IAVs-S containing these particular marker combinations are transmissible in ferrets, as previously described25.
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study
| 100.0 |
A key prerequisite for influenza pandemic is that the virus becomes highly transmissible in humans. Numerous studies have reported that the internal genes from H1N1/2009 pandemic are a critical factor to promote aerosol transmissibility for reassortant viruses38,39,50,52–56. In this study, we identified novel swine triple H1N1 and H3N2 reassortants with similar internal genetic composition (genotypes C, D, and E). This provides direct evidence that pandemic H1N1 internal genes with or without the CS H1N1 NS gene complex have been successfully incorporated by reassortment and fixed into EA H1N1 and HL H3N2 IAVs-S at least during our surveillance period of time.
|
study
| 100.0 |
Currently, influenza A H1N1 and H3N2 viruses are the circulating seasonal influenza A viruses subtypes in human. The H1N1/2009 pandemic became the current seasonal H1N1 virus. Our EA H1N1 HAs share <73.7 and 78.1% similarity with the H1N1/2009 pandemic vaccine strain (A/Michigan/45/2015 H1N1), at nucleotide level and amino acid level, respectively. Our H3N2 IAVs-S share <94.1 and 91.5% similarity with the H3N2 vaccine strain (A/Hong Kong/4801/2014 H3N2), at nucleotide level and amino acid level, respectively. Studies have reported that seasonal trivalent inactivated influenza vaccine induce poor cross-reactive antibodies to EA H1N1 virus23 and does not protect against swine H3N257. Importantly, according to the risk assessment tool, which is developed by the Centers for Disease Control and Prevention in the United States to evaluate the pandemic potential of different influenza strains58, we found that the EA H1N1 and swine H3N2 viruses are among the animal viruses with the highest risk score in Yang’s analysis26. Besides, at least one human infection with a similar reassortant IAV-S has been reported22. We suggest that intensive surveillance of IAV-S and of swine-to-human infections with the IAV-S described in our study should be a priority for future research.
|
study
| 100.0 |
Figure S1 Phylogenetic analysis of the PB2 (A), PB1 (B), PA (C) and NP (D) of the 14 IAVs-S. The unrooted trees were based on nucleotides sequences of PB2, PB1, PA and NP and were generated with the MEGA 7.0 program by using neighbor-joining analysis and reliability of the tree was assessed by bootstrap analysis with 1000 replications. Neighbor-joining bootstrap values ≥70 are shown at the major branches of the trees
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study
| 100.0 |
Figure S1 Phylogenetic analysis of the PB2 (A), PB1 (B), PA (C) and NP (D) of the 14 IAVs-S. The unrooted trees were based on nucleotides sequences of PB2, PB1, PA and NP and were generated with the MEGA 7.0 program by using neighbor-joining analysis and reliability of the tree was assessed by bootstrap analysis with 1000 replications. Neighbor-joining bootstrap values ≥70 are shown at the major branches of the trees
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study
| 100.0 |
Because particle beams have characteristics such as a Bragg peak and a steep lateral penumbra, they minimize the damage to surrounding normal tissues and effectively concentrate damage onto the tumor.1, 2 However, the high‐dose radiation still poses some risk to normal tissues and adverse effects can occur if the irradiation position shifts from the target. Therefore, accurate patient positioning is necessary for irradiation treatment.
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other
| 99.9 |
For photon therapy, patient positioning is often determined using CT images acquired during treatment planning and cone beam (CB) CT images acquired at the time of treatment.3, 4, 5, 6 However, simple x‐ray images are commonly used to determine patient positioning for particle therapy at many facilities. There are also some commercial CBCT solutions for particle therapy. For example, CBCT can be included within IBA equipment, although it is probably difficult to adapt this to prevent collision with the irradiation nozzle in a facility with fixed beam lines. Thus, positioning is based on bony structures using the x‐ray images, with a certain margin added for the uncertainty of interfractional motion of the target to assure that the irradiation dose hits the target.
|
other
| 98.06 |
Our facility provides carbon ion radiotherapy as a treatment option for some cancers.7, 8 Radiography technologists manually perform patient positioning using orthogonal (vertical and horizontal) radiographic images. Manual positioning requires skill and experience because individual differences in positioning can increase the exposure dose with repeated x‐ray images. Additionally, inexperience can result in longer time necessary for positioning. It takes approximately 10–15 min for patient positioning, with 30–60 s for each single matching. Therefore, a high‐precision and high‐speed automatic positioning system is needed to realize safer treatments and increase treatment throughput.
|
other
| 99.9 |
ExacTrac (BrainLAB) is an automatic patient positioning system used in many photon therapy facilities.9, 10, 11 Although this system achieves fast and highly accurate automatic patient positioning, it is incompatible with particle therapy, which requires visualization of bony structures, because all bony structures in the x‐ray image size of the ExacTrac system cannot be seen. Mori et al. reported an automatic patient positioning system for carbon ion radiotherapy.12, 13 The accuracy of the system was evaluated for tumors in three sites (pelvis, head and neck (H&N), and lung) and the authors reported the optimal metrics for the calculation. However, the system was not evaluated for use in other sites such as liver and pancreas. Additionally, the study did not mention the optimal region size for the calculation at each site. The positioning error could possibly be reduced by choosing the optimal region size for each target site.
|
review
| 76.6 |
We have developed a high‐precision system for calculating patient positional displacement between digital radiography images (DRs) and digitally reconstructed radiography images (DRRs), to reduce the radiation exposure to patients, minimize individual differences among radiological technologists, and decrease the positioning time for carbon ion radiotherapy. In this study, to clarify the practicality of the system, the accuracy of this system was evaluated relative to our setup tolerance using clinical data from patients with tumors at five sites. Moreover, the dependence of calculation accuracy on the size of the region of interest (ROI) and initial positioning parameters were evaluated for each site. It may be useful to know the initial positional dependence to calculate the limits of our system.
|
study
| 100.0 |
At our facility, CT images are acquired with x‐ray CT (Aquilion LB, Self‐Propelled, Toshiba Medical Systems); treatment planning is performed with the XiO‐N system (Mitsubishi Electric and Eleckta). In the treatment room, horizontal and vertical x‐ray tubes, flat panel detectors (DAR – 8000f, Shimadzu), and carbon beam irradiation nozzles are positioned as shown in Fig. 1. Patient positioning is performed using orthogonal DRs acquired with the flat panel detectors and DRRs reconstructed from CT data during treatment planning.
|
other
| 99.7 |
Fifty patients treated at our facility for cancer of the prostate, lung, H&N, liver, or pancreas (n = 10 each) from April 2010 to November 2015 were randomly selected for retrospective analysis. Each pair of orthogonal images at completion of patient positioning on 1 day during the treatment period was retrospectively analyzed. This study was approved by the Institutional Review Board at our hospital (approval number: 15‐109); all data were anonymized. Orthogonal DRs after patient positioning, treatment planning data, and CT images were evaluated.
|
study
| 99.94 |
The system for calculating patient positional displacement between DRs and DRRs was developed based on a 2D‐3D registration algorithm.14, 15, 16 The system can calculate the positional displacements between the DR and DRR. A flowchart of the calculation algorithm for our system is shown in Fig. 2. The system uses two main procedures, which are 2D matching and roll optimization. The 2D matching step is intended to reduce the calculation costs, as the creation of DRRs, which have a very high calculation cost, only then occurs in the first iteration. The investigation of patient positional displacement between the DR and DRR optimized six parameters d=(dx,dy,dz,dθx,dθy,dθz), indicating lateral, longitudinal, and vertical directions and pitch, roll, and rotation, respectively. The value d0=(dx,dyV,dθz) was optimized on the vertical images for 2D matching; d1=(dz,dyH,dθx) was optimized on the horizontal images for 2D matching. The variables indicate the lateral axis, vertical axis, and rotation on each image. After 2D matching, dy was calculated as dy=(dyV+dyH)/2. Additionally, d2=dθy was optimized on both images for roll optimization. When d2 was calculated in the roll optimization step, the other five parameters (dx,dy,dz,dθx,dθz), calculated in the 2D matching steps, were directly used. The steepest descent method and the golden section method were used to optimize 2D matching; the golden section method was used for roll optimization.
|
study
| 100.0 |
Zero‐mean normalized cross‐correlation (ZNCC)1 11, 17 was used to assess the similarity between DR and DRR. ZNCC is shown in eq. (1):(1)ZNCC(di)=∑w(IDR−IDR¯)(IDRR(di)−IDRR¯)∑w(IDR−IDR¯)2∗∑w(IDRR(di)−IDRR¯)2(i=0,1,2)[Correction added on 8th February 2018, after first online publication: Equation was corrected.]
|
study
| 99.94 |
where W is the calculation window inside the region of interest (ROI) on vertical or horizontal images, IDR is the pixel value of each image, and IDRR(di)is also the pixel value of each image generated by moving each image or CT volumes with di. The average pixel values in the calculation window for DR and DRR are IDR¯ and IDRR¯, respectively. [Correction added on 8th February 2018, after first online publication: Equation was corrected.] Optimization was performed to minimize an evaluation value calculated as fdi, shown in eq. (2). In roll optimization, ZNCC was used for the average of ZNCCs on the vertical and horizontal images. The image size of DR and DRR for all calculation steps used in this study was 256 × 256; the pixel spacing was 0.447 mm. The CT image size was 512 × 512. The pixel spacing in prostate, lung, liver, and pancreas cases was 1.074 mm; the pixel spacing in H&N cases was 0.879 mm. The CT slice thickness at all sites was 2 mm. (2)f(di)=1−ZNCC(di)
|
study
| 100.0 |
Our calculation system was implemented using a client server system (VT64 Workstation E5‐4S; CPU, Xeon E5‐2670 2.60 GHz (8 cores) × 2; Memory, 32 GB; Operating system, Red Hat Enterprise Linux Server release 6.3: Visual Technology). The calculation program was written in C++ and CUDA 5.018 with open libraries (OpenCV 2.4 and DCMTK 3.6). The client PC (DELL Vostro; CPU Intel Core i7‐3770 3.4 GHz; Memory, 4 GB; Operating system, Windows 7) used a GUI‐based program written in Visual Studio C# with the library OpenCVSharp 2.4.
|
other
| 99.94 |
To evaluate the accuracy of the system, two radiological technologists with sufficient positioning experience determined the best‐matched position for the bony structures on six parameters between the DR and DRR using the system's manual mode; this position was defined as the reference position. When the reference positions were xref,yref,zref,θx,ref,θy,ref,θz,ref, the error of the system was calculated as the root mean square errors (RMSEs) shown in eqs. (3) and (4). These values were separately calculated in translational and rotational directions. (3)Translation:ΔT=(dx−xref2+dy−yref2+dz−zref2)/3 (4)Rotation:ΔR=(dθx−θx,ref2+dθy−θy,ref2+dθz−θz,ref2)/3
|
study
| 100.0 |
Additionally, eq. (5) was used to determine if the errors were within our tolerance. If eq. (5) was satisfied, the calculation result was acceptable.(5)ΔD=ΔTt2+ΔRr2<1,where t is a translational tolerance factor and r is a rotational tolerance factor. At our facility, setup tolerance is set at 2 mm. 19 Moreover, the angle corresponding to a 2‐mm displacement over 7.5 cm (one half of the maximum irradiation field) is 1.53°. Therefore, t=2 and r=1.53 were used in this study.
|
study
| 100.0 |
Eight patterns of DR sets and DRRs of a head and neck phantom (Whole Body Phantom PBU‐50, Kyoto Kagaku) were used to verify that our calculation system worked normally. The eight patterns of DR sets are shown in Table 1. The length × width of ROI on the vertical image was 41.2 × 46.5 mm, and these values on the horizontal image were 43.0 × 38.4 mm. The reference DRs are shown in Fig. 3.
|
study
| 100.0 |
To evaluate the accuracy dependence on ROI size at the five sites, three ROI sizes (small, medium, and large) were defined on the horizontal and vertical images. The length × width of small, medium, and large ROI on the vertical image were 41.2 × 46.5 mm, 61.7 × 69.7 mm, and maximum displayed DR, respectively; these values on the horizontal image were 43.0 × 38.4 mm, 64.4 × 57.6 mm, and maximum displayed DR, respectively. The center of both small and medium sizes was set at the isocenter. In most cases, the small size contained the planning target volume, which is an important matching target for the patient positioning, while the medium size usually contained the nearest bones. Examples of the three sizes are shown in Fig. 4. The initial positional values (x,y,z,θx,θy,θz) on the CT images were set to (5 mm, 5 mm, 5 mm, 0.5°, 0.5°, 0.5°) to replicate positioning in clinical use.
|
study
| 100.0 |
Examples of each ROI size on the DR. (a) Small size for prostate cancer patient. (b) Medium size for lung cancer patient. (c) Large size for H&N cancer patient. The upper row shows the vertical image, the lower row shows the horizontal image, and the yellow box shows the ROI.
|
other
| 99.9 |
Four patterns of the initial parameters (x,y,z,θx,θy,θz) on the CT images were set: (2 mm, 2 mm, 2 mm, 0.2°, 0.2°, 0.2°), (5 mm, 5 mm, 5 mm, 0.5°, 0.5°, 0.5°), (10 mm, 10 mm, 10 mm, 1°, 1°, 1°), and (20 mm, 20 mm, 20 mm, 2°, 2°, 2°). The accuracy of each pattern was evaluated using the ROI size that had the smallest error, calculated as described in Section 2.D.3, to evaluate the dependence of accuracy on initial position parameters.
|
study
| 100.0 |
To evaluate the correlation between the calculation result and potential error in each image, averages of vertical and horizontal f(d)and ΔD in the reference position were compared in all cases using the result of the Section 2.D.3 condition, and the correlation coefficient R was calculated for each site.
|
study
| 99.94 |
The calculation results for the five sites are shown in Table 2. The optimal ROI size was small for prostate cancer and liver cancer, large for lung cancer and pancreatic cancer, and medium for H&N cancer. For all sites, the calculation times for small, medium, and large sizes were 25.1 ± 2.91, 24.3 ± 2.3, and 24.1 ± 1.90 s, respectively. At their optimal ROI size, all calculations in the prostate, lung, and H&N cancer patients were acceptable.
|
study
| 100.0 |
Figure 5 shows the calculation results and the number of acceptable cases at each site when the initial positional values were changed. When the initial position parameters for the translation and rotation were 20 mm and 2°, all cases were acceptable only for lung cancer. In contrast, nine prostate cancer cases, seven H&N cancer cases, three liver cancer cases, and three pancreatic cancer cases were acceptable.
|
study
| 100.0 |
For the preliminary verification of the head and neck phantom, the average errors were within 0.2 mm and 0.2°, as shown in Table 1. Although these include variations due to the radiological technologists and the error of the calculation system, it was assumed that the system works normally and correctly.
|
study
| 75.8 |
Because all cases of prostate, lung, and H&N cancer were acceptable with their optimal ROI sizes (Table 2), they were accurate enough to be feasible at the actual treatment site. However, more than half of liver cancer and pancreatic cancer cases were unacceptable. Here, we consider the cause of large error in one case of pancreatic cancer. The red box in Fig. 7(a) shows the position of one vertebra on the DR; the red line shows the position of the diaphragm. The blue boxes in Figs. 7(b) and 7(c) show the position of the vertebra on the DRR (corresponding to the red box in Fig. 7(a)); the blue lines show the position of the diaphragm (corresponding to the red line in 7 (a)). The positions of the red and blue boxes are almost the same in 7(a) and (c); however, the positions of the red and blue lines are different. Abe et al. reported that the average interfractional error in the marker position was 3.4 mm in the superior–inferior direction.20 Additionally, Kawahara et al. reported that the average interfractional error in the diaphragm position was 3.4 mm in the superior–inferior direction.21 As shown above, because the positional reproducibility of the liver in the abdomen is low, the patient positioning calculation was misled by the diaphragm position although patient positioning should be performed based on the vertebra position. Considering the correlation values on the images, the ZNCC on the position in Fig. 7(b) is 0.953, and that on the position in Fig. 7(c) is 0.757. The finding that the ZNCC on the calculation was higher than that on the reference position indicates that it is difficult to calculate the optimal patient positioning using the ZNCC alone. In contrast, the images in Figs. 7(a) and 7(c) show that the position of the diaphragm in the DR was different than that in the reference position, whereas the position of the vertebra was almost the same. We assume that the radiological technologist positioned the patient on the basis of the vertebrae, excluding low‐reproducibility regions such as the diaphragm.
|
study
| 100.0 |
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