Escherichia coli O157:H7 is a food-borne pathogen and the major cause of hemorrhagic colitis. Pseudomonas is the genus most frequent psychrotrophic spoilage microorganisms present in milk. Two-species bacterial systems with E. coli O157:H7, non-pathogenic E. coli, and P. fluorescens in skimmed milk at 7, 13, 19, or 25 °C were studied. Bacterial interactions were modelled after applying a Bayesian approach. No direct correlation between P. fluorescens’s growth rate and its effect on the maximum population densities of E. coli species was found. The results show the complexity of the interactions between two species in a food model. The use of natural microbiota members to control foodborne pathogens could be useful to improve food safety during the processing and storage of refrigerated foods.Shiga toxin-producing  Escherichia coli O157:H7 strains are foodborne pathogens causing hemorrhagic colitis or the hemolytic uremic syndrome [E. coli O157:H7 can go as far as several days or weeks in milk and dairy products [Shiga toxin-producing syndrome ,3,4,5. Tproducts ,7,8 showPseudomonas, to milk [Pseudomonas also appears in pasteurized dairy products as a post-processing contaminant [Soil, water and vegetation are the main sources of psychrotrophic spoilage microorganisms, such as  to milk ,10,11. D to milk . Pseudomtaminant ,13.P. fluorescens produces extracellular materials resulting in a competitive advantage over other species [Staphylococcus aureus’s growth in the presence of Pseudomonas spp was reported. Marshall and Schmidt [Listeria monocytogenes was co-cultured in the presence of P. fluorescens. Other studies reported conversely that P. fluorescens can inhibit the growth of L. monocytogenes [E. coli O157:H7 by P. fluorescens [P. fluorescens and Bacillus spp. were able to act as biocontrol agents of Salmonella Saintpaul on Jalapeno pepper or of E. coli O157:H7 on TSA agar and bell pepper disks. On the surface of spinach leaves, Olaya et al. [P. fluorescens moderately suppressed the growth of E. coli O157:H7. The same research group also showed P. fluorescens inhibition of E. coli O157:H7 in poor environments such as distilled water or buffered peptone water [Different bacterial species interact without physical barriers in many natural environments, and foods are one such example where co-culture experiments have shown the prevailing genotypes in mixed cultures ,15,16,17 species  or the n species . In earl species ,22, enha Schmidt  and Farr Schmidt  found siytogenes ,28,29,30orescens . Liao [3orescens  and Liaoorescens  reporteda et al.  reportedne water .E. coli O157:H7 or non-pathogenic E. coli and P. fluorescens in skimmed milk over a range of temperatures and times that are both typical and atypical for milk storage and distribution.The aim of our work is to study the interaction between co-culturing bacterial species using Bayesian inference. The Bayesian approach provides a consistent framework for estimating parameters from a model using prior knowledge about the system to improve the estimations , with thEscherichia coli: O157:H7 LCDC 86-51 , O157:H7 ATCC 35150 , and the non-pathogenic strain E. coli ATCC 8739 (Ec) were used. All strains were cultured overnight in BHI broth  at 37 °C. Pseudomonas fluorescens ATCC 13525  was cultured in BHI at 25 °C for 24 h.Three strains of 9 CFU/mL was reached as previously described [4 CFU/mL were achieved. These populations were evaluated by spreading onto TSA  plates and incubating at 37 °C for 48 h.Bacterial cultures were grown at 37 °C until a population of 10escribed . Briefly4 CFU/mL were selected and co-cultures of EcO1, EcO2, Ec, and Pf were prepared and stored at 7, 13, 19 or 25 °C. Actual co-culture starting populations of 3.9–4.1, 4.0–4.1, 4.0–4.1, and 3.9–4.1 log CFU/mL were obtained for EcO1, EcO2, Ec, and Pf, respectively. Single cultures of EcO1, EcO2, Ec, and Pf were cultivated with the same initial populations. Control cultures of un-inoculated skimmed milk were prepared and stored under the same conditions. Assays were carried out in triplicate.A first 4-level factor  and a second 4-level factor  were used for a 4 × 4 full factorial experiment. Starting concentrations of ca. 10Escherichia spp. counting, plates were incubated at 37 °C for 18–24 h, and random colonies were serologically confirmed using the E. coli O157 Latex Test Kit . P. fluorescens colonies were counted after an incubation at 35 °C for 24–48 h.Cultures were at 0, 2, 4, 6, 12, or 24 h, and 2, 4, 8, 12, 16, 20, 28 days. Aliquots (0.1 mL) were surface-plated onto MacConkey Sorbitol Agar  and Fluorocult VRB-Agar  or onto Pseudomonas Agar F or Flo Agar . For E. coli and P. fluorescens were transformed to decimal logarithmic values. The lag time (λ), the maximum population density (Nmax), and the time to reach (ttr) populations of 6 or 8 log CFU/mL were estimated for each culture using the DMFit, ComBase [t/dt)/Nt is the relative or instantaneous growth rate of the microorganism, tN is the cell concentration in a bacterial culture at time t, and maxµ is the maximum growth rate. The term tα is an adjustment function, and tf is a logistic inhibition function for two-species mixed cultures [λ is the lag time, tNa and Nbt are the cell concentration of the microorganisms a or b in co-culture at time t, and maxN is the total carrying capacity (both species). For EcO1 cultures the model can be re-defined:EcO1 (3a) and µEcO1(Pf) (3b) are the maximal growth rates of EcO1 cultured alone or in the presence of P. fluorescens, respectively. Similar approaches to the equations (3a–b) were done for the cultures of EcO2 ), Ec ), and P. fluorescens , µPf(EcO2), and µPf(Ec)). When the cultures reached their maximal values, a decline period was observed. The decline phase was modeled alone with a modification of equations (3a–b), i.e., µ was replaced by the negative-sign parameter k in order to reflect the negative slope of that survival growth section.Plate counts of  ComBase . Then, a ComBase  was selecultures :αt={0,  t* = Nt + εt, where Nt is the population of EcO1, EcO2, Ec, or Pf cultured alone or in co-culture, and εt is a normally distributed error term with zero mean and constant variance equal to σt: Nt* ~ Normal .The approach above assumes deterministic behavior, but an error term may be introduced to reflect the influence of factors outside the experimental design. Thus, the observed concentration of bacteria at time t may modelled as NEcO1, µEcO2, µEc, and µPf), the 2-species mixtures (µEcO1(Pf), µEcO2(Pf), µEc(Pf), µPf(EcO1), µPf(EcO2), and µPf(Ec)), and the standard deviation of errors (σt); the other terms are constants and are shown as squares. Decline rates and the 95% credible intervals were also estimated based on the posterior distribution of parameters from equations 3a–b. The estimation of parameters, by means of a Bayesian methodology, is undertaken by simulating the posterior distribution of the model parameters, which includes the likelihood of the experimental data  and the prior distributions of the parameters. A general introduction to this methodology in differential equations for biological systems can be seen in [A Bayesian estimation of the parameters for computing the posterior distribution of parameters of the model was carried out. The estimated parameters are shown in  seen in .t and σt [The Runge-Kutta method was used to discretize the system of differential equations ; then tht and σt . In eacht and σt  via Rstat and σt  was usedE. coli O157:H7 LCDC 86-51 (EcO1), E. coli O157:H7 ATCC 35,150 (EcO2), and non-pathogenic E. coli (Ec) co-cultured with P. fluorescens (Pf) at 7 or 25 °C in skimmed milk are shown in E. coli spp. co-cultured with P. fluorescens or single-cultured at 7, 13, 19, or 25 °C. The highest µ values were detected at 19 and 25 °C. It is interesting to observe the differences on the growth rates between E. coli spp. and P. fluorescens cultured alone: a psychrotrophic bacteria such as P. fluorescens did not show the lowest growth rates at any temperature except for 25 °C; however, E. coli spp. showed it at 7 and 13 °C. Co-cultured P. fluorescens showed similar growth rates as when cultured alone. The effect of P. fluorescens on the growth rate of E. coli strains appears to be greater at low temperatures (7 and 13 °C) increasing E. coli spp. µ values. At higher temperatures (19 and 25 °C) P. fluorescens does not seems to cause the same effect. EcO1, µEcO2, µEc, or µPf) and decline rates  of E. coli spp. and P. fluorescens cultured alone or in co-cultures. The E. coli spp. co-cultures show the lowest µ values at 7 and 13 °C ranging from 0.563 day−1 for the EcO1(Pf) co-cultures at 7 °C to 0.945 day−1 for the Ec(Pf) co-cultures at 13 °C. At 19 and 25 °C the µ values were similar ranging from 1.358 day−1 for the EcO1(Pf) co-cultures to 2.178 day−1 for the Ec(Pf) co-cultures at 25 °C.Bayesian inference examples of growth and decline periods for E. coli strains cultured alone were not detected . The higher k decline rates from single-cultured strains were found in EcO2 cultures at 19 °C (–0.233 day−1) and 25 °C (–0.247 day−1).At 7 °C, decreasing populations of the three detected . DecreasEcO1, σEcO2, σEc, or σPf) and decline rates  of E. coli spp. and P. fluorescens cultured alone or in co-cultures. Standard deviation is read as the predicted concentrations’ random error of the microorganisms’ real observations.The standard deviations from the survival curves, i.e., with growth and decline periods included, are shown in max) of E. coli spp. and P. fluorescens in single cultures or co-cultured in milk are shown in E. coli spp. Nmax values were observed at 7 °C (4.4–4.8 log CFU/mL in single cultures and 5.2–5.4 log CFU/mL in co-cultures); at 13, 19, or 25 °C, the Nmax values were similar for all co-cultures (8.0–8.5 log CFU/mL in single cultures and 7.9–8.5 log CFU/mL in co-cultures). The P. fluorescens Nmax values were similar for all co-cultures at all temperatures (8-9 log CFU/mL).The maximum population density , decreasing at 19 and 25 °C and showing faster growth: 0.56–0.64 day and 0.40–0.48 day, respectively. The ttr 6 log values of P. fluorescens were lower than those from E. coli strains at all temperatures, indicating faster growth. All E. coli spp. and P. fluorescens cultures were able to reach a population of 8 log CFU/mL, except E. coli spp. at 7 °C. At 13 °C all E. coli spp. cultures reached 8 log CFU/mL at 3.36–3.68 d; lower results were found at 19 or 25 °C indicating a faster growth: 1.04–1.12 day or 0.56–0.96 day, respectively. All P. fluorescens cultures reached at 8 log CFU/mL after 0.96–1.04 d at 7 °C; the ttr 8 log results at 13, 19, or 25 °C were lower showing a faster growth: 1.44–1.76 day, 0.96–1.04 day, or 0.56–0.88 day, respectively. The P. fluorescens’s ttr 8 log values were lower than those from E. coli strains at all temperatures, indicating overall faster growth.The time to reach (ttr) a population of 6 or 8 log CFU/mL is shown in E. coli O157:H7 strains in refrigerated food such as milk depicts a health risk for the consumers. The native microbiota or the presence of protective cultures could compete with the pathogens and help in controlling E. coli O157:H7 strains during the processing and storage of refrigerated food [Pseudomonas spp. could be important competitors in perishable refrigerated food products due to their psychrotrophic profile (able to grow at 0–15 °C) [The presence of ted food ,51. Pseu0–15 °C) ,52.Pseudomonas spp.), may be useful in the control of E. coli O157:H7 growth. According to Samelis and Sofos [E. coli O157:H7 co-cultured with Pseudomonas sp. grew faster as the temperature increased from 10 to 15 or to 25 °C in TSB broth. These authors found that the pathogen inhibition was enhanced in co-cultures grown at 10 to 15 °C with 1% of added glucose. At 25 °C the inhibition was enhanced even without added glucose. Previously, Janisiewicz et al. [P. syringae inoculated into apple injuries inhibited the growth of E. coli O157:H7. Similar results were found when a high level of ground beef native flora inhibited the growth of E. coli O157:H7 at 10–12 °C [E. coli O157:H7 was suppressed in co-culture with Pseudomonas at 10, 15, and 25 °C. These results are in agreement with the Jameson Effect [E. coli species seems not to be affected by P. fluorescens. Similar results were found by Buchanan and Bagi [P. fluorescens suppressed Listeria monocytogenes growth by inhibiting its maximum population density at low incubation temperatures (4 °C); the inhibition was less evident at higher temperatures (12 and 19 °C). McKellar [P. fluorescens suppressed the growth of E. coli O157:H7 in nutrient broth at 22 °C only when P. fluorescens had reached its maximum population. Similar results were found in a previous study in co-cultures of Listeria spp. with P. fluorescens [E. coli O157:H7 co-cultured with Pseudomonas reached a population of ca. 6 log CFU/mL after ~2.6 d at 10 °C, not achieving a population of 8 log CFU/mL along the study (14 day); the same co-culture reached a maximum of 7 log CFU/mL (~6.1 day) when the TSB broth was supplemented with 1% of glucose. In our study E. coli spp. did not reach 6 or 8 log CFU/mL at 7 °C. Samelis and Sofos [E. coli O157:H7 co-cultured at 15 °C with Pseudomonas reached populations of 6 or 8 log CFU/mL after ~0.5 or 2 day, respectively and when the co-cultures were supplemented with 1% of glucose E. coli O157:H7 achieved populations of 6 or 8 log CFU/mL after 1.2 or 7 day, respectively. We found slightly slower growth in our study at 13 °C with a ttr 6 log of 1.6–2.2 day, and a ttr 8 log of 3.5–3.7 day. These authors [E. coli use of the lactose from the milk.Antagonistic microorganisms  of P. fluorescens at 25 °C were observed. These results are not consistent with the Jameson Effect [Pf and its effect on the maximal population densities of E. coli spp. (Pearson’s coefficient correlation of –0.407). The values of E. coli spp. Nmax were high at all temperatures except at 7 °C (Nmax of about 5.2–5.4 log CFU/mL), so the increasing µPf values did not increased E. coli spp. Nmax together with the increase of the temperatures. It would be possible to consider the fermentation of milk lactose by the E. coli spp. as a “high risk, high reward” strategy in the two-species communities studied [E. coli spp. must engage additional competitive mechanisms to remain viable such as lactose fermentation [P. aeruginosa suffering the attack from Vibrio cholerae or Acinetobacter baylyi’s type VI secretion system (T6SS) respond striking back with its own T6SS [Escherichia coli and Salmonella [E. coli O157:H7 [P. fluorescens bacterial competition against the potato tuber pathogen Pectobacterium atrosepticum. Although the objective of our study is far from the description of a T6SS P. fluorescens activity against E. coli spp., the results provide evidence for a bacterial ‘‘tit-for-tat’’ [In our experiments us sakei  or P. florescens  togethern Effect ,55,65 wi studied ,67. E. centation  althoughown T6SS . The T6Slmonella  includin O157:H7 . Decoin  O157:H7  describeor-tat’’  or “T6SSor-tat’’  evolutioEscherichia coli O157:H7 and Pseudomonas fluorescens co-cultured at 7, 13, 19, and 25 °C in milk. A parametric Bayesian approach was used assuming that the parameters µ (growth rate), k (decline rate), σ (standard deviation of the growth rates), and –σ (standard deviation of the decline rates) are random variables with their own prior distributions. Model results and confidence intervals are based on a probabilistic background. The highest E. coli O157:H7 populations were similar at all temperatures, except at 7 °C: E. coli spp. strains reached their maximal population of 4 log CFU/mL cultured alone, and 5 log CFU/mL co-cultured with P. fluorescens. At 13, 19, and 25 °C E. coli spp. reached their maximal population of 8 log CFU/mL single cultured and co-cultured, with times to reach a population of 6 log CFU/mL after ~48 h at 13 °C or ~24 h at 19 and 25 °C. P. fluorescens achieved its maximal densities of 8–9 log CFU/mL in all cultures at all temperatures, with similar times to reach a population of 6 or 8 log CFU/mL. The results obtained show that the growth rate of P. fluorescens has no direct correlation with its effect on the maximal population of E. coli strains. Modeling the behavior of bacterial communities helps in understanding their dynamics. The inhibition of foodborne pathogens with the use of some species from the natural food microbiota as probiotics may be a tool to improve the safety of refrigerated foods such as milk and dairy products.The aim of this work was to study and model the dynamics of the competition between


Plasmodium knowlesi infections and determine whether other simian malaria parasites are being transmitted to humans, we examined 1,047 blood samples from patients with malaria at Kapit Hospital in Kapit, Malaysia, during June 24, 2013–December 31, 2017. Using nested PCR assays, we found 845 (80.6%) patients had either P. knowlesi monoinfection (n = 815) or co-infection with other Plasmodium species (n = 30). We noted the annual number of these zoonotic infections increased greatly in 2017 (n = 284). We identified 6 patients, 17–65 years of age, with P. cynomolgi and P. knowlesi co-infections, confirmed by phylogenetic analyses of the Plasmodium cytochrome c oxidase subunit 1 gene sequences. P. knowlesi continues to be a public health concern in the Kapit Division of Sarawak, Malaysian Borneo. In addition, another simian malaria parasite, P. cynomolgi, also is an emerging cause of malaria in humans.To monitor the incidence of  Plasmodium spp. were identified in the late 1800s, and >30 species have been described in primates, including humans, apes, and monkeys  and pig-tailed macaques (M. nemestrina) in the area found these species harbor 6 simian malaria parasites: P. inui, P. knowlesi, P. cynomolgi, P. coatneyi, P. fieldi, and P. simiovale , along with P. knowlesi (78%) and P. cynomolgi (56%). Besides P. knowlesi, 2 other simian parasites, P. inui and P. cynomolgi, have zoonotic capabilities that have been proven through accidental and experimental infections . We used nested PCR assays to test for the presence of different Plasmodium species by using primers specific for P. falciparum, P. vivax, P. malariae, P. ovale, P. knowlesi, P. inui, P. cynomolgi, P. fieldi, and P. coatneyi as described previously (https://www.qiagen.com) and used it for molecular characterization of malaria parasites.Malaria was diagnosed at Kapit Hospital by using blood film examination. Approximately 2 mL of venous blood was collected from each patient. Blood spots on filter paper and thick and thin films were prepared, and 500 µL aliquots of blood were stored frozen. We received samples at the Malaria Research Centre, Universiti Malaysia Sarawak, for further analyses. We extracted We obtained written consent from all enrolled patients or their parents or guardians for patients <17 years of age. Ethics clearance for this study was obtained from the medical research ethics committee of Universiti Malaysia Sarawak -03.02/06-Jld.2[24] and UNIMAS/NC-21.02/03-02 Jld.2[19]) and from the medical research and ethics committee of the Ministry of Health Malaysia . P. cynomolgi involved a 3-step PCR rather than the 2-step PCR used for P. knowlesi. First, we amplified the complete mitochondrial genome by PCR using Plasmodium-specific primers Pkmt F1 and Pkmt R1 , 0.3 µmol of each primer (Pkmt F1 and Pkmt R1), 1 U/25 µL KOD FX Neo polymerase (Toyobo), and 3 µL of purified genomic DNA under the following conditions: 98°C for 30 s for first denaturation; 35 cycles at 98°C for 30 s, 55°C for 30 s, and 72°C for 5 min; then a final extension for 10 min at 72°C. We used S.N.A.P UV Gel Purification Kit  to perform gel purification of the PCR amplified mitochondrial DNA (mtDNA) whole genome.Sequencing of the partial cytochrome c oxidase subunit 1 (COXI) gene of P. cynomolgi-specific primers: cox1_F1 (5′-CCAAGCCTCACTTATTGTTAAT-3′), cox1_R1 (5′-ACCAAATAAAGTCATTGTTGATCC-3′), and cox1_R3 (5′-ATGGAAATGAGCAATTACATAG-3′). The heminested 1 (N1) TD-PCR amplification for each sample was performed in an 11-µL reaction mixture containing 1× colorless PCR buffer, 2 mmol MgCl2, 0.2 mmol dNTP mix, 0.25 µmol of each primer (cox1_F1 and cox1_R1), 0.275 U GoTaq G2 Flexi DNA Polymerase , and 0.5 µL of purified mtDNA template under the following conditions: 94°C for 3 min for first denaturation; 10 cycles at 94°C for 30 s, 62°C for 45 s, and 72°C for 85 s; 25 cycles at 94°C for 30 s, 53°C for 45 s, and 72°C for 85 s; then a final extension for 5 min at 72°C. After N1 TD-PCR, we performed a heminested 2 (N2) TD-PCR amplification for each of the PCR products in a 22 µL reaction mixture containing similar concentration of PCR master-mix recipe with cox1_F1 and cox1_R1 primers and 1 µL of purified mtDNA template under the following conditions: 94°C for 3 min for first denaturation, 10 cycles at 94°C for 30 s, 59°C for 45 s, and 72°C for 85 s; 25 cycles at 94°C for 30 s, 53°C for 45 s, and 72°C for 85 s; then a final extension for 5 min at 72°C. We used the S.N.A.P UV Gel Purification Kit (Invitrogen) to gel purify the amplicon.We then used the purified mtDNA amplicons as a template for the heminested touchdown PCR (TD-PCR) with P. knowlesi COXI fragment by using P. knowlesi–specific primers: cox1_Pk_F1 (5′-CATATCCAAGCCTCATTTATGA-3′) and cox1_Pk_R1 (5′-GTGAAAATGAGCAATTACATAA-3′). Amplification was performed in a 22 µL reaction mixture containing similar concentration of PCR master-mix recipe with cox1_F1 and cox1_R1 primers and 1 µL of purified mtDNA template. We used the following thermal cycling parameters: 94°C for 3 min for first denaturation; 10 cycles at 94°C for 30 s, 62°C for 45 s, and 72°C for 85 s; 25 cycles at 94°C for 30 s, 56°C for 45 s, and 72°C for 85 s; then a final extension for 5 min at 72°C. We purified the amplicon as we mentioned in the previous step.We performed TD-PCR amplification of the >3 clones each for P. cynomolgi and P. knowlesi were sequenced and both forward and reverse strands were sequenced for each clone.We cloned the purified COXI fragments into TOPO TA Cloning Kit with pCR2.1-TOPO vector (Invitrogen) according to the manufacturer’s protocol. We purified the recombinant plasmids containing the COXI fragment by using the S.N.A.P. Plasmid DNA MiniPrep Kit (Invitrogen) and sent these to a commercial facility for DNA sequencing. For all samples, https://tracer-1-5.software.informer.com), discarded the first 10% sampling of the MCMC chains as burn-in, and conformed the sample size to >200 for all continuous parameters. We used Tree Annotator to annotate the tree generated by BEAST and visualized the maximum clade credibility tree by using FigTree version 1.3.1 (https://figtree-1-3-1.software.informer.com). We deposited Plasmodium COXI sequences generated in this study in GenBank under accession nos. MN372324–61 . We estimated parasitemia by examining thick blood films and counting parasites associated with <500 leukocytes by 2 independent microscopists. We converted the parasite count to a count per microliter of blood by using the actual leukocyte count of each patient measured with a Sysmex KX-21N Kobe hematology analyzer . We used a light microscope to observe the morphologic characteristics of the parasites in thin blood films.We stained thick and thin blood films with Giemsa by using pH 7.2 buffer and examined films under an Olympus BX53 microscope  were 12–19 years of age, and 24 (3%) were <12 years of age; 540 (51.5%) were female and 507 (48.5%) were male. Six patients  had co-infections with P. knowlesi and P. cynomolgi  had R assays ; Table 1ynomolgi  without P. knowlesi malaria cases, 19% (202) of malaria cases in Kapit Hospital were infected by human Plasmodium species (non–simian Plasmodium parasites). During 2013–2016, 2–3 indigenous infections were reported annually, but no indigenous infections were recorded in 2017. Based on travel history, 95% (192/202) of patients infected with nonsimian Plasmodium species were timber camp workers who had returned to Sarawak after working in Africa, Papua New Guinea, Brazil, and the Solomon Islands.Despite the high number of P. cynomolgi and P. knowlesi coinfections, we obtained Plasmodium DNA sequences of 38 partial COXI genes  from the 6 patients. The Bayesian phylogenetic inference of these DNA sequences showed 20 sequences formed a monophyletic clade with P. cynomolgi referral sequences and was distinct from the P. vivax clade. The remaining 18 sequences, however, formed 2 distinct subclades with the P. knowlesi referral sequences  occurred in the Malaysian Borneo states of Sarawak and Sabah  strain, and the B strain of P. cynomolgi indicated that the incubation period in the liver varied from 15 to 20 days for the B strain and 16 to 37 days for the M strain (P. knowlesi of 9–12 days (P. knowlesi is only 24 hours but is 48 hours for P. cynomolgi (P. knowlesi and P. cynomolgi co-infection occur simultaneously through a single mosquito bite, P. knowlesi parasites would emerge from the liver before P. cynomolgi and replicate rapidly to become the dominant species infecting the person.In the 2 patients whose blood films we examined, P. knowlesi infections, which can be severe and sometimes fatal (P. cynomolgi infections in humans are mild or even asymptomatic (P. cynomolgi had mild infections, and for 1 participant the infection persisted for 58 days (P. cynomolgi after returning home from travels in Southeast Asia (P. cynomolgi and P. knowlesi co-infections in Cambodia (P. cynomolgi monoinfections in Sabah, Malaysian Borneo (P. cynomolgi with P. knowlesi co-infections were symptomatic.Unlike P. cynomolgi in a naturally acquired human host (P. cynomolgi in enlarged and at times distorted erythrocytes with Schüffner’s stippling. We observed erythrocytes with stippling without malaria parasites in 1 of the patients. This phenomenon, termed pitting, was initially described for P. knowlesi in the 1960s during animal experiments (P. falciparum infections in which erythrocytes with ring-infected erythrocyte surface antigen and no intracellular parasites in circulation were observed (–P. cynomolgi parasites might have been pitted, leaving the once-infected erythrocytes circulating in the patient’s blood.A recent study described the morphologic characteristics of young to near-mature trophozoites of P. cynomolgi and P. vivax have been described previously (P. cynomolgi by microscopy. In our study and recent reports of naturally acquired human P. cynomolgi infections in Cambodia, Malaysia, and Southeast Asia, molecular methods were necessary to identify P. cynomolgi (P. cynomolgi monoinfections in our study as P. vivax. Therefore, the incidence of P. cynomolgi in Asia, the natural habitat for several monkey hosts (P. cynomolgi, but also P. knowlesi.The morphologic similarities between P. knowlesi continues to be of public health concern in the Kapit Division of Sarawak, Malaysian Borneo, and that another simian malaria parasite, P. cynomolgi, also is an emerging cause of malaria in humans. Further epidemiologic and entomologic studies using molecular tools and a multidisciplinary approach need to be undertaken in Southeast Asia to determine the incidence of P. cynomolgi and P. knowlesi. Such studies also will assist in determining whether these parasites continue to cause zoonotic infections or whether the loss of natural habitat of macaques, coupled with changes in mosquito composition, abundance, and feeding behavior and an increase in the human population, result in their switch to humans as the preferred hosts.In conclusion, we observed that Appendix. Accession numbers of the cytochrome c oxidase subunit 1 gene sequences of Plasmodium included in the phylogenetic analysis, including those generated in this study.


Fucus vesiculosus is common to the intertidal zones of the Baltic Sea, where it is exposed to high fouling pressures by microorganisms. Our previous studies showed, repeatedly, the consistent antimicrobial activity of F. vesiculosus crude extracts against human pathogens, while untargeted metabolomics analyses have revealed a variety of metabolites. In this study, we applied the UPLC-QToF-MS/MS-based “bioactive molecular networking” (BMN) concept on the most bioactive n-hexane and n-butanol subextracts of Baltic F. vesiculosus coupled with in silico dereplication tools to identify the compounds responsible for antimicrobial activity. The first antimicrobial cluster identified by BMN was galactolipids. Our targeted isolation efforts for this class led to the isolation of six monogalactosyldiacylglycerol (MGDG) derivatives (1–6) and one digalactosyldiacylglycerol . The MGDGs 5 and 6 and the DGDG 7 exhibited activity against Staphylococcus aureus. The second compound class with high bioactivity was phlorotannins. In particular, phlorethol-type phlorotannins showed high correlations with antimicrobial activity based on the BMN approach, and two phlorotannins (8–9) were isolated. This study shows that antimicrobial components of F. vesiculosus reside in the algal cell walls and membranes and that BMN provides a complementary tool for the targeted isolation of bioactive metabolites.The brown alga  Fucus vesiculosus (phylum: Ochrophyta), also known as bladder wrack, is widely distributed and abundant in the intertidal zones of the Northern hemisphere; in particular, in the North and Baltic Sea, Atlantic coasts of Europe and North America. Small populations of bladder wrack are also found on the East Pacific coast of Canada +)—could be dereplicated against the GNPS library (S)-1,2-bis-O-eicosapentaenoyl-3-O-β-d-galactopyranosyl-sn-glycerol (m/z 823.5354 [M + H]+), revealing the predominant constituents of this subextract to be galactolipids, a compound class commonly found in marine algae, including Fucus species. Comprehensive manual dereplication of the UPLC-QToF-MS/MS data of the fractions allowed for the annotation of another 23 nodes in this compound class, in particular 19 monogalactosyldiaclyglycerols (MGDGs), two digalactosyldiaclyglycerols (DGDGs) and three digalactosylmonoaclyglycerols (DGMGs) that all showed different polyunsaturated substitutions of eicosanoic acid, octadecanoic acid, hexadecanoic acid or tetradecanoic acid  were associated with several nodes of the galactolipid cluster , were matched to a small cluster without detected bioactivity (m/z 593.2769 [M + H]+). Two more clusters were putatively annotated to a steroid and a lipid cluster, natural product classes that are expected to be present in a nonpolar n-hexane extract. Nodes of these clusters gave various matches to these compound classes in the in silico database, but no specific compound was identified based on the MS/MS fragmentation data. Furthermore, the characteristic brown algal carotenoid fucoxanthin in its dehydrated form (m/z 641.4215 [M − H2O + H]+) was detected in a separate carotenoid cluster  is used to designate the configuration of the glycerol derivatives, where the carbon that would “appear on top in the Fischer projection shows a vertical carbon chain with the hydroxyl group at C-2 to the left” is assigned as C-1 + that corresponded to the molecular formula C47H72O10Na. HR-MS/MS fragmentation analysis showed two high intensity fragment ions at m/z 517.2783 [M + Na]+ and m/z 543.2935 [M + Na]+ , H-2‴/H-3‴ (J = 9.3 Hz) and H3‴/H4‴ (J = 3.5 Hz) as well as NOE correlations observed from H-1‴ to H-3‴ and H-5‴. HMBC correlations from the anomeric proton H-1‴ (δH 4.28) to C-3 (δC 62.8) showed that the sugar unit was attached at the C-3 position of the glycerol moiety. Furthermore, the glycerol methylene protons H2-1  correlated on the HMBC spectrum with C-1′ (δC 173.5) of the C20:5 chain, while the methylene protons at H-3″ (δH 1.65) correlated with the carbonyl carbon C-1″ (δC 173.3) of the second acyl chain (C18:4) linked at C-2; thus, confirming the regiochemical distribution of the two acyl chains as proposed via MS/MS fragment analysis. HMBC correlations from the methyl protons H3-20′ to C-18′ and from H-18″ to C-16″, revealed that both chains were derived from omega-3 fatty acids where the first double bond is three positions away from the lipid tail end +) indicated the presence of an additional sugar unit. 1H NMR did not provide clear resonances for 7 due to broad signal overlap .Compound  overlap ; thus, iion of 7  containe residue . Ions at at sn-2 . Fragmenl moiety . On the 3/MeOH/H2O (4:1:0.1) mixture. Compounds 1–5 and 7 have previously been isolated from various brown algae and microalgae −), annotated through GNPS , phlorethols (linked with C-O bonds), fuhalols (linked with C-O bonds with an extra hydroxyl group on one unit) or eckol (with dioxane linkage) − as fucodiphloroethol [m/z 993.1359 [M − H]− as pentafucodiphlorethol [m/z 479.0607 [M − H]−) [(m/z 373.0564 [M − H]−) [m/z 497.0717 [M − H]−) [m/z 621.0890 [M − H]−) [m/z 745.1033 [M − H]−) −)  and tripM − H]−)  could beM − H]−)  and hexam/z 477 to 603 [M − H]−  was actually the doubly charge ion of the compound at m/z 957.1215 [M − H]−  as a red-brownish solid from the bioactive fraction B15. Initially, we acquired the NMR spectra for 8 in DMSO-6d that showed aromatic proton signals typical of phenolic compounds , δH 5.83 , δH 5.77 , δH 5.78 , δH 5.71 , δH 5.70 , δH 5.65   and another ion at m/z 461.0498 [M − H]− due to the loss of four phloroglucinol units  . The obsd δC 157 , confirma et al.  observedurements . We atteol units . Conside9, with a pseudomolecular ion at m/z 497.0733 [M − H]− and molecular formula C24H18O12, which was identified as a fucophlorethol based on characteristic MS/MS fragmentation (m/z 265.0803 [M − H]− indicated the loss of one phloroglucinol moiety plus resorcinol (−124–108 Da), m/z 247.0695 [M − H]− originated from the loss of two phloroglucinol moieties, m/z 233.0898 [M − H]− from the loss of a phloroglucinol moiety and an O-phloroglucinol (−124–140 Da), m/z 139.0454 [M − H]− from the residue O-phloroglucinol, and m/z 123.0496 [M − H]− from the residue phloroglucinol. Several other fragment ions that originated from further cross-ring cleavages were also identified   links maiculosus . In our l months , whereasextracts . TherefoF. vesiculosus n-hexane subextract was highly enriched in galactolipids; in particular, MGDGs, DGDGS, and DGMGs. This compound class was not observed in our previous detailed F. vesiculosus metabolome studies, which were performed on crude MeOH and DCM extracts c 0.1, CHCl3); 1H NMR  and 13C NMR , m/z 819.5018 [M + Na]+ C47H72O10Na, calculated 819.5023; deposited in the GNPS spectral library, https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00005723382#%7B%7D(2S)-1,2-bis-O--3-β-d-galactopyranosyl-sn-glycerol/MGDG (18:4/18:4) (2): Colourless oil; [α]c 0.1, CHCl3); 1H NMR , m/z 793.4849 [M + Na]+ C45H70O10Na, calculated for 793.4867; deposited in https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00005723381#%7B%7D(2S)-1-O--2-O--3-β-d-galacto-pyranosyl-sn-glycerol/MGDG (18:3/18:4) (3): Colourless oil; [α]c 0.1, CHCl3); 1H NMR , m/z 795.5009 [M + Na]+ C45H72O10Na, calculated for 795.5023; deposited in the GNPS spectral library, https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00005723380#%7B%7D(2S)-1,2-bis-O--3-β-d-galactopyranosyl-sn-glycerol/MGDG (18:3/18:3) (4): Colourless oil; [α]c 0.1, CHCl3); 1H NMR , m/z 797.5190 [M + Na]+ C45H74O10Na, calculated for 797.5180; deposited in the GNPS library, https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00005723383#%7B%7D(2S)-1-O--2-O--3-β-d-galacto-pyranosyl-sn-glycerol/MGDG (20:5/18:3) (5): 0.5 mg, colourless oil; [α]c 0.1, CHCl3); 1H NMR , m/z 821.5193 [M + Na]+ C47H74O10Na, calculated for 821.5180; deposited in the GNPS spectral library, https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00005723384#%7B%7D(2S)-1-O--2-O--3-β-d-galacto-pyranosyl-sn-glycerol/MGDG (20:4/18:4) (6): Colourless oil; [α]c 0.1, CHCl3); 1H NMR , m/z 821.5180 [M + Na]+ C47H74O10Na, calculated for 821.5180; deposited in the GNPS spectral library, https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00005723385#%7B%7D(27): Colourless oil; [α]c 0.1, CHCl3); 1H NMR , m/z 983.5672 [M + Na]+ C53H84O15Na, calculated for 983.5678; deposited in https://gnps.ucsd.edu/ProteoSAFe/gnpslibraryspectrum.jsp?SpectrumID=CCMSLIB00003134754#%7B%7DDGDG (20:5/18:3) , m/z 957.1215 [M − H]− C48H29O22, calculated for 957.1206.Compound 9: Red-brown solid; (−)-HR-ESIMS found m/z 497.0733 [M − H]− C24H17O12, calculated for 497.0728.Compound F. vesiculosus fractions were analysed by an Acquity UPLC I-Class System coupled to a Xevo G2-XS QTof Mass Spectrometer . Before injection, all samples were dissolved in MeOH and filtered through 0.2 µm PTFE syringe filters . Samples were prepared at a concentration of 0.1 mg/mL and injected (1 µL) onto an Acquity UPLC HSS T3 column  operating at 40 °C. A binary mobile phase system (A: 0.1% formic acid in water UPLC/MS grade and B: 0.1% formic acid in acetonitrile) was pumped at a flow rate of 0.6 mL/min. The following gradients were adapted: n-hexane subfractions—0–7 min linear 70% to 100% B; 11.5–12.5 min 100% B; n-BuOH subfractions—0–11.5 min linear from 1 to 100% B, 11.5–12.5 min 100% B. The last 2.5 min of all runs were used to recondition the column. Total run time was 15 min. An optimised data-dependent acquisition mode was used consisting of a full positive mode MS survey scan covering the mass range between 50–1600 Da at 0.1 s scans, followed by an MS/MS scan of the five most intense ions. For fragmentation, a collision energy ramp with low collision energy of 6–60 eV and high collision energy of 9–80 eV was applied. The spectrometer settings were: capillary voltage: 3.0 kV (positive mode) and 3.5 kV (negative mode), cone voltage: 30 V, source temperature: 150 °C, cone gas flow: 50 L/h, desolvation gas flow: 1200 L/h. Samples were run in triplicate to identify injection errors, then one consensus chromatogram was picked for further bioinformatic analysis.The m/z tolerance 0.01 (or 5 ppm). The chromatogram was deconvoluted with the baseline algorithm . Deisotoping of the chromatogram was achieved by the isotope peak grouper algorithm with m/z tolerance of 0.01 (or 5 ppm) and RT tolerance 0.1 min. All samples were combined in a peak list using the join aligner algorithm; the data were duplicate peak filtered and ions detected in the solvent blanks were removed from the mass list. Only data with MS2 scans were exported as csv and mgf files and uploaded to the GNPS platform for feature-based molecular networking analysis [m/z. MS/MS spectra were window filtered by choosing only the top 6 fragment ions in the +/− 50 Da window throughout the spectrum. For molecular networking of the n-hexane subextract (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=c3414fdee6a94ac7876c86b4539a8d36), the precursor ion mass tolerance was set to 0.02 Da and the MS/MS fragment ion tolerance to 0.02 Da. A molecular network was then created where edges were filtered to have a cosine score above 0.7 and more than 6 matched peaks. While for molecular networking of the n-BuOH subextract (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=c38c712eb5464fcf88b404f0bd437c3f), the precursor ion mass tolerance was set to 0.02 Da and the MS/MS fragment ion tolerance to 0.5 Da. A molecular network was then created where edges were filtered to have a cosine score above 0.65 and more than 4 matched peaks. For both datasets the subsequent parameters were as follows: edges between two nodes were kept in the network if and only if each of the nodes appeared in each other’s respective top 10 most similar nodes. Finally, the maximum size of a molecular family was set to 100, and the lowest scoring edges were removed from molecular families until the molecular family size was below this threshold. The analogue search mode was used by searching against MS/MS spectra with a maximum difference of 100.0 in the precursor ion value. The library spectra were filtered in the same manner as the input data. All matches kept between network spectra and library spectra were required to have a score above 0.7 and at least 6 matched peaks. A publicly accessible R script available at (https://github.com/DorresteinLaboratory/Bioactive_Molecular_Networks/blob/master/Bioactive_Molecular_Networks_v1.1_MZmine2.r) was used to determine a bioactivity score for each ion in the samples. Samples were first scaled by normalizing the intensity of the TIC and then the Pearson correlation score (r) and its significance were calculated between the peak area of an ion and the bioactivity level. The outputted node attribute table was incorporated into the Cytoscape software to visualize the molecular network and to map out the bioactivity scores. The data used for the molecular networking analysis were deposited in the MassIVE Public GNPS database (http://massive.ucsd.edu) under access number MSV000085539.The raw data files were converted to mzXML format using ProteoWizard and then imported to MZmine v2.37 for preprocessing. The mass detection was set to a noise level of 1000 for the MS1 level and 50 for MS2 levels. The chromatogram was built with ions showing a minimum time span of 0.01, minimum height of 2500 and analysis . The dathttps://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=59bc27b00c184d08a577697d2c3bb464# and https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=c871df6c9c0d4421b09607a0ea2d8e00). The data was filtered by removing all MS/MS peaks within +/− 17 Da of the precursor m/z. MS/MS spectra were window filtered by choosing only the top 6 peaks in the +/− 50 Da window throughout the spectrum. The data were then clustered with MS-Cluster with a parent mass tolerance of 0.02 Da and a MS/MS fragment ion tolerance of 0.02 Da to create consensus spectra. Further, consensus spectra that contained less than 2 spectra were discarded. A network was then created where edges were filtered to have a cosine score above 0.7 and more than 3 matched peaks. Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other’s respective top 10 most similar nodes. The spectra in the network were then searched against GNPS spectral libraries. The library spectra were filtered in the same manner as the input data. All matches kept between network spectra and library spectra were required to have a score above 0.7 and at least 3 matched peaks. Subsequently, the clustered data were downloaded. Using an UBUNTU virtual machine, the clustered spectra mgf file and molecular networking attribute out files were then searched against the UNPD-ISDB (available at http://oolonek.github.io/ISDB/) where the library search parameters were adjusted to TOLERANCE = 0.005, SCORE_THRESHOLD = 0.2 and TOP_K_RESULTS = 5. The in silico dereplication results were carefully check for their feasibility, proofing MS/MS fragment spectra and considering biological sources of reported hits.For annotation of the networks, the automated GNPS dereplication workflow in combination with the Universal Natural Product Database (UNPD)-ISDB workflow was employed . Therefohttp://dnp.chemnetbase.com), MarinLit (http://pubs.rsc.org/marinlit/) and DEREP_NP [Furthermore, the chromatograms were manually dereplicated by calculating putative molecular formulae in MassLynx v4.1 and searching them against the Dictionary of Natural Products  of 0.01. To prepare the assay, the samples (20 mg/mL stock solution) were dissolved in medium and transferred into a 96-well microtiter plate and 200 µL of the diluted culture was added to each well. The inoculated microplates were incubated for 5 h at 37 °C and 200 rpm. To detect the inhibitory effect of the substances 10 µL of a resazurin solution  was added to the microplate, incubated again for 5 min and the fluorescence signal (560 nm/590 nm) was measured using the microplate reader . For IC50 determination a dilution series was prepared and the IC50 value was calculated by Microsoft Excel as the concentration that show 50% inhibition of viability on the basis of a negative control (no compound). The resulting values were compared with the positive control (chloramphenicol) and the DMSO control on the same plate.Antimicrobial assays were performed using Methicillin-resistant


The generated AuNPs exhibited SERS activity at 1620 cm−1 in the presence of Vitoria blue B (VBB) molecular probes, and an RRS peak at 290 nm. Based on the AuNP bifunctional probes, the increased SERS and RRS intensities respond linearly with the concentration of CO in the range of 100–1500 ng/mL and 30–5230 ng/mL, respectively. To improve the sensitivity, the produced AuNPs were used as nano-enzyme catalysts for the new indicator reaction of HAuCl4-ethanol (En) to amplify the signal. The sensitive SERS method was coupled with the accurate RRS method to develop a sensitive and accurate SERS/RRS di-mode method for determination of 3.0–413 ng/mL CO, based on the AuNP-HAuCl4-En nanocatalytic reaction and its product of AuNPs as SERS and RRS bifunctional probes.Carbon monoxide (CO) is a commonly poisonous gas. It is important to detect CO in daily life. Herein, a new and sensitive surface enhanced Raman scattering (SERS) and resonance Rayleigh scattering (RRS) coupled di-mode method was developed for CO, based on gold nano-enzyme catalysis and gold nanoprobes. CO can react with HAuCl The ΔI = I − I0 and ΔI290 nm = I290 nm − (I290 nm)0 were calculated.4-ethanol catalytic system:CO-HAuCl In a 5 mL empty test tube, 200 µL pH 5.2 HAc-NaAc buffer, 100 µL 0.2 mg/mL HAuCl4 and 120 µL 26.03 mg/L CO were added, diluted to 0.8 mL with water, and mixed well. After 5 min, a 200 µL 99.7% ethanol, 110 µL 0.01 mol/L HCl and 80 µL 1.0 mg/mL HAuCl4 were added. The tube was heated to 75 °C in a water bath for 20 min, and cooled with ice–water. Then, 57 µL 20 µmol/L VBB and 30 µL 1 mol/L NaCl solutions were added, and diluted to 1.5 mL with water. The SERS at 1620 cm−1 (I) and RRS intensity at 370 nm (I370 nm), and the blank  without of CO were recorded. The ΔI = I − I0, ΔI370 nm = I370 nm − (I370 nm)0 and ΔIDi-mode = ΔI + ΔI370 nm were calculated.2 system:CO-PdCl In a 5 mL empty test tube, 200 µL pH 5.2 HAc-NaAc buffer, 150 µL 0.1 mg/mL PdCl2 and 120 µL 26.03 mg/L CO were added, mixed well, and then diluted to 1.5 mL with water. After 5 min, The RRS spectra were recorded through a fluorescence spectrophotometer. The RRS intensity at 370 nm (I), and the blank (I0) without of CO were recorded. The ΔI = I − I0 were calculated.4-glucose catalytic system:CO-HAuCl In a 5 mL empty test tube, 200 µL pH 5.2 HAc-NaAc buffer, 100 µL 0.2 mg/mL HAuCl4 and 120 µL 26.03 mg/L CO were added, diluted to 0.8 mL with water, and mixed well. After 5 min, 100 µL 0.1 mol/L glucose, 100 µL 0.01 mol/L HCl and 80 µL 1.0 mg/mL HAuCl4 were added. The tube was heated in a 75 °C water bath for 20 min, and cooled with ice–water. Then, 57 µL 20 µmol/L VBB and 30 µL 1 mol/L NaCl solutions were added, and diluted to 1.5 mL with water. The SERS at 1620 cm−1 (I), and the blank (I0) without of CO were recorded. The ΔI = I − I0 were calculated.4-citrate catalytic system:CO-HAuCl In a 5 mL empty test tube, 200 µL pH 5.2 HAc-NaAc buffer, 100 µL 0.2 mg/mL HAuCl4 and 120 µL 26.03 mg/L CO were added, diluted to 0.8 mL with water, and mixed well. After 5 min, 100 µL 0.1 mol/L trisodium citrate, 110 µL 0.01 mol/L HCl and 80 µL 1.0 mg/mL HAuCl4 were added. The tube was heated in a 75 °C water bath for 20 min, and cooled with ice–water. Then, 57 µL 20 µmol/L VBB and 30 µL 1 mol/L NaCl solutions were added, and diluted to 1.5 mL with water. The SERS at 1620 cm−1 (I), and the blank (I0) without of CO were recorded. The ΔI = I − I0 were calculated.The preparation of SEM samples: According to the experimental methods, 1.5 mL reaction solution was taken into a centrifuge tube for 20 min (150 × 100 r/min). Then, the supernatant was removed and diluted to 1.5 mL, with 30 min ultrasonic oscillation. The latter was repeated twice and then 1.5 mL water was added; we then applied 2 μL samples to the silicon wafers for natural drying. After all the steps mentioned above, SEM was performed.The preparation of CO samples: A total of 10 mL absorb liquid (pH 5.2 buffer + HAuCl4) in u-shaped tube, which connected to the atmospheric sampling instrument, sampling for 60 min with the flow velocity of 0.5 L/min, was used. Then, this was transferred into 5 mL colorimetric tubes. There were also some blank experiments being set for CO detection.S) was the sum of RRS and SERS. That is, IS = IRRS + ISERS. In order to achieve this goal, we explored the CO system with SERS and RRS effects.When a beam of light irradiates nanoparticles with molecules, it generates Rayleigh scattering and Raman scattering. Rayleigh scattering is an elastic scattering. Here, it was much greater than that of the Raman scattering intensity of inelastic scattering. That is to say, the inelastic scattering intensity can be ignored when compared with the Rayleigh scattering. When the molecules were adsorbed on the nanoparticle substrate that produced the SERS effect, the intensity was enhanced greatly. The total scattering intensity  have a positive correlation with the concentration of CO. According to this, two simple and rapid SERS and RRS methods can be established for the analysis of CO. Furthermore, the gold nanoparticles have rich free surface electrons that can enhanced the redox electron transfer and exhibit strong catalysis of the ethanol reduction of HAuCl4 to produce gold nanoparticles with SERS activity and RRS effects. It can be deduced from Equation 1 that under the catalysis of the gold nanoparticles (Au)n generated in the first step, HAuCl4 is reduced to Au by ethanol, and finally autocatalytic growth occurs to AuNPs, and the corresponding ethanol is oxidized to acetic acid. Meanwhile, the formed small Au crystal nucleus also catalyzed the redox reaction of ethanol-HAuCl4, which is called autocatalysis. With the increase of CO concentration, the SERS, RRS and both sums increased linearly due to the gold nanoparticles increasing in the catalytic analytical system. Thus, a novel and highly sensitive SERS/RRS coupled di-mode method was established for the determination of trace CO  absorption peak at 548 nm. Along with the increase of the CO concentration, the absorption peak at 548 nm intensity increased slowly  and acetic acid (AA). We also need to pay attention to the catalytic effect of the Au nuclei formed by the redox reaction. That is to say, the autocatalytic reaction also exists in this system. At the same time, although CO reduces Au3+ at a high speed, the reduction ability is weak, so the SERS and RRS sensitivity of the first reaction is very weak. After the nanogold seed is generated through the first-step reaction, the nanogold seed can be used as a catalyst, which greatly improves the growth efficiency of gold nanoparticles. The specific reaction mechanism is shown in We known that Auarticles . The Au34 system have uniform particle sizes. In the TEM inset of 4-En was shown as in As the figure shows A, the go4 concentration on the system ΔI was also researched. When the concentration of HAuCl4 for the redox reaction was 13.3 μg/mL, ΔI was largest, so the concentration of 13.3 μg/mL HAuCl4 was chosen  in this experiment the ΔI could achieve the largest in the condition , generating Au nanoparticles, which exhibit SERS activity and RRS effects. With the increase of CO concentration, both scattering values also increased. Accordingly, two simple and rapid SERS and RRS methods for CO could be established, respectively. Based on the Au nanoparticle catalysis of HAuCl


Apocynum venetum leaves are used as a kind of phytomedicine and the main ingredient in some traditional Chinese medicine products for the relief of colitis. To understand the bioactive constituents of A. venetum L., we did a phytochemistry study and investigated anti-Inflammatory effects of compounds and explored the underlying mechanisms.A. venetum L. leaf and detected the most effective compound by NO inhibition assay. We investigated anti-Inflammatory effects on dextran sulfate sodium (DSS)-induced colitis mice and lipopolysaccharide (LPS)-stimulated RAW264.7 cells. The disease activity index was determined by scores of body weight loss, diarrhea and rectal bleeding; histological damage was analyzed by H&E staining; macrophages change in the colon were analyzed by immunohistochemistry (IHC); myeloperoxidase activity was measured by myeloperoxidase assay kits; levels of proinflammatory cytokines were determined by qPCR and ELISA; protein production such as COX-2, iNOS, STAT3 and ERK1/2 were determined by western blotting.We isolated compounds from ethanol extract of A. venetum L. leaf and found uvaol has excellent potential of inhibiting NO production. We further found uvaol could attenuate disease activity index (DAI), colon shortening, colon injury, and colonic myeloperoxidase activity in DSS-induced colitis mice. Moreover, uvaol significantly reduces mRNA expression and production of pro-inflammatory cytokines  and infiltration of macrophages in colonic tissues of colitis mice. Studies on LPS challenged murine macrophage RAW246.7 cells also revealed that uvaol reduces mRNA expression and production of pro-inflammatory cytokines and mediators. Mechanically, uvaol inhibits the pro-inflammatory ERK/STAT3 axis in both inflamed colonic tissues and macrophages.We isolated uvaol from ethanol extract of A. venetum leaf contains uvaol and uvaol has potent anti-inflammatory effects on DSS-induced experimental colitis and LPS-stimulated RAW264.7 macrophage cells. These results suggest uvaol is a prospective anti-inflammatory agent for colonic inflammation. Inflammatory bowel disease (IBD) is a chronic inflammatory condition of intestinal mucosa, which includes Crohn’s disease (CD) and ulcerative colitis (UC) . EpidemiThe pivotal event of inflammatory response in the colon is the activation of colonic macrophages . As macrApocynum venetum L., a plant of Apocynaceae family, commonly grows in salt-barren zone in China, is a plant with windbreak and sand fixation ability +, 425[M-OH]+, 1H-NMR: 5.13, 3.22, 3.18, 3.53, 0.93, 0.81 , 0.79, 0.95, 0.99, 1.00, 1.10 ; 13C NMR  15.63(C-24), 15.71(C-25),16.77(C-30),17.37(C-26), 18.33(C-6), 21.35(C-29), 23.3(C-11), 23.32(C-27), 23.38(C-16),26(C-15), 27.25(C-2), 28.13(C-23), 30.62(C-21), 32.81(C-7), 35.19(C-22), 36.87(C-10), 38.01(C-17), 38.78(C-1), 39.35(C-20), 39.43(C-19), 40(C-8), 42.04(C-14), 47.65(C-9), 54.01(C-18), 55.15(C-5), 69.95(C-28), 79.03(C-3), 125.04(C-12), 138.6(C-13).Uvaol. Molecular formula: Ci.p.), two uvaol groups . Weight and disease activity index (DAI) scores are measured every 2 days. DAI was determined as described previously [Male C57BL/6 mice aged 6–8 weeks (weighed 18–22 g) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd.  and housed in temperature 25 ± 2 °C environment with a 12 h light/dark cycle. The mice were fed with a standard rodent diet with free access to drinking water to adapt to the new environment for 1 week. Control group was given a normal drinking water and diet, the other mice were given 1.8% (v/w) DSS in drinking water for 1 week, which made the mice suffer from acute colitis. Subsequently, they were randomly divided into four groups: DSS model group, CsA group  for another 24 h, then cell viability was measured by CCK-8 kit.RAW264.7 cells were purchased from the ATCC and cultured with RPMI-1640 containing 10% fetal bovine serum, 100 Units/ml penicillin and 100 µg/ml streptomycin in an incubator at 37 °C in a humidified atmosphere with 5% CO4/well overnight and incubated without or with uvaol (0.25 µM or 0.125 µM) for 1 h, stimulated with LPS at a final concentration of 1 µg/ml for 24 h at 37 °C, 5% CO2 incubator. Supernatants were collected and measured using the Griess reagent following the manufacturer’s instructions.RAW264.7 cells were seeded into 48-well plates at a density of 5 × 10Proteins from RAW264.7 cells and colon tissues were extracted with RIPA buffer  containing protease and phosphatase inhibiter. Protein concentrations were quantified by BCA assay kit . Samples of proteins were separated by 8% or 6% SDS–PAGE gels  and transferred onto polyvinylidene fluoride (PVDF) membranes. After blocked with 3% BSA for 2 h, membranes were incubated with the following primary antibodies for 24 h at 4 °C: β-actin (1:1000), iNOS (1:1000), COX-2 (1:1000), STAT3 (1:1000), pSTAT3 (1:1000), ERK (1:2000) and p-ERK (1:1000). After incubation with HRP-linked anti-rabbit or anti-mouse IgG antibodies (1:2000), protein bands were visualized with Pierce™ ECL western blotting substrate .Colonic tissues were homogenized with lysis buffer to extract total protein and centrifuged at 12000 g at 4 °C for 10 min, and the supernatants were collected for the determination of levels of the cytokines. The concentration of TNF-α, IL-6 and IL-1β in the supernatant of LPS-stimulated RAW264.7 cells and colonic tissues were detected by ELISA kits respectively according to manufacturer’s protocols. The total protein in colon tissues sample was measured by the BCA protein assay kit.−ΔΔCT method. The primer sequences were as follows: β-actin , TNF-α , IL-6: , IL-1β: , MCP-1: .Total RNA of RAW264.7 and colon tissues were extracted by Trizol reagent  and followed by reverse transcriptase  for cDNA. Quantitative PCR was performed by using a Real Time PCR system with SYBR Green Master . Target-gene transcriptions were normalized to β-actin and data were analyzed by 2For the histological examination, distal colon specimens were fixed in 4% formalin for 24 h and embedded in paraffin, stained with hematoxylin and eosin (H&E), and then subjected to blind analysis and scored as previously described [Colonic tissues were fixed in 4.0% buffered paraformaldehyde. Sectioned samples were deparaffinized in xylene and a series of graded alcohol. Sections were further microwaved in citrate buffer (pH 6.0) to repair antigen and quenched endogenous peroxidase with 3% hydrogen peroxide, and blocked with 3.0% bovine serum albumin (BSA) and incubated with mouse anti-rabbit F4/80 antibody  at 4 °C overnight. The sections were washed with PBS (PH 7.4) three times and incubated with HRP-linked goat anti-rabbit IgG antibody . Sections were then washed with PBS, and incubated by DAB substrate kit  and counterstained with hematoxylin. After dehydration with a series of increasingly concentrated ethanol, sections were mounted with neutral gum. The number of macrophages in colonic tissues were quantified with integral optical density (IOD) by Image Pro-Plus v.6.0 software.P < 0.05.All data were analyzed with GraphPad PRISM 6.0  software using one-way ANOVA followed by Bonferroni’s multiple comparisons test. All quantitative values were expressed as the mean ± SEM. Differences were deemed significant when P = 0.0004). However, this decrease was greatly abrogated by treating with uvaol at dosage of 50 and 100 mg/kg respectively. In parallel, ANOVA showed significant differences between all groups on disease activity index (DAI) score . Mice after DSS treatment showed high DAI scores in comparison of normal mice, while both positive agent cyclosporin A (CsA) and uvaol could decrease DAI scores significantly  . Compared with the control group, the colonic MPO activity of DSS model group was significantly increased, whereas the increase was significantly rescued by both uvaol and CsA administration. As well, severe crypt destruction and inflammatory cell infiltration were observed in the histological sections from DSS-treated mice. Both uvaol and CsA could reduce the histopathological manifestation of colitis . Collectively, these present results reveal that 100 mg/kg and 50 mg/kg dosages of uvaol both exert a potent therapeutic effect against DSS-induced colitis in mice.As indicated in Fig. P < 0.0001). Compared with the control group, DSS treatment resulted in a five-fold increase of macrophages in colon tissues and this accumulation of macrophages were significantly suppressed by intraperitoneal administration of uvaol. For mRNA expression of pro-inflammatory mediators in colon tissues of colitic mice, ANOVA divulged significant differences between all groups for MCP-1 , TNF-α , IL-1β  and IL-6  mRNA expression , IL-1β  and IL-6  production in colon tissues of DSS-treated mice. Also, the administration of uvaol significantly decreased protein production of pro-inflammatory cytokines TNF-α, IL-1β and IL-6 in colon tissues of DSS-treated mice . RAW 264.7 cells treated with uvaol at a concentration range of 0.0625 μM to 0.25 μM showed no significant cytotoxicity as compared with control group. For nitric oxide (NO) production, ANOVA revealed significant differences between all groups  , IL-1β , IL-6  and MCP-1 ; as well as protein production of TNF-α , IL-1β  and IL-6 . Notably, these alterations were significantly relieved by uvaol at a concentration of 0.25 μM, but slightly rescued by uvaol at a concentration of 0.125 μM. In parallel, ANOVA also showed significant differences between all groups for protein expression of iNOS  and COX-2 . Protein levels of iNOS and COX-2 were significantly upregulated in response to LPS stimulation, and these can be reversed by treatment of uvaol . After DSS treatment, p-STAT3 expression was significantly increased, while this increase can be reduced by both 100 and 50 mg/kg of uvaol. Further, we next detected the effects of uvaol on ERK and p-ERK expression, and found significant differences between all groups for p-ERK expression  by ANOVA. DSS also elevated p-ERK expression, while both 100 and 50 mg/kg uvaol could significantly suppress this elevation  and p-STAT3 expression , and 0.25 μM uvaol could significantly suppress ERK and STAT3 phosphorylation upon LPS stimulation.As presented in Fig. In the current study, we observed that administration of uvaol attenuate the severity of DSS-induced colitis in mice, as affirmed by reduced clinical manifestations, colon shortening, histological damage and colonic myeloperoxidase activity, which is associated with suppression of colonic macrophage activation via inhibition of ERK/STAT3 signaling pathway.Among the animal models established for studying the pathogenesis of IBD, as well as screening of potential therapeutic interventions, DSS-induced colitis mouse model is one of the most commonly used . This moInnate immune system provides the first line of defense against external insults by triggering strong inflammatory response, while causing infiltration of innate immune cells in the intestine of IBD patients. There are a large number of macrophages in the intestine, as an important part of innate immunity and participates in the pathological process of IBD, which play a vital role in maintaining the intestinal homeostasis , 27. PreIt is eminent that the signal transducers and activators of transcription (STAT) pathways play a central role in the regulation of cytokines and the inflammatory response, especially STAT3 . In actiA. venetum L. need further augmented research. Herein, it can be concluded that uvaol is a promising candidate in treatment of IBD.The present study designates that uvaol is eloquent in alleviating DSS-induced colonic inflammation, which is endorsed by improving the clinical symptoms of colitis, decreasing macrophage infiltration and pro-inflammatory cytokines release in vivo, together with suppressing pro-inflammatory macrophage activation in vitro Fig. . The pro


In this paper, the effect of stochastic loading on tensile damage and fracture of fiber-reinforced ceramic-matrix composites (CMCs) is investigated. A micromechanical constitutive model is developed considering multiple damage mechanisms under tensile loading. The relationship between stochastic stress, tangent modulus, interface debonding and fiber broken is established. The effects of the fiber volume, interface shear stress, interface debonding energy, saturation matrix crack spacing and fiber strength on tensile stress–strain curve, tangent modulus, interface debonding fraction and fiber broken fraction are analyzed. The experimental tensile damage and fracture of unidirectional and 2D SiC/SiC composites subjected to different stochastic loading stress are predicted. When fiber volume increases, the initial composite strain decreases, the initial tangent modulus increases, the transition stress for interface debonding decreases and the initial fiber broken fraction decreases. When fiber strength increases, the initial composite strain and fiber broken fraction decrease. Ceramic-matrix composites (CMCs) have the advantages of high-temperature resistance, corrosion resistance, low density, high specific strength and high specific modulus . The fab(1)Stage I, linear-elastic region.(2)Stage II, matrix cracking and interface debonding region. Matrix micro cracking occurs first and the fiber debonding from the matrix, leading to nonlinear behavior of CMCs.(3)Stage III, saturation matrix cracking region. Matrix cracking approaches saturation with complete debonding of the fiber from the matrix.(4)Stage IV, fiber failure region. The fiber gradually fractures with increasing applied stress.Under tensile loading, multiple damage mechanisms of matrix cracking, interface debonding and fiber failure occur ,5,6,7,8.The composite elastic modulus, proportional limit stress, ultimate tensile strength and the fracture strain can be obtained from the tensile stress–strain curve. For the SiC/SiC composite fabricated using the MI method, the fracture strength and strain of Hi-Nicalon S SiC/SiC composite is much higher than that of Tyranno SA3 SiC/SiC composite; however, the initial elastic modulus and proportional limit stress of Hi-Nicalon S SiC/SiC composite is lower than that of Tyranno SA3 SiC/SiC composite at the same fiber volume of 34.8% and the composite tensile strength increases with the fiber volume . MarshalThe objective of this paper is to investigate the effect of stochastic loading on tensile damage and fracture of fiber-reinforced CMCs for the first time. A micromechanical constitutive model is developed considering multiple damage mechanisms under tensile loading. The relationship between stochastic stress, tangent modulus, interface debonding and fiber broken is established. The effects of fiber volume, interface shear stress, interface debonding energy, saturation matrix crack spacing and fiber strength on tensile stress–strain curve, tangent modulus, interface debonding fraction and fiber broken fraction are analyzed. The experimental tensile damage and fracture of unidirectional and 2D SiC/SiC composites subjected to different stochastic loading stress are predicted.τi is the interface shear stress, rf is the fiber radius, σfo is the fiber axial stress in the interface bonding region, ld is the interface debonding length, lc is the matrix crack spacing and ρ is the shear-lag model parameter.When stochastic stress occurs under tensile loading, matrix cracking, interface debonding, and fiber failure occur. Multiple stochastic loading sequence is shown in ls is the saturation matrix crack spacing, σm is the matrix stress, σR is matrix cracking characteristic stress and m is the matrix Weibull modulus.Using stochastic matrix cracking model, the relationship between stochastic stress and matrix crack spacing is given by Equation (2) .(2)lc=lVf and Vm are the fiber and matrix volume fraction, respectively, Em and Ec are the matrix and composite elastic modulus, respectively, and ζd is the interface debonding energy.The fracture mechanics approach is used to determine the fiber/matrix interface debonding length. The interface debonding length is determined by Equation (3).L> is the average fiber pullout length, and P(Φ) is the fiber failure probability, which is obtained by Equation (5).σc is the fiber characteristic strength, and mf is the fiber Weibull modulus.The Global Load Sharing (GLS) criterion is used to determine the intact fiber stress. The relationship between the fiber intact stress, fiber failure probability and fiber pullout length is given by Equation (4) .(4)σVf=When matrix cracking and interface debonding occur, the composite strain is given by Equation (6).η is the interface debonding fraction, as shown in Equation (8).When fiber failure occurs, the composite strain is given by Equation (7).The tangent modulus is defined by Equation (9).Vf = 0.2, Ef = 350 GPa, Em = 300 GPa, rf = 7.5 μm, αf = 4.0 × 10−6/°C, αm = 4.8 × 10−6/°C, ∆T = −1000 °C, ζd = 0.5 J/m2, τi = 25 MPa and mf = 3.Under stochastic loading, the damages of the matrix cracking, interface debonding and fiber failure occur. The micro stress filed of the damaged CMCs after stochastic loading is given by Equation (1). The fiber axial stress distribution is affected by the stochastic loading stress level, matrix cracking, interface debonding and fiber failure. The stochastic matrix cracking model is used to determine the matrix crack spacing at the applied stress level, as shown in Equation (2), and the fracture mechanics interface debonding criterion is used to determine the interface debonding length, and the GLS criterion is used to determine the load allocation between fracture and intact fibers. The micromechanical constitutive models for the conditions of the matrix cracking, interface debonding and fiber failure are given by Equations (6) and (7). Using the developed micromechanicam constitutive models and damage models, the effects of the fiber volume, interface shear stress, interface debonding energy, matrix crack spacing and fiber strength on tensile stress–strain curve, tangent modulus, interface debonding fraction and fiber broken fraction of SiC/SiC composite subjected to different stochastic loading are analyzed. Li et al.  investigVf = 27.7 and 40% 5], Guo aσs = 140, 180, 200 and 240 MPa at room temperature are shown in Li et al.  investigσs = 140 MPa, the initial strain is ε0 = 0.001%; the initial tangent modulus is Ep = 288 GPa, the degradation rate of tangent modulus is 7% compared with original specimen and the fiber broken fraction is P = 0.006. With increasing stress to σtr = 63.8 MPa, the tangent modulus decreases to Ep = 267.4 GPa corresponding to η = 0.024. When the stress increases from σtr = 63.8 MPa to σ = 140 MPa, the tangent modulus remains constant of Ep = 282.3 GPa with η = 0.025.Under σs = 180 MPa, the initial strain is ε0 = 0.005%; the initial tangent modulus is Ep = 251 GPa, the degradation rate of tangent modulus is 19% compared with original specimen and the fiber broken fraction is P = 0.018. With increasing stress to σtr = 103.4 MPa, the tangent modulus decreases to Ep = 187.4 GPa corresponding to η = 0.122. When the stress increases from σtr = 103.4 MPa to σ = 180 MPa, the tangent modulus remains constant of Ep = 211.3 GPa with η = 0.126.Under σs = 200 MPa, the initial strain is ε0 = 0.009%; the initial tangent modulus is Ep = 229.5 GPa, the degradation rate of tangent modulus is 26% compared with original specimen and the fiber broken fraction is P = 0.028. With increasing stress to σtr = 123.2 MPa, the tangent modulus decreases to Ep = 147.1 GPa corresponding to η = 0.22. When the stress increases from σtr = 123.2 MPa to σ = 200 MPa, the tangent modulus remains constant of Ep = 169.7 GPa with η = 0.226.Under σs = 240 MPa, the initial strain is ε0 = 0.028%; the initial tangent modulus is Ep = 194.2 GPa, the degradation rate of tangent modulus is 38% compared with original specimen and the fiber broken fraction is P = 0.078. With increasing stress to σtr = 162.8 MPa, the tangent modulus decreases to Ep = 94 GPa corresponding to η = 0.49. When the stress increases from σtr = 162.8 MPa to σ = 240 MPa, the tangent modulus remains constant of Ep = 109.8 GPa with η = 0.5.Under Liu  investigσs = 140, 180, 200 and 220 MPa at room temperature are shown in For unidirectional SiC/SiC composite, the tensile stress–strain curves, tangent modulus versus strain curves, interface debonding fraction and broken fiber fraction versus stress curves without stochastic loading and with stochastic loading at σs = 140 MPa, the initial strain is ε0 = 0.002%; the initial tangent modulus is Ep = 235.9 GPa, the degradation rate of tangent modulus is 1% compared with original specimen and the fiber broken fraction is P = 0.01. With increasing stress to σtr = 58.8 MPa, the tangent modulus decreases to Ep = 235.7 GPa corresponding to η = 0.0001. When the stress increases from σtr = 58.8 MPa to σ = 140 MPa, the tangent modulus remains constant of Ep = 235.8 GPa with η = 0.0001.Under σs = 180 MPa, the initial strain is ε0 = 0.009%; the initial tangent modulus is Ep = 234 GPa, the degradation rate of tangent modulus is 1.1% compared with original specimen and the fiber broken fraction is P = 0.03. With increasing stress to σtr = 98.4 MPa, the tangent modulus decreases to Ep = 230.1 GPa corresponding to η = 0.005. When the stress increases from σtr = 98.4 MPa to σ = 180 MPa, the tangent modulus remains constant of Ep = 230.9 GPa with η = 0.005.Under σs = 200 MPa, the initial strain is ε0 = 0.0174%; the initial tangent modulus is Ep = 229.6 GPa, the degradation rate of tangent modulus is 2.8% compared with original specimen and the fiber broken fraction is P = 0.048. With increasing stress to σtr = 118.8 MPa, the tangent modulus decreases to Ep = 215.4 GPa corresponding to η = 0.023. When the stress increases from σtr = 118.8 MPa to σ = 200 MPa, the tangent modulus remains constant of Ep = 217.8 GPa with η = 0.023.Under σs = 220 MPa, the initial strain is ε0 = 0.033%; the initial tangent modulus is Ep = 220.1 GPa, the degradation rate of tangent modulus is 6.8% compared with original specimen and the fiber broken fraction is P = 0.082. With increasing stress to σtr = 139 MPa, the tangent modulus decreases to Ep = 184.5 GPa corresponding to η = 0.07. When the stress increases from σtr = 139 MPa to σ = 220 MPa, the tangent modulus remains constant of Ep = 189.2 GPa with η = 0.071.Under σs = 80, 100 and 120 MPa at room temperature are shown in For 2D SiC/SiC composite, the tensile stress–strain curves, tangent modulus versus strain curves, interface debonding fraction and broken fiber fraction versus stress curves without stochastic loading and with stochastic loading at σs = 80 MPa, the initial strain is ε0 = 0.002%; the initial tangent modulus is Ep = 138.3 GPa, the degradation rate of tangent modulus is 1.2% compared with original specimen and the fiber broken fraction is P = 0.01. With increasing stress to σtr = 13.2 MPa, the tangent modulus decreases to Ep = 137.6 GPa corresponding to η = 0.001. When stress increases from σtr = 13.2 MPa to σ = 80 MPa, the tangent modulus remains constant of Ep = 137.7 GPa with η = 0.002.Under σs = 100 MPa, the initial strain is ε0 = 0.008%; the initial tangent modulus is Ep = 135.6 GPa, the degradation rate of tangent modulus is 3.1% compared with original specimen and the fiber broken fraction is P = 0.027. With increasing stress to σtr = 32.4 MPa, the tangent modulus decreases to Ep = 131 GPa corresponding to η = 0.012. When stress increases from σtr = 32.4 MPa to σ = 100 MPa, the tangent modulus remains constant of Ep = 131.3 GPa with η = 0.013.Under σs = 120 MPa, the initial strain is ε0 = 0.025%; the initial tangent modulus is Ep = 131.9 GPa, the degradation rate of tangent modulus is 5.8% compared with original specimen and the fiber broken fraction is P = 0.068. With increasing stress to σtr = 52.8 MPa, the tangent modulus decreases to Ep = 119.5 GPa corresponding to η = 0.038. When stress increases from σtr = 52.8 MPa to σ = 120 MPa, the tangent modulus remains constant of Ep = 119.5 GPa with η = 0.039.Under σs = 80, 100 and 120 MPa at room temperature are shown in Guo and Kagawa  investigσs = 80 MPa, the initial strain is ε0 = 0.005%; the initial tangent modulus is Ep = 56 GPa, the degradation rate of tangent modulus is 6.7% compared with original specimen and the fiber broken fraction is P = 0.01. With increasing stress to σtr = 33.6 MPa, the tangent modulus decreases to Ep = 49.4 GPa corresponding to η = 0.07. When stress increases from σtr = 33.6 MPa to σ = 80 MPa, the tangent modulus remains constant of Ep = 50.4 GPa with η = 0.074.Under σs = 100 MPa, the initial strain is ε0 = 0.016%; the initial tangent modulus is Ep = 53.5 GPa, the degradation rate of tangent modulus is 11% compared with original specimen and the fiber broken fraction is P = 0.027. With increasing stress to σtr = 52.8 MPa, the tangent modulus decreases to Ep = 39.7 GPa corresponding to η = 0.19. When stress increases from σtr = 52.8 MPa to σ = 100 MPa, the tangent modulus remains constant of Ep = 40.7 GPa with η = 0.2.Under σs = 120 MPa, the initial strain is ε0 = 0.046%; the initial tangent modulus is Ep = 51 GPa, the degradation rate of tangent modulus is 15% compared with original specimen and the fiber broken fraction is P = 0.068. With increasing stress to σtr = 73.2 MPa, the tangent modulus decreases to Ep = 30.8 GPa corresponding to η = 0.39. When stress increases from σtr = 73.2 MPa to σ = 120 MPa, the tangent modulus remains constant of Ep = 31.7 GPa with η = 0.395.Under σs = 180, 220, 260 and 300 MPa are shown in Morscher  investigσs = 180 MPa, the initial strain is ε0 = 0.003%; the initial tangent modulus is Ep = 142.3 GPa, the degradation rate of tangent modulus is 50% compared with original specimen and the fiber broken fraction is P = 0.004. With increasing stress to σtr = 122 MPa, the tangent modulus decreases to Ep = 53.9 GPa corresponding to η = 0.63. When stress increases from σtr = 122 MPa to σ = 180 MPa, the tangent modulus remains constant of Ep = 63.9 GPa with η = 0.638.Under σs = 220 MPa, the initial strain is ε0 = 0.007%; the initial tangent modulus is Ep = 131.4 GPa, the degradation rate of tangent modulus is 54% compared with original specimen and the fiber broken fraction is P = 0.01. With increasing stress to σtr = 162 MPa, the tangent modulus decreases to Ep = 42 GPa corresponding to η = 0.63. When stress increases from σtr = 122 MPa to σ = 180 MPa, the tangent modulus remains constant of Ep = 63.9 GPa with η = 0.97.Under σs = 260 MPa, the initial strain is ε0 = 0.014%; the initial tangent modulus is Ep = 128.7 GPa, the degradation rate of tangent modulus is 55% compared with original specimen and the fiber broken fraction is P = 0.021. With increasing stress to σtr = 150 MPa, the tangent modulus decreases to Ep = 41.3 GPa corresponding to η = 0.93. When stress increases from σtr = 150 MPa to σ = 260 MPa, the tangent modulus remains constant of Ep = 46 GPa with η = 1.0.Under σs = 300 MPa, the initial strain is ε0 = 0.029%; the initial tangent modulus is Ep = 128.3 GPa, the degradation rate of tangent modulus is 55.1% compared with original specimen and the fiber broken fraction is P = 0.04. With increasing stress to σtr = 148 MPa, the tangent modulus decreases to Ep = 41.3 GPa corresponding to η = 0.93. When stress increases from σtr = 148 MPa to σ = 300 MPa, the tangent modulus remains constant of Ep = 46 GPa with η = 1.0.Under In this paper, the effect of stochastic loading on tensile damage and fracture of fiber-reinforced CMCs is investigated. A micromechanical constitutive model is developed considering multiple damage mechanisms under tensile loading. The relationship between stochastic stress, tangent modulus, interface debonding and fiber broken is established. The effects of fiber volume, interface shear stress, interface debonding energy, saturation matrix crack spacing and fiber strength on tensile stress–strain curve, tangent modulus, interface debonding fraction and fiber broken fraction are analyzed. The experimental tensile damage and fracture of unidirectional and 2D SiC/SiC composites subjected to different stochastic loading stress are predicted.(1)When fiber volume increases, the initial composite strain decreases, the initial tangent modulus increases, the transition stress for interface debonding decreases and the initial fiber broken fraction decreases;(2)When the interface shear stress increases, the initial composite strain remains the same, the initial tangent modulus increases, the transition stress for interface debonding remains the same and the initial fiber broken fraction remains the same;(3)When the interface debonding energy increases, the initial composite strain, tangent modulus and broken fiber fraction remain the same and the transition stress for interface debonding decreases;(4)When saturation matrix crack spacing increases, the initial composite strain decreases, the initial tangent modulus increases and the transition stress for interface debonding and initial fiber broken fraction remain the same;(5)When the fiber strength increases, the initial composite strain and fiber broken fraction decrease and the initial tangent composite modulus and transition stress for interface debonding remains the same.