High serum immunoglobulin E (IgE) levels are associated with cardiovascular events. We aimed to evaluate the association between total IgE levels during the first trimester of pregnancy and pregnancy-induced hypertension (PIH) development in a large Japanese cohort. We analysed data pertaining to singleton primipara pregnancies recorded in the Japan Environment and Children’s Study involving births from 2011 to 2014. Serum IgE levels were determined using the immunonephelometric technique. High serum IgE was defined as level ≥ 170 IU/ml. Hypertensive disorders in pregnancy (HDP) were categorized into early onset (Eo) PIH (developed < 34 weeks) or late onset (Lo) PIH (developed ≧ 34 weeks). A multiple logistic regression model was used to estimate the risk of high serum IgE levels on PIH, Eo-PIH, and Lo-PIH. Overall, 32,518 participants were enrolled. The prevalence of total, Eo-, and Lo-PIH was 3.2%, 0.6%, and 2.3%, respectively. Patients with high serum IgE levels had an increased risk of Lo-HDP . No correlation was found with either PIH  or Eo-PIH. High serum IgE levels during the first trimester were associated with the risk of Lo-PIH. Our results could influence and shape further research regarding the pathogenesis of Lo hypertension. HDP is a public health burden, because it is the direct cause of approximately 30,000 maternal deaths per year and accounts for 14% of maternal deaths worldwide3. Pre-eclampsia (PE) and gestational hypertension (GH) are the main forms of HDP, together referred to as pregnancy-induced hypertension (PIH), which are conventionally defined as new onset hypertension after 20 weeks of gestation, with (in cases of PE) or without (in cases of GH) signs of dysfunction in organs including the kidney, liver, and placenta4. PIH can also be categorised into early-onset (Eo)-PIH (onset hypertension before 34 weeks) and late-onset (Lo)-PIH (onset of hypertension after 34 weeks)5. These conditions have different clinical implications. Eo-type but not Lo-type is a high-risk factor for foetal death 6. However, both Eo- and Lo-type result in 10- and twofold increase in perinatal maternal death, respectively, compared with that in pregnant women without newly onset hypertension during pregnancy5. Although there are distinct perinatal differences between Eo-PIH and Lo-PIH, the exact mechanisms leading to Eo- and Lo-PIH remain unknown. The clinical burden of PIH necessitates that the mechanisms underlying the pathophysiology of PIH are elucidated in order to implement preventative strategies.Hypertensive disorders of pregnancy (HDP) occur in approximately 2.5% of all pregnancies in Japan8. One of the best-known mechanisms for mast cell activation is the binding of immunoglobulin E (IgE) to its high-affinity receptor FceR1 on the mast cell surface. After IgE binding, mast cells release histamine, mast cell protease, proteoglycan, cytokines, and chemokines10. Many of these inflammatory mediators are associated with the development of pregnancy-related hypertension14. Therefore, high serum IgE levels during early pregnancy could be associated with later maternal cardiovascular events such as PIH. Nevertheless, little is known about the relationship between serum total IgE levels during the first trimester of pregnancy and the development of PIH.Mast cells are essential components of asthma and allergic responses15.Therefore, the aim of this study was to evaluate the association between serum IgE levels during the first trimester and the development of PIH, including both Eo- and Lo-PIH, using nationally representative data from the Japan Environmental and Children's Study (JECS)In the JECS dataset, the total number of records for infants delivered between 2011 and 2014 was 104,065. Of these, 1994 infants were delivered as a result of multiple gestation, 1222 participants had maternal chronic hypertension, 16,307 participants had insufficient data, and 49,783 were multiparous participants, and they were excluded. As a result, 32,518 participants who were singleton primipara and had no history of hypertension before pregnancy were eligible for enrolment in this study. They were subsequently categorised into two groups according to positive IgE sensitisation (IgE < 170 IU/ml or IgE ≥ 170 IU/ml) . Body mass index (BMI) categories and maternal education levels were significantly different between the participants in the two groups , with the participants in the IgE ≥ 170 IU/ml group having a higher BMI and lower educational level. The ratio of smokers was higher in the ≥ 170 IU/ml group than in the IgE < 170 IU/ml group . There was no significant difference with respect to prevalence of systemic lupus erythematosus (SLE)  between the groups. Regarding the obstetric outcomes, no pertinent differences were found in the development of PIH (p = 0.114), Eo-PIH (p = 0.351), and Lo-PIH (p = 0.058).The maternal characteristics and obstetric factors were summarised according to the serum IgE levels and Table Participants were categorised into one of the three following groups, the non-PIH group (defined as control), Eo-PIH group, or Lo-PIH group. The prevalence of obstetric outcomes in PIH, including pre term birth (PTB), low birth weight (LBW), and small for gestational age (SGA), was compared across the three groups. Table The median (IQR) serum total IgE level between the control, Eo-PIH, and Lo-PIH groups was not significantly different . The proportion of positive serum IgE cases in the control, Eo-PIH, and Lo-PIH groups was not also significantly different .2 and the proportion of women with an education level < 10 years were significantly higher in the Lo-PIH group .The ratio of smokers and those with systemic lupus erythematosus were not significantly different among the three groups . The proportion of women with a body mass index (BMI) > 25.0 kg/mRegarding obstetrics outcomes in PIH, the PTB < 37 weeks, LBW < 2500 g, and SGA were significantly different among the three groups . The prevalence of PTB < 37 weeks, LBW < 2500 g, and SGA was highest in the Eo-PIH group. Overall, the incidence of these outcomes was highest in the Eo-PIH group, followed by the Lo-PIH group, and lowest in the control group.We calculated the adjusted odds ratio (aOR) to estimate the risk of high serum IgE during early trimester on newly onset hypertension during pregnancy. Table In this study, although we assessed data pertaining to maternal background  and analysed the differences between participants with serum IgE levels < 170 IU/ml and IgE ≥ 170 IU/ml, we found no differences regarding the onset of PIH , Eo-PIH, and Lo-PIH between the two groups. We also compared maternal background variables with obstetric outcomes such as PTB, LBW, and SGA in those without PIH and in participants with Eo-PIH and Lo-PIH. Our findings indicated significant differences in adverse obstetrics outcomes and maternal background between the three groups. Multiple logistic regression analyses found that high serum IgE levels during first trimester were a risk factor for development of Lo-PIH.16. Most previous studies that measured serum IgE levels have focused on cardiovascular events such as hypertension or coronary artery disease in the general population18. A study including 156 patients with coronary heart disease showed a significantly higher total IgE concentration in patients with unstable angina and acute myocardial infarction than in those with stable angina or those in the control group19. In another study, 195 patients with any form of ischemic heart disease had significantly higher total IgE levels than those in the control group20. Guo et al. reported that serum IgE levels were significantly higher in patients with multi-vessel disease than in those with single vessel disease, suggesting that a higher serum IgE level was an independent predictor for acquiring multi vessel disease21. On the contrary, a cross sectional study that included 315 women showed no significant positive correlation between increased serum IgE levels and cardiovascular disease22. In this study, we used the cut off for total IgE as 170 for the follow reasons: First, the participants in this study were Japanese women and Japanese commercial medical test companies set the cut off for total IgE as 170 IU/ml. Second, previous Japanese studies have also applied the cut off as 170 IU/ml for total serum IgE levels23. Finally, the value of total serum IgE is not normally distributed and strongly skewed to the right  was higher than that in the LO-PIH group . High IgE levels are associated with microvascular disorders, and maternal microvascular disorders could result in Lo-PIH. Therefore, our results that maternal high serum IgE levels are a risk factor for later newly onset hypertension during pregnancy are biologically plausible.The exact cause of PIH also remains unclear. Eo-PIH is reportedly caused by the failure of the trophoblasts to invade the maternal spiral artery, resulting in a high maternal vascular resistance34. Additionally, we only included primipara cases and had a sufficiently large number of the same in the ethnic population. Nevertheless, this study also has potential limitations. First, although we accounted for some confounding factors in large portions of the questionnaire, unknown factors that could affect the occurrence of PIH might have existed because the pathogenesis of PIH is still to be elucidated. There are unknown factors that may change the present finding that higher maternal serum IgE levels are associated with PIH. Second, although obstetric outcomes were based on medical records, this study focused on PIH, which does not differentiate between GH and PE as we did not have information regarding the severity of hypertension, presence of urine protein, or any organ dysfunction. As PE is accompanied by foetal growth restriction, proteinuria, or other organ dysfunction, the complications of PE more severely affect the neonate than do those of GH. In addition, different pathogenesis may exist among these two phenotypes35. However, there is also evidence that up to 50% of women with GH will eventually develop PE36 and the future risk for maternal cardiovascular events such as chronic hypertension is the same37. Therefore, we thought GH and PE may be undistinguishable in terms of long-term maternal cardiovascular risks.This was the first large-scale study conducted in Japan by the Japanese government with meticulous attention to data collection. Therefore, this study is considered to be representative of the general pregnant population in Japan35, the cut-off value for positive IgE (IU/ml) was defined as 170 IU/ml. The cut-off value of serum IgE levels may have affected the results of the logistic regression analysis. Thus, further studies that examine the relationship between serum IgE levels and the risk of Lo-PIH occurrence, are warranted.Finally, since there are very few previous studies on serum IgE levels during pregnancyOur findings indicate that high serum IgE levels (≥ 170 IU/ml) during the first trimester are associated with the risk of Lo-PIH. The results of this study may provide novel insights into the pathogenesis of new onset hypertension during pregnancy. However, only a few studies have previously measured maternal serum IgE levels, and hence additional studies are needed to confirm or refute our findings.15. Pregnant women were eligible for participation in the JECS: (1) if lived in the study area at the time of the application and expected to live in Japan in the near future; (2) if they had an expected delivery date between 1 August 2011 and mid-2014; and (3) if they were able to participate without difficulty (i.e. they could complete the self-management questionnaires). The JECS protocol was reviewed and approved by the Ministry of the Environment’s Institutional Review Board on Epidemiological Studies and by the Ethics Committees of all participating institutions. The study was conducted in accordance with the Helsinki Declaration and other nationally valid regulations and guidelines. Written informed consent was obtained from all participants.The present study utilised data from the JECS, a government-funded birth cohort study that commenced in January 2011. The JECS investigated the effects of several environmental factors, such as heavy metals and allergens, on children’s healthThe current analysis utilised the JECS data set released in March 2018 (data set: jecs-an-20180131), from which we used the following information: (1) a self-reported questionnaire completed in the first trimester, including details about medical conditions at the time of pregnancy, number of previous deliveries, and smoking status; (2) a self-reported questionnaire completed during the second/third trimester, which included particulars regarding socioeconomic data; (3) obstetric outcomes retrieved from medical record transcripts provided by a co-operating health care provider, and (4) maternal blood sample records, collected during the first trimester.6.Maternal medical conditions at the time of pregnancy include hypertension, thyroid disease, psychological disease, gastro intestinal disease, epilepsy, SLE, cancer, and diabetes mellitus. We excluded a case with hypertension at the time of pregnancy because this study’ focus was new onset of hypertension during pregnancy. We included SLE as a possible confounding factor because it has potential risk of PIH36. In the present study, we excluded cases with insufficient data, multiple pregnancies, hypertension at the time of pregnancy, and multipara.This study only involved primipara women since the risk of PIH is much higher in them compared to multipara women38. A high serum IgE level was defined as IgE ≥ 170 IU/ml based on the results of a Japanese cross-sectional study in pregnant women23. Maternal systolic and diastolic blood pressures were measured at the time of blood sample collection.Blood samples were obtained from the mother during the first trimester of pregnancy. Serum total IgE titres were analysed in a contract clinical laboratory by immunological assays. Maternal serum total IgE titres were assayed by fluorescence immunoassay, using ImmunoCAP 4. PIH was further classified into two categories: Eo-PIH (PIH onset before 34 weeks of gestation) and Lo-PIH (PIH onset after 34 weeks of gestation). SGA was defined as a birth weight less than − 1.5 SDs below the population mean, corrected for gestational age and sex according to the ‘New Japanese neonatal anthropometric charts for gestational age at birth39. PTB was defined as delivery before 37 gestational weeks. LBW was defined as birth weight < 2500 g.Obstetrics outcomes included PIH, gestational age at birth, and birth weight. PIH was defined as new onset hypertension (≥ 140/90 mmHg) after conception21.The following parameters were considered to be confounding factors: maternal age at delivery, BMI before pregnancy, maternal smoking status, maternal educational status, and SLE. The mothers were categorised into four age groups: < 20, 20–29, 30–39, or ≥ 40 years. The maternal BMI before pregnancy was calculated by dividing the mother’s weight (kg) by the square of the mother’s height (m). BMI was categorised into < 18.5, 18.5–25.0, or > 25.0 kg/mA self-reported questionnaire during first trimester had the following options regarding smoking history: ‘Never’, ‘Previously did. Quit prior to current pregnancy’, ‘Previously did. Quit during this pregnancy’, and ‘Currently smoking’. Women who chose ‘Currently smoking’ were considered smokers (smoking category); otherwise, they were considered non-smokers (non-smoking category).1.Based on the Japanese educational system, maternal education was categorised into the following four groups: junior high school: < 10, high school: 10–12, professional school or university: 13–16, and graduate school: ≥ 17 years of education40. The confounding factors in this study were determined based on previously identified risk factors for newly onset hypertension during pregnancy44.Maternal participants were requested to provide the following information regarding SLE: ‘Have you ever been diagnosed with SLE at a medical institution?’. The maternal participants who answered ’Yes’ were classified into the SLE groupT-test and chi-square tests were performed to compare continuous and categorical variables, respectively. To compare more than three variables, the Kruskal–Wallis  and chi-square tests were used. Finally, logistic regression models were used to calculate the aORs and 95% CI for PIH, Eo-PIH, and Lo-PIH. An aOR was calculated after adjusting for maternal age, BMI before pregnancy, maternal smoking during pregnancy, SLE, and maternal education. Logistic regression model analysis was performed using dummy variables for categorical variables composed of more than three categories . SPSS version 26  was used for the statistical analyses. A p-value < 0.05 was deemed to be statistically significant.


Ascophyllum nodosum, Fucus vesiculosus and Undaria pinnatifida were characterised and screened for their inhibitory effects on maltase and sucrase enzymes. Furthermore, enzyme kinetics and the mechanism of inhibition of maltase and sucrase were determined using linear and nonlinear regression methods. All tested extracts showed a dose-dependent inhibitory effect of α-glucosidase with IC50 values ranging from 0⋅26 to 0⋅47 mg/ml for maltase; however, the only extract that was able to inhibit sucrase activity was A. nodosum, with an IC50 value of 0⋅83 mg/ml. The present study demonstrates the mechanisms in which different brown seaweed extracts with varying composition and molecular weight distribution differentially inhibit α-glucosidase activities. The data highlight that all brown seaweed extracts are not equal in the inhibition of carbohydrate digestive enzymes involved in postprandial glycaemia.Type II diabetes is considered the most common metabolic disorder in the developed world and currently affects about one in ten globally. A therapeutic target for the management of type II diabetes is the inhibition of α- glucosidase, an essential enzyme located at the brush border of the small intestinal epithelium. The inhibition of α-glucosidase results in reduced digestion of carbohydrates and a decrease in postprandial blood glucose. Although pharmaceutical synthetic inhibitors are available, these are usually associated with significant gastrointestinal side effects. In the present study, the impact of inhibitors derived from edible brown algae is being investigated and compared for their effect on glycaemic control. Carbohydrate- and polyphenolic-enriched extracts derived from  Type II diabetes is the most common form of diabetes and is usually characterised by the presence of obesity and/or an abnormal increase of postprandial glycaemia, insulin resistance and relative insulin deficiency,3. It is considered that the control of postprandial glycaemia is one of the strategies for the management of type II diabetes, through a reduction in the consumption of foods with high amounts of readily available carbohydrates or through an inhibition of the key enzymes involved in the digestion of carbohydrates,6. In the human gastrointestinal tract, dietary carbohydrates are digested into glucose by six different enzymes: first by salivary and pancreatic α-amylase, also known as α-1,4-endoglucosidases, and then by the mucosal α-glucosidases maltase, sucrase, glucoamylase and isomaltase,8. α-Glucosidases are located on the brush border membrane of the small intestine and form two complexes with different substrate specificities, maltase-glucoamylase and sucrase–isomaltase complexes. Maltase is the major enzyme responsible for digestion and absorption of dietary starch. It hydrolyses the α-1,4-linkages of maltose residues to release a single glucose molecule, whereas sucrase hydrolyses the α-1,2-linkages of sucrose into glucose and fructose. The glucose is then absorbed by the intestinal epithelial cells and then released into blood circulation to be used as an energy source for the human body . Therefore, the inhibition of maltase and sucrase enzymes in the gut can reduce postprandial glucose and help regulate glucose levels in the bloodstream after the ingestion of carbohydrate-rich meal. In type II diabetes, oral antidiabetic drugs such as acarbose, miglitol and voglibose are known for inhibiting α-glucosidase activity; however, some of these treatments come with side effects like abdominal distention and gas accumulation due to the undigested starch and sugar reaching the colon. It has also been reported that long-term use of these drugs might result in more serious side effects, especially in patients with chronic renal failure.The global prevalence of diabetes has doubled during the past 20 years and is currently affecting the health of millions of people,16. Research has been published on the bioactive properties of seaweeds and their extracts for numerous potential applications in human health and nutrition–20. Seaweeds are rich in bioactive compounds in the form of polyphenols, carotenoids, vitamins, phycobilins, phycocyanins and polysaccharides, many of which had been shown to have an effect on glycaemic function–23. For instance, polyphenols and, specifically, phlorotannins present in brown seaweeds exhibited strong α-amylase and α-glucosidase inhibition in vitro. In addition, supplementation with polyphenolic-rich extracts has been reported to be effective for postprandial blood glucose control and significantly reduced fasting blood insulin levels in human subjects. Increased insulin sensitivity in non-diabetic patients after the consumption of a seaweed polyphenol-rich extract has also been reported. Fucoidan is an abundant bioactive sulphated polysaccharide in brown seaweed and has previously been shown to inhibit the starch-digesting enzymes, α-amylase and α-glucosidase. In addition, fucosterol, a sterol found in brown seaweed, also reduced postprandial blood glucose levels and glycogen degradation when administered orally in epinephrine-induced diabetic rats.Thus, alternative products derived from natural sources, such as marine seaweed, have received significant interest in the last few years due to their promising health properties,30. The availability of comparable compositional data would provide an initial understanding on the key biomolecules that are contributing to the efficacy in reducing postprandial hyperglycaemia. To our knowledge, most studies citing the effects of seaweed on the inhibition of α-glucosidases were conducted using artificial substrates such as 4-nitrophenyl-β-d-glucopyranoside (pNPG),32, and no study carried out to date has compared the effects of the composition and structure of seaweed extracts on their ability to inhibit maltase and sucrose activities. In the present study, we investigated the differential inhibitory effects of four brown seaweed extracts from different sources with varying composition and molecular weight distribution on maltase and sucrase activities. In addition, we determined the inhibition mechanisms and the respective inhibition constants for each of these extracts.Despite this growing evidence, identification and selection of the most promising seaweeds and/or extracts is difficult, due to a deficit of comparable data, the use of different experimental models, extraction procedures, extract compositions and bioactive component physicochemical propertiesAschophyllum nodosum and Fucus vesiculosus (AFE); (2) a combination of polyphenols from A. nodosum and F. vesiculosus and chromium (AFCE); (3) a pure seaweed extract from A. nodosum (MANE) and (4) a fucoidan-rich extract from Undaria pinnatifida (UPE). AFE, AFCE and UPE were purchased from online supplement websites, and MANE was provided as a gift by Marigot Ltd.Four seaweed extracts derived from different brown algae species were evaluated in the present study: (1) a polyphenol-rich extract from . Total phenolics were determined using the Folin–Ciocalteu's phenol reagent, according to the method described by Zhang et al.. Total uronic acids were determined using the Blumenkrantz and Asboe-Hansen Method. Fucose, xylose, mannose, galactose, glucose and mannitol content were analysed using high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD). Sulphate content was determined using the BaCl2–gelatin turbidimetry method. Fucoidan content was calculated as the sum of fucose, sulphate and other monosaccharides such as xylose, mannose and galactose according to Rioux and Turgeon.The four brown seaweed extracts were characterised in terms of their polyphenol, fucoidan, uronics, glucose and ash content which were identified as the major components in these extractsMw) distribution of carbohydrates of the four brown seaweed extracts was detected and measured using high-performance size exclusion chromatography with a refraction index detector (HPSEC-RID). The HPSEC Shimadzu system consisted of a system controller CBM-20A, a solvent delivery module LC-20AD, an online degasser DGU-20A5, an autosampler SIL-20ACHT, a refraction index detector (Varian Prostar 350 RID) and an LC workstation. HPSEC analysis was performed using PL aquagel-OH MIXED-H columns . The mobile phase  was used as the isocratic elution at room temperature. The flow rate and injection volume were set to 1 ml/min and 40 μl, respectively. A molecular weight calibration curve was constructed with the retention time values of known dextran standards . For the analysis of the extracts, the measurable range was divided into four segments . An average Mw for each extract within each range was determined, and relative peak area values were calculated using the LCsolution software .The molecular weight . The solution was sonicated in an ice bath for 30 min and then centrifuged at 10 000 g for 20 min at 4°C. The resulting supernatant was used as a source of α-glucosidases for activity and inhibition assays outlined later. Protein concentrations of the enzyme mixtures were determined using the Bradford assay (Bio-Rad).About 300 mg of rat intestinal α-glucosidase (EC 3.2.1.48) acetone powder (Sigma-Aldrich) was dissolved in 10 ml of phosphate buffer (100 md-(+)-maltose monohydrate and sucrose were purchased from Sigma-Aldrich . Maltase and sucrase inhibitory activities were obtained according to the method of Akkarachiyasit et al. with slight modifications. Briefly, the maltase inhibitory activity of the brown seaweed extracts was determined by incubating 100 μl of the extracts at final concentrations  or phosphate buffer  with 50 μl of the diluted enzyme (1 : 30). After pre-incubating the reaction mixture at 37°C, 50 μl of maltose at final concentrations  in phosphate buffer  was added to the mixture and incubated for 30 min at 37°C. For sucrase inhibition, 100 μl of seaweed extracts  or phosphate buffer  were incubated with 50 μl of the diluted enzyme (1 : 5). After pre-incubating the reaction mixture at 37°C, 50 μl of sucrose at final concentrations  in phosphate buffer  was added to the mixture and incubated for 60 min at 37°C. The reaction mixtures were then heated at 100°C for 10 min to stop the reaction and centrifuged at 1000 g at 4°C to precipitate the enzyme. Acarbose was used as a positive control for maltase inhibition at final concentrations of 0⋅0001–0⋅001  mg/ml.After the determination of the released glucose, the inhibitory activity was calculated from the formula as follows:C − T)/C × 100, where C is the enzyme activity without the inhibitor and T is the enzyme activity with the inhibitor.Inhibition (%) = . For the HPAEC–PAD analysis, samples were diluted with deionised water to fall within the calibration range, filtered through a 0⋅2 μM PTFE filter and maintained at 4°C before analysis. Glucose was separated using a CarboPac PA-1 anion-exchange resin column (4⋅6 × 250 mm) connected to a CarboPac PA-1 guard column (4⋅6 × 50 mm)  at 18°C in an isocratic gradient (18 mm NaOH). The chromatography system consisted of an Agilent 1260 Infinity Quaternary LC system  and a Decade II electrochemical detector . The amperometry detector cell contained a gold electrode and a HyREF reference electrode. Glucose was identified by a comparison of the retention time to that of the commercial standard  and quantified by the integration of peak area with the ChemStation software .Quantitative analysis of glucose after the enzymatic reaction was carried out by using two different detection methods: direct HPAEC–PAD or indirect enzymatic measurement with the glucose oxidase method using 50 value was defined as the concentration of each inhibitor to inhibit 50  per cent of the rat maltase and sucrase activities from rat intestinal α-glucosidase. It was determined by linear regression of the log-transformed values of inhibitor concentration (log [I]) v. the relative activity (v/Vmax) at 10 mm maltose, and 25 mm sucrose using GraphPad Prism software methods .The ICm). Inhibition/dissociation constants K (iK or iK′) were determined using Dixon and Cornish-Bowden plots, fitting obtained linear data through Microsoft Office Excel v.2013. iK constants were determined for inhibitors that showed competitive and mixed inhibition using Dixon plots, and the iK′ constants were determined by a Cornish-Bowden plot for inhibitors that showed uncompetitive inhibition, non-competitive inhibition and mixed inhibition. The ratio iK′/iK was used to determine the mechanism of inhibition, because its value established the degree to which the binding of inhibitors changes the affinity of the enzyme for the substrate,39.Enzyme kinetic assays for maltase activity were performed according to the reaction conditions described earlier with varying concentrations of the substrate maltose . Statistically significant differences were analysed with the one-way analysis of variance (ANOVA) by Tukey's HSD test at P ≤ 0⋅05 using Sigma Plot 12.0  and Statgraphics Centurion XVI  software. The application of this parametric test was performed after checking the data normality (Shapiro–Wilk's test) and equal variance assumptions. Principal component analysis (PCA) was performed to establish if a correlation existed between the composition, molecular weight distribution and the maltase activity inhibition (expressed as IC50). The PCA was assessed using XLSTAT software package version 2014.5.03  through the correlation matrix Pearson n−1. The correlation biplot was determined on the basis of the first and second principal components (PCs).All experiments were conducted in triplicate, and results were expressed as mean values ± standard error  with an average Mw of ~800 kDa, representing between 54 and 70  per cent of the total biomolecules detected, respectively. However, MANE and UPE were characterised as containing a significant proportion of lower Mw biomolecules (<50 kDa) with average molecular weights of 13⋅66 and 20⋅61 kDa, respectively. Biomolecules detected in the 1–10 kDa range for MANE were approximately 3-fold more abundant compared with UPE  (50 of 0⋅83 mg/ml. The other extracts and acarbose were not found to inhibit sucrase (The inhibitory effect of the four brown seaweed extracts was assessed separately trations . Signifitrations . The IC53 mg/ml) , while M sucrase .Fig. 1.iK constants were measured using Dixon plots, and the iK′ constants were determined by using a Cornish-Bowden plot (r2) higher than 0⋅85 for substrate concentrations ranging from 1⋅25 to 5 mm. According to the calculated ratio iK′/iK, the four brown seaweed extracts were found to have different mechanisms of action in inhibiting maltase activity. AFE behaved as a competitive inhibitor (iK'/iK = infinite) (iK'/iK = 0) (iK'/iK > 1)  , MANE an/Ki = 0)  and 2(d)/Ki > 1) . Acarbos50 was observed in the statistical Pearson correlation test, with correlation coefficient values of −0⋅850 and −0⋅877, respectively .It is widely accepted that the control of postprandial glycaemia is an effective strategy for the management of type II diabetes,40,41. More importantly, many researchers have reported the important role that these parameters play in the determination of the bioactivity of seaweed. For example, Lordan et al. reported that the extraction solvent ratio affected the composition of the phenolic compounds and consequently the α-glucosidase inhibitory effects of the extracts. The four brown seaweed extracts within the present study were tested for their abilities to inhibit maltase and sucrase enzymatic activities. Results obtained from glucose detected both through HPAEC–PAD and the glucose oxidase method showed that all extracts significantly inhibited maltase activity in a concentration-dependent manner, but only the A. nodosum extract (MANE) was able to inhibit both maltase and sucrase activities. The ability of MANE to inhibit both maltose and sucrose hydrolysis suggests that it may be more robust and effective in the reduction of postprandial hyperglycaemia. The most popular method used to date for the detection of glucose in seaweed extract α-glucosidase inhibition studies is glucose oxidase or hexokinase assays. However, we found that the levels of glucose detected in the enzyme reaction mixture using the glucose oxidase method were much lower compared with HPAEC-PAD (data not shown). Although IC50 values for maltase inhibition were close for both methods, the chromatographic analytical method was more sensitive in detecting glucose in the sucrase inhibition assay. It has been reported previously that polyphenols can have a scavenger effect on the oxidative intermediates generated during the glucose oxidase–peroxidase reaction, which can lead to possible misleading inhibition data and/or an underestimation of glucose results. Therefore, HPAEC–PAD seems to be a more accurate analytical method for the measurement of glucose in α-glucosidase inhibition studies, especially in those natural extracts with a significant amount of polyphenolic and/or antioxidant components.The compositional analysis revealed significant differences in the biomolecule contents of the extracts and their molecular weight distributions. The differences observed were expected since the composition and structure of different seaweeds are known to vary depending on the species, location, season of harvest and extraction method,32, which does not have the ability to discriminate between maltase and sucrase inhibition activities. The amount of sucrose and maltose can vary in an individual's diet, and this variation can impact on the effectiveness of α-glucosidase inhibition on postprandial hyperglycaemia. There is a limited number of recent studies reporting the effects of seaweed/seaweed extracts on maltase or sucrase inhibition. Hwang et al. showed that extracts from the brown seaweed Sargassum hemiphyllum were more efficient at inhibiting maltase (IC50: 0⋅09–2⋅88 mg/ml) than sucrase (IC50: 1⋅89–3⋅47 mg/ml). Other studies have reported that bromophenols extracted from different red seaweed species such as Polyopes lancifolia, Grateloupia and Symphyocladia latiuscula inhibited rat-intestinal maltase (IC50: 1⋅2–5 mm) and sucrase (IC50: 1⋅0–4⋅2 mm)–48. The measured IC50 values for maltase for the four brown seaweed extracts tested in the present study were in the low range (high potency) of the values mentioned previously. The MANE extract displays significant maltase and sucrase inhibition and is one of the most effective reported to date. The observed variation in IC50 reported in the literature could be linked to the use of specific experimental conditions, seaweed species, extraction procedures, extract composition and bioactive component physicochemical properties,49,50.The inhibitory effect of edible seaweeds on α-glucosidase has been previously reported using the artificial substrate pNPGKm value for maltase activity obtained using the Lineweaver–Burk plot was 5⋅8 mm and was found within the reported range, confirming the suitability of the in vitro experimental system,51,52. To better understand how the four brown seaweed extracts inhibited maltase activity, the ratio iK′/iK was used to determine the mechanism of inhibition. When iK′/iK = 1, the inhibitor does not alter the substrate binding to the enzyme, and the model is identical to a non-competitive inhibition. However, when the iK′/iK is infinite, the binding of the inhibitor prevents the binding of the substrate and the model becomes identical to competitive inhibition. When iK′/iK is very small or equal to zero, the binding of the inhibitor enhances substrate binding to the enzyme, and the model becomes nearly identical to an uncompetitive model. Finally, when iK′/iK > 1, the model becomes a mixed inhibition,39. The competitive inhibition mechanism from AFE would suggest that the substrate and the inhibitor cannot bind to the enzyme at the same time, competing simultaneously for access to the enzyme active site. This mechanism could have important implications in an in vivo scenario in the small intestine, as maltose is continuously released from the hydrolysis of dietary starch, and the effect of a competitive inhibitor can be diluted out by increasing amounts of the substrate. However, in the uncompetitive inhibition model (observed for MANE and UPE), the inhibitor binds to the substrate–enzyme complex and the inhibition is independent of the substrate concentration. This reduces the influence of maltose accumulation on the efficacy of these inhibitors as they do not bind to the active site of the enzyme. Therefore, uncompetitive inhibitors have been reported to be more effective in controlling metabolic pathways in vivo than competitive inhibitors, making this mode of action more desirable.The Mw, the number of sulphate groups and linkage position–56. However, very little is known about the effects of the monosaccharide building blocks within the fucoidan polymer structure on α-glucosidase inhibition. In the present study, we found that specific compositional parameters of fucoidan molecules were correlated to maltase inhibitory activity. A higher content of xylose and mannose present in the fucoidan structure was correlated to lower IC50 values. However, the opposite was observed for sulphate and galactose, which increased IC50, and an extract with high amounts of these components would not be a good inhibitor. Our results would also suggest that there is a link between low Mw biomolecules in the tested brown seaweed extracts and high maltase inhibitory activity. This is consistent with the enhanced in vitro effects of low Mw fucoidans from brown seaweeds on inflammatory processes.Fucoidans have been characterised as efficient α-amylase and α-glucosidase inhibitors, and their bioactivity has been reported to vary according to the seaweed species, harvest time, location, composition and physicochemical parameters of these carbohydrates such as A. nodosum (MANE) was the best overall performer in terms of its potential to reduce the glucose released by intestinal enzymes for both starch- and carbohydrate-rich diets. The reported data also provide a better understanding of what is responsible for the inhibitory effects of seaweed extracts on these enzymes. This information should be of value in the evaluation and assessment of the potential use of different seaweed extracts in the control of postprandial hyperglycaemia.In summary, it is evident from the data generated in the present study that there are significant compositional and structural differences between the four brown seaweed extracts studied. These differences appear to have a significant role on the profiles of enzyme activities inhibited and the type of inhibition mechanism. The pure seaweed extract from


MEXCLP) by introducing an upper bound for the busyness of each site. We apply our new mathematical formulation to the case of a local EMS provider. To evaluate the solution quality, we use a discrete event simulation based on anonymized real-world call data. Results of our simulation-optimization approach indicate that the coverage can be improved in most cases by taking site interdependencies into account, leading to an improved ambulance allocation and a faster emergency care.In order to allocate limited resources in emergency medical services (EMS) networks, mathematical models are used to select sites and their capacities. Many existing standard models are based on simplifying assumptions, including site independency and a similar system-wide busyness of ambulances. In practice, when a site is busy, a call is forwarded to another site. Thus, the busyness of each site depends not only on the rate of calls in the surrounding area, but also on interactions with other facilities. If the demand varies across the urban area, assuming an average system-wide server busy fraction may lead to an overestimation of the actual coverage. We show that site interdependencies can be integrated into the well-known Maximum Expected Covering Location Problem ( We propose a new mixed-integer programming model for emergency medical services (EMS) systems incorporating site interdependencies.To evaluate EMS service quality, we develop a simulation model based on a real-life EMS system.Computational studies show increased service quality when modeling site interdependencies.Further, results from a case study reveal improved service quality for a real-world application.In case of medical emergencies, patients require fast and qualified assistance. From a patients point of view, the response time of paramedics depends primarily on the location of the nearest ambulance. These locations are typically fixed on a strategic level and there may be several ambulances at each one. If an ambulance is available at the closest site, that site will answer an incoming emergency call. Otherwise, if all ambulances of that site are busy, the call is transferred to the second closest site in the system and so on. Therefore, the availability of a site has a great influence on the response time of emergency calls. By locating multiple ambulances at one site, the probability of the site being available increases.coverage, defined as the proportion of emergency calls that are reached within a given time threshold. Coverage targets vary across countries and regions [The quality of an EMS system is typically measured using  regions . In our We measure the time threshold from the time an incoming call occurs until the first ambulance arrives at the location of the emergency. In this context, we differentiate between pre-travel delay and travel time. We define the pre-travel delay as the time between the arrival of an incoming call and the departure of an ambulance. The travel time is the time an ambulance needs to reach the location of the emergency after departure from its site. Thus, an optimal distribution of ambulances over the urban area is important for a high quality EMS system.MCLP)) [MEXCLP)) [MALP)) [Due to the practical relevance, there is a rich body of literature on EMS location problems. Standard models maximize, e.g., the coverage )  or the eMEXCLP)) . The bus [MALP)) . These s [MALP)) . These aMEXCLP tends to lead to an overestimation of the expected coverage at the city center and an underestimation in the suburbs. In practice, ambulances in the suburbs less frequently face emergency calls compared to ambulances in the city center. Thus, these less frequented ambulances often serve as a back-up for emergency events that occur in the city center, but need more time to reach the emergency. The result is an inefficient allocation of the limited resources of the EMS system due to the simplifying assumption of a system-wide busy fraction. To improve the standard models for the case of heterogeneous demand regions, a specified system-wide busy fraction must be adhered at every single site. This upper bound for capacity utilization limits the number of assigned emergency calls for each site depending on its number of ambulances. Whenever discussing ’capacity’ in the context of capacitated EMS location models, we follow this interpretation.One central assumption that limits the adequate representation of reality and, thus, is often addressed by these extensions, is the average system-wide server busy fraction. Urban areas are typically characterized by a heterogeneous spatial distribution of emergency calls - the frequency of calls at the city center is higher than in the suburbs. Thus, the busy fraction is different for each ambulance throughout an urban area. If a uniform  busy fraction is assumed, given a similarly sized area of coverage for each site, the actual busy fraction of sites close to the city center tends to be higher, while the busy fraction of suburban sites is lower. This simplifying assumption of the One main simplifying assumption in standard models is the site or ambulance independency, i.e., neglecting the interaction effects between sites or ambulances. However, if the closest site cannot answer an incoming emergency call, the call is transferred within the system with predetermined priorities (levels) as depicted in Fig. MEXCLP accounting for a maximum busy fraction per site by the introduction of capacity constraints. In extensive computational experiments, we show that the consideration of site interdependencies leads to an improved model performance compared to existing standard models. The remainder of this paper is structured as follows: Section This leads to the following research question: How can we model site interdependencies in EMS location models and what is the impact on service quality? To answer this question, we propose a simulation-optimization approach with an innovative mixed-integer linear optimization model based on the Mathematical optimization models are frequently applied to support decision-making in emergency medical services, examples related to staff planning or location problems are presented in , 10. TheMost publications deal with stochastic EMS location problems. This work extends classical EMS location models to consider heterogeneous busyness over the urban area and model site interdependencies. Thus, our review focuses on ambulance location models and probabilistic extensions.MEXCLP) by Daskin [MALP) by ReVelle/Hogan [MEXCLP as, e.g., locating fire and police stations, as well as large-scale emergency and disaster events [MEXCLP and MALP extend the MCLP and include a busy fraction q, i.e., the fraction of time a server is busy with a call and therefore not available to answer an additional call. This is the probability that a call cannot be answered by the server.Since the early 1970s, several deterministic EMS location models have been proposed , 14–16. y Daskin  and the r events , 18. TheMEXCLP maximizes the fraction of demand that is covered within a certain time threshold considering server busyness. Integer variable xi specifies the number of servers at site i. Binary variable yjk denotes whether demand node j is covered by a number of k servers that are positioned at facilities i ∈ Nj that reach node j within the time threshold. The number of calls at demand node j is specified by dj and a total number of V servers is available. The uniform server busy fraction q is obtained by assuming that the system-wide workload is evenly distributed among the V servers. The MEXCLP is then defined as follows [The  follows :1\documk is (1 − q)qk− 1. Objective . Howeveee e.g.,  compare MALP formulation by ReVelle/Hogan [α reliability. Their chance constraint requires that at least one server is available in the system with a probability greater or equal than α. Note that the busy fraction is 1-α. Uncertainty in the EMS context mostly refers to answering emergency calls within a given time threshold while some ambulances may already be busy. Chance constraint approaches are widely used in the context of EMS .Following the idea of these chance constraint approaches, this paper presents a new type of linear capacity constraint that ensures a given busy fraction for each site to be kept. Similar to the chance constraints of ReVelle/Hogan  we stateMEXCLP and MALP models in the EMS context. These extensions include time dependencies , amee e.g., , 32), pree e.g., –35), or ee e.g., –38).MCLP model by Current/Storbeck [CMFLP). In their approach, there is a limit on the number of clients that may be allocated to a server. In a similar manner, Shariat-Mohaymany et al. [Furthermore, some extensions comprise capacity constraints to which our approach belongs. These models allow for allocating additional demand to a site if and only if there is enough capacity, i.e., idle servers, available at that site. One of the first publications addressing this issue was published in 1991 by Pirkul/Schilling  based onStorbeck . They prStorbeck  extend tStorbeck  have prey et al.  propose MECPDD) is a stochastic model that accounts for travel time and server availability uncertainties and balances the busyness among the servers by introducing lower and upper capacity bounds on the servers. Thus, this model formulation also follows the idea of limiting server capacity by a maximum busy fraction. To solve the MECPDD an iterative algorithm is presented based on the hypercube queueing model by Larson [All these approaches neglect some of the system dynamics, e.g., server interdependencies, which could lead to errors in determining the level of coverage. Ansari et al.  propose y Larson . In contMEXCLP with probabilistic coverage and capacity constraints. In this way, our computational experiments can separately quantify the advantage of modeling probabilistic coverage and capacity constraints. Thus, the contribution of the paper is fourfold:Modeling of site interdependencies and capacity utilization,Innovative mathematical formulation which imposes an upper bound on the site busy fraction,Mixed-integer linear optimization model that is solved using commercial solvers,Validation of the formulation in a large computational experiment and case study with anonymized real-world data, showing improved coverage.This paper proposes a new linear optimization model accounting for limited site capacities to ensure a maximum busy fraction. In our simulation-optimization approach, we use a discrete event simulation to evaluate the performance of the model solutions in order to identify the upper bound on the busy fraction that provides the highest coverage. The effect of forwarded calls on the busyness of a site is considered and thus site interdependencies are explicitly modeled under the assumption of statistical server independence. Furthermore, we propose two extensions of the We are not aware of any previous linear mathematical formulations that integrate site interdependencies by introducing a uniform capacity upper bound for each site.In contrast to the idea of Shariat-Mohaymany et al.  that limCMEXCLP). Preliminary considerations focus on the capacitated extension of the MEXCLP taking into account site interdependencies. To avoid redundancies, the notation is adopted from the previous section, unless otherwise stated. A complete overview of the notation can be found in the Appendix.This section presents the proposed Capacitated Maximum Expected Covering Location Problem . On each successive level, the probability that a call is forwarded is the product of the busy fractions of all preceding facilities and the probability that the site is available. The maximum amount of demand that is forwarded from the preceding levels is thus defined by qubl− 1)((1 − qub). Thus, the maximum busy fraction of a site is qub if constraint .The priority a site CMEXCLP extends the MEXCLP by capacity constraints to ensure a maximum busy fraction and to account for site interdependencies as well as for probabilistic coverage.The CMEXCLP . The LMEXCLP represents an extension of the MEXCLP by probabilistic coverage only. We use the LMEXCLP as a benchmark for the CMEXCLP in our computational experiments and case study. We state the mathematical formulation in the Appendix.The CMEXCLP  maximizestraints  ensure tstraints  state thstraints  require straints ). Additistraints ). Finallstraints  is adoptts . Bi. BiCMEXCCMEXCLP, the objective is to maximize the expected number of calls reached within the time threshold. We consider this objective throughout our computational experiments and case study. Nevertheless, we would like to formulate a chance constraint that is a possible extension of the CMEXCLP. Let αj be the minimum probability that a call at demand node j is reached within the time threshold. Then we can state the maximum tolerable failure probability as 1 − αj. The following non-linear constraint limits the failure probability for each demand node j:In the αj. Similar to our capacity constraint, we rely on a uniform upper bound for the site busy probability and integrate probabilistic coverage. We may linearize the constraint in a similar manner to the chance constraint proposed in MALP II by ReVelle/ Hogan [The product of the probabilities that each of the assigned sites does not reach the demand node in time must be lower or equal to the tolerable failure probability. Constraints  ensure te/ Hogan   as aented in  use hypeented in  as they J| = 163 planning squares, each of which is one square kilometer in size. Among these, there are a number of |I| = 50 planning squares, where sites exist or additional sites could be established. Currently, an average of 13 ambulances are on duty, allocated to six ambulance stations. We consider one year of call data as the basis for our experiments. Over the period of a year, there were a total number of about 27,000 time critical emergency calls or on average approximately 3.1 calls per hour. As a model input, the Euclidean distance between planning squares only provides a rough approximation of the real street network distances. Thus, we use the Open Street Map Routing Machine and Nominatim to estimate the actual street distances between each two planning squares [We use anonymized real-world data of an EMS provider for our computational experiments and case study in the next section. We subdivide the city area into | squares . In ordeMEXCLP and LMEXCLP are computed on the basis of the historical average call duration, the historical average number of calls per day and the number of available ambulances using Equation  as described in Section simulated coverage in the following. The highest simulated coverage value then defines the best qub value for each test instance.The busy fraction for the Equation . In the To evaluate the quality of the solutions obtained by the different mathematical formulations for our test instances, we use a Discrete Event Simulation (DES). DES is a common methodology for analyzing the quality of model solutions in the context of EMS . An emerscipy based on the minimum sum of squared errors. To estimate the travel times, the actual travel time as predicted by the Geographic Information System (GIS) software is multiplied by a time dependent travel time correction factor. These time dependent correction factors are the ratio of the historical and the GIS estimated times required for each trip. We then fitted time dependent generalized logistic distribution functions for each hour of the day based on the computed correction factors of all historical trips. On average our fitted distribution function generates a value of 0.9570 with a standard deviation of 0.3707 for the correction factor. This implies that an ambulance on average only needs 95.70% of the travel time that GIS software estimates. However, the high standard deviation implies that the actual travel times highly depend on various known and unknown factors as, e.g., traffic, weather conditions, types of road, or time of day [Furthermore, we simulate uncertainties associated with dynamic pre-travel delays and travel times of the ambulances using distribution functions. There are many different approaches in the literature to account for randomness of travel times . We e of day , 48. ThuIn order to determine the time required to reach the scene of an accident, we add the pre-travel delay to the travel time. Each simulation run then determines the response time for all historical calls over the period of one year. The total call duration, that determines when the ambulance is available after answering a call, is calculated by adding up the stochastic response time and the historical average duration it takes for an ambulance to return to its site. If the rare event occurs that a call cannot be answered by any local ambulance, it is outsourced to another EMS provider and, thus, leaves the considered system. It is a common policy between the municipalities to answer emergency calls of neighboring EMS providers when no local ambulance is available. For detailed information on the frequency of calls arriving when all local ambulances are busy, see Appendix Finally, for a validation of our DES model we compared the historical coverage values with the simulated coverage as obtained by the simulation model for the actual choice of facility and vehicle locations. We compared the coverage values of more than 100 planning squares with at least 30 calls over the period of one year. On average, the simulation would indicate whether a call was covered on time incorrectly for less than 2 emergency calls per planning square per year. Thus, our simulation model provides a suitable representation of the real world EMS system.CMEXCLP, busy fractions from 0.05 to 0.525 were assessed in 0.025 increments. Preliminary experiments have indicated that a more detailed subdivision does not improve coverage results. Furthermore, busy fractions > 0.525 did not improve the solution quality either, as the capacity constraints are non-binding for large values of qub. In order to determine the best upper bound for the site busy fraction, the model solution was evaluated by the DES for each qub value. The highest coverage value calculated by the simulation then defines the best qub value for each instance. As a result, the MEXCLP, LMEXCLP, and CMEXCLP were solved for a total of 42 test instances to evaluate the model performance. Note that the CMEXCLP model had to be solved multiple times per instance. The time limit for all computations was set to 1800 seconds.All computational experiments were performed on a computer with an Intel Core i7-8550 with 8GB of RAM. The models were implemented in Python 3.7 and solved with Gurobi 8.1. Test instances were obtained by varying the number of sites from 4 to 12 and the number of ambulances from 6 to 12 both in single steps. Note that cases with fewer ambulances than sites are not of interest and have been neglected  to solve one instance for a specific qub value for the CMEXCLP model. The time the DES takes to determine the simulated coverage in case of the CMEXCLP is not included, as it is applied after solving all model instances and only requires a few seconds per instance. Interestingly, the runtime of the level based formulation of LMEXCLP is reduced on average by adding capacity constraints given a predetermined busy fraction. The longer runtime of the CMEXCLP stems primarily from the iterative approach for determining the best qub value. However, the runtime strongly depends on the respective scenario. Instances with 6 to 8 possible sites are more difficult to solve because there tend to be more combinations for assigning ambulances and demand nodes to sites. Considering the CMEXCLP formulation, the runtime additionally depends on the selected value for qub. A high value for qub means that the capacity of each site increases, as ambulances are allowed to be called more frequently. Thus, the coverage constraints can be met more easily and the instances are typically easier to solve.To evaluate the solution characteristics of the three mathematical formulations LMEXCLP could not be solved to optimality within the time limit, while the average gap is 0.85%. However, most instances of CMEXCLP were solved to optimality and the average gap is 0.28% across all 840 sub-instances (= 20 ∗ 42) for determining the best qub value for each instance. As a result, the test instances were solved to a reasonable degree of optimality from a practical perspective. The complexity of the LMEXCLP and CMEXCLP as compared to the MEXCLP stems from the combinatorial nature of the multi-level assignments. The MEXCLP could be solved to optimality in all instances.It turns out that some instances of MEXCLP by probabilistic coverage improves the coverage by 1.46%-points (%p) on average, while the maximum improvement amounts to 3.01%p. Adding capacity constraints improves the simulated coverage by a further 0.88%p on average and up to 2.54%p at maximum as reported in Table To assess the solution quality, we compare the level of coverage for the optimal solutions of each of the three mathematical formulations. Therefore, we use the previously described DES to determine the coverage of the network structure prescribed by the different mathematical formulations for each test instance. In our experiments, an emergency call is considered covered if it is reached within a time threshold of 10 minutes, e.g., a coverage of 90% means that 90% of all emergency calls were reached within 10 minutes. The resulting coverage for each instance is calculated as the mean of 30 simulation runs and is shown in Table CMEXCLP with the LMEXCLP formulation, instances with fewer resources, i.e., 4 to 6 sites and 6 to 8 ambulances, improve coverage by 1.58%p on average, which is 0.70%p above the overall average. By contrast, in cases with many resources, i.e., 8 to 12 sites and 10 to 12 ambulances, the coverage is improved only by 0.39%p on average, which is 0.49%p below the overall average. Conversely, an extension of the MEXCLP by probabilistic coverage leads to a higher improvement if more resources are available. Comparing the LMEXCLP with the MEXCLP, an improvement in coverage by 0.38%p is obtained on average for instances with few resources. For instances with many resources, however, the coverage is improved by 2.10%p on average, which is highly above the average improvement of 1.46%p.Figure CMEXCLP is higher in case of limited resources is that the capacity constraints are more frequently binding. With a higher number of ambulances and sites, the capacity constraints are binding less frequently and thus have a lower impact on the solution. The consideration of probabilistic coverage is particularly important when a high number of ambulances is available. This is mainly caused by uncertain travel times leading to a lower coverage than expected by the model. When more ambulances are available, it is possible to counteract the effect of uncertain travel times by locating ambulances out over the urban area. This option is neglected by the MEXCLP because of its binary definition of coverage, whereas coverage is improved using the LMEXCLP by considering a more accurate representation of travel times. In 3 of 42 cases, the LMEXCLP finds the same solution as the CMEXCLP because the capacity constraints are non-binding for the optimal solution of the LMEXCLP. In these cases, there are neither advantages nor disadvantages in introducing the capacity constraints. As a result, our experiment has shown  and the resulting simulated coverage for each planning square in Fig. Sbase and S0/2.To further illustrate the effects of explicitly modeling probabilistic coverage and site interdependencies, we compare the optimal ambulance allocation prescribed by the mathematical models  most sites are located near the city center. Due to the assumption of a system-wide busy fraction, the MEXCLP and LMEXCLP overestimate the coverage of the demand in the city center. Thus, only few ambulances are located at existing sites near the city center while most are allocated in the suburbs. As a result, the CMEXCLP formulation improves the coverage.An interesting observation resulting from this figure is that the explicit consideration of site interdependencies often leads to the same site locations compared to the S0/1) or construction of one site (S1/0) and the relocation of two sites (S0/2) or construction and relocation of each one site (S1/1) lead to very similar simulated coverage results for the LMEXCLP and CMEXCLP formulations. It seems that the positive effect of an additional site is limited by the given number of ambulances. Furthermore, the main benefit appears to result from adding new sites to the network, while some existing sites can be dropped without a large impact on the coverage provided by the network. In the considered scenarios, it is more beneficial to maintain a number of six sites by relocating one or two sites than to construct additional sites. Thus, from a practical point of view, decisions on resources should consider both the number of sites and ambulances. One result that is somewhat counterintuitive is the deterioration in coverage of the MEXCLP in scenarios S0/1, S0/2, and S1/1. It is likely that this reduction is due to the error inherent in the assumption of a system-wide busy fraction combined with a binary coverage formulation. This further illustrates the importance of explicit consideration of probabilistic coverage and site interdependencies in modeling EMS networks.Looking at Fig. MEXCLP model have a significant influence on the network quality. The results of this study show that the explicit consideration of site interdependencies is highly relevant in real EMS systems and leads to improved location solutions, especially in case of limited resources. The CMEXCLP model provides the highest value for simulated coverage in all considered scenarios and is therefore an important tool for planning EMS networks. Further, the CMEXCLP determines a solution that achieves a higher coverage with an identical number of ambulances than the solution of MEXCLP or LMEXCLP, i.e., it attains better performance at the same cost.Overall, both extensions of the CMEXCLP explicitly models site interdependencies by introducing new upper bound chance constraints on the busyness of each site. Furthermore, we model probabilistic coverage leading to a more realistic representation of the EMS system by the mathematical formulations.The new mathematical formulation In extensive computational experiments, we have separately evaluated the effect of modeling probabilistic coverage and site interdependencies. Probabilistic coverage has often improved the solution. However, we have obtained the best solution for each test instance by taking into account site interdependencies. While the size of the effect varies, the solution has been improved in comparison to the other mathematical formulations. The new mathematical model leads to large enhancements of coverage, in particular when there are strongly limited resources. Especially, the assignment of ambulances to sites is often superior to previous formulations due to the more accurate representation of capacities. As a result, site interdependencies are an important feature when optimizing an EMS system with a heterogeneous demand distribution, since their consideration may lead to a substantial improvement of coverage.In our case study, we have demonstrated the value of the new mathematical formulation for a real EMS system. It turns out that two relocations lead to the largest improvement over the base case.CMEXCLP like for example time dependencies. Moreover, the value of the proposed formulation should be investigated by applications to more EMS systems. Futhermore, the possibility of overfitting could be analyzed by taking into account training and testing samples to separate the DES input estimation from the DES model selection that determines the best solution. Finally, specialized solution approaches are required for large instances, especially when chance constraints on the minimum fraction of coverage at each demand node are introduced.Future research should look into further extensions of the


Pseudosciaena crocea, the changes in myofibrillar protein and muscle microstructure during storage were studied with crushed ice as a control. During the storage period, the rate of decrease in myofibrillar protein content, Ca2+‐ATPase activity, and total sulfhydryl groups in the slurry ice group was lower than in the control group (p < .05). There was a significant linear correlation between the hydrophobicity and the storage time (Rcrushed ice (4℃) = 0.9881, Rslurry ice (4℃) = 0.9878, Rslurry ice (−1℃) = 0.9674), and trichloroacetic acid (TCA) soluble peptide content was lower than in the control group at the same time. Slurry ice (−1℃) was optimal in maintaining protein content in P. crocea; the arrangement of myofibrils in P. crocea treated by slurry ice was compact and the gaps were small. Slurry ice can delay the denaturation and degradation of fish myofibrillar protein and maintain its quality.In order to explore the effect of slurry ice on myofibrillar protein of  Pseudosciaenacrocea. There was a linear correlation between the hydrophobicity and the storage time.Slurry ice can delay the denaturation and degradation of fish myofibrillar protein. Slurry ice (−1℃) is the best one to keep protein of  Slurry ice can delay the denaturation and degradation of fish myofibrillar protein.Pseudosciaenacrocea.Slurry ice (−1℃) is the best one to keep protein of There was a linear correlation between the hydrophobicity and the storage time.1Pseudosciaena crocea is an economically important marine fish that is widely cultured in southeast China due to its pleasant taste and high nutritional value  during storage. In this study, slurry ice with fine particles and good fluidity was used as the preservation medium, and the effects on changes in myofibrillar protein and muscle tissue microstructure were investigated in comparison with traditional crushed ice refrigeration.Protein is one of the most important nutritional components of 22.1Pseudosciaena crocea were cultured in the northern shore sea area of Xiangshan Port . After capture, they were aerated and transported to the Marine and Fishery Science and Technology Innovation Base (Ningbo City) for treatment. The mass of each fish was determined to be 500 ± 50 g.Tris‐hydrochloride (Tris‐HCl), ammonium molybdate, anhydrous ethanol, anhydrous sodium sulfite, ammonium persulfate, sodium hydroxide, hydroquinone, and glutaraldehyde were analytically pure and purchased from Sinopharm Chemical Reagent Co., Ltd; sodium dodecyl sulfate, bovine serum albumin, and tyrosine were analytically pure and purchased from Beijing Solaibao Technology Co., Ltd.2.2P. crocea were completely submerged in slurry ice, sealed, and stored in a refrigerator at either 4℃ or −1℃. A control group using broken ice was used to store the P. crocea in a “layer‐ice‐fish” manner and sealed and stored in a refrigerator at 4℃. Freshly fished P. crocea without cold treatment were used as a zero‐day storage sample. This was sampled every other day for 6 days after low‐temperature storage, and subsequently every day. The samples were used for the determination of various protein parameters and the observation of muscle microstructure. Each sample contained three replicates, which were used to determine the mean value.Seawater  was added directly to the insulation bucket, the condensate water and solenoid valve were opened, and the liquid icemaker started to prepare delicate slurry ice with good fluidity. The system temperature was −2.2℃ and the mass ratio of micro‐ice particles and water in the system was 7:3. The 2.3g for 20 min at 4℃. The supernatant was discarded, and this was repeated twice. Subsequently, four volumes of precooling buffer B  were added to the precipitate, homogenized for 2 min, and then incubated at 4℃ for 1 hr. This was then centrifuged at 10,000 ✕ g for 15 min, and the supernatant containing the myofibrillar protein solution was retained. The myofibrillar protein concentration was determined using the biuret method ; ρ is the protein concentration (mg/mL).Sulfhydryl content was determined according to the method of Ellman  with sli2.52 aqueous solution, pH 7.0; 20 mol/L ATP solution) was added to the myofibrillar protein solution and incubated in a water bath at 30℃ for 5 min. Trichloroacetic acid (TCA) solution (1.0 ml of 15% w/v) was then added to stop the reaction. For the blank control, 1.0 ml of 15% (w/v) TCA solution was added immediately before the mixture reaction to denature the protein. After the reaction, 1 ml solution was added to 1 ml molybdic acid sulfate solution, 0.5 ml metal reagent, and 2.5 ml distilled water, mixed, and reacted at 25℃ for 45 min. The absorbance was measured at a wavelength of 640 nm. Ca2+‐ATPase activity was calculated as follows:Solution C  at a wavelength of 660 nm.The content of TCA soluble peptides was determined using the Lowry method, with reference to the method of Morrissey et al., . A standFish meat (3.0 g) was added to 27 ml of 50 g/L TCA, homogenized for 1 min, allowed to stand in an ice bath for 1 hr, and centrifuged at 5,000 × g for 5 min at 4℃. The supernatant (1 ml) was treated as described above in place of the tyrosine solution. The amount of tyrosine released was calculated by referring to the standard curve, and the result was expressed in µmol/g.2.8SEM was performed according to the method of Zhou et al.,  glutaraldehyde for 24 hr, the morphological changes of mycelium were observed by SEM (Hitachi Limited). et al.,  with sli2.90 represents the response from Day 0, and d represents the storage time.The data analysis followed a completely randomized design Kuehl,  in Pytho2+‐ATPase, total sulfhydryl, myofibrillar protein, and surface hydrophobicity. The following treatment conditions were tested: 4℃ crushed ice, 4℃ slurry ice, and −1℃ slurry ice, with three replicates per treatment. Equation , μ is an overall mean, iτ is the effect of temperature, jβ is the effect of ice types, and ijkϵ is the experimental random error. To conduct the pairwise comparison, we used a t test with Bonferroni correction.In this work, we estimated the effect of treatments on five responses; TCA soluble peptides, Ca33.1P. crocea showed a decreasing trend with the extension of storage time , and slurry ice (4℃), and slurry ice (−1℃), respectively; compared with fresh samples, this equates to a decrease of 88.35%, 61.54%, and 44.48%, respectively. The protein content of fish stored in crushed ice (4℃) decreased the fastest, followed by slurry ice (4℃). During the late storage period, the amount of myofibrillar protein content in the slurry ice (4℃ and −1℃) group was 2.63 mg/g and 2.98 mg/g on days 11 and 17, respectively. This shows that slurry ice can slow down myofibrillar protein degradation and has a good refrigeration and preservation effect.It was determined that the myofibrillar protein content of all three treatment groups of e Figure . After 13.2P. crocea was 56.96 mol/105 g. The sulfhydryl content of fish stored on crushed ice (4℃) decreased the fastest to 15.62 mol/105 g on the 7th day, which was 73.0% lower than the fresh fish. The total sulfhydryl content on the 7th day of slurry ice (4℃ and −1℃) was 33.16 mol/105 g and 42.78 mol/105 g, respectively. The results indicate that slurry ice treatment can slow down the decrease of total sulfhydryl content of myofibrillar proteins.Sulfhydryl groups are very active functional groups in the structure of fish protein, which can reflect the denaturation of protein to a certain extent. The total sulfhydryl content of fish meat in all three treatment groups decreased continuously with storage time Figure . The tot3.32+‐ATPase activity was 0.49 µmol/(mg·min)  group, the Ca2+‐ATPase activity decreased rapidly from day 0 to day 8 and decreased by 59.2% on day 8. The rate of decrease slowed from day 8 and reached 0.17 µmol/mg·min on day 11. In the slurry ice (−1℃) group, there was only a 0.21 µmol/mg·min decrease from 0 to 11 days, and Ca2+‐ATPase activity was significantly higher than the control group. Thus, Ca2+‐ATPase activity in the P. crocea was better maintained using slurry ice treatment than broken ice.The activity of ATPase is often used as an indicator to evaluate the protein denaturation of fish meat. The better the protein integrity, the better was the activity. The initial value (0 day) of Ca) Figure . During 3.4Surface hydrophobicity is one of the important structural properties of proteins. It can reflect the relative content of hydrophobic amino acids on the surface of protein molecules, which in turn indicates the degree of protein denaturation  group. From day 0 to 7, the amount of TCA soluble peptides increased rapidly with time in the control fish, and their contents were higher than those in slurry ice (4℃ and −1℃), indicating that the degradation rate of the fish was slower under slurry ice storage conditions, and the lower the storage temperature, the lower the degree of protein degradation.The TCA soluble peptide content increased with the degree in protein degradation during storage. The TCA soluble peptide content of e Figure a. There 3.6P. crocea are arranged in a compact manner, with no voids between the muscle fibers, and they have a flat and smooth surface . In particular, the −1℃ slurry ice had the lowest degradation rate for myofibrillar protein. Treatments also impacted the total sulfhydryl level . In particular, the −1℃ slurry ice has the lowest decay rate for total sulfhydryl content. Furthermore, Ca2+‐ATPase levels were impacted by the treatments . In particular, the −1℃ slurry ice has the lowest decay rate for Ca2+‐ATPase.Storage treatments impacted the myofibrillar protein level (p <.05)  was higher than that between other pairwise comparisons (p <.001). In particular, −1℃ slurry ice has the lowest decay rate for surface hydrophobicity. During the storage period, the surface hydrophobicity of fish protein in the slurry ice (4℃ and −1℃) treatment groups was lower than in the control group (p <.05), and the lower the storage temperature, the lower the hydrophobicity.With regards to surface hydrophobicity, the three treatments had different effects  Figure . Pairwisp <.05; Figure p <.001) was greater than that between the other pairwise comparisons (p <.001), resulting from the several outliers ranging from 0.075 to 0.15 shown in the 4℃ slurry ice group. The −1℃ slurry ice was shown to have the lowest increase in TCA soluble peptides.The different storage conditions impacted the TCA soluble peptide levels , indicating that the low temperature of the slurry ice system and the sealing protection could partially inhibit the enzyme activity and microbial growth and reproduction, slow down the oxidation process, reduce the deformation degree of myofibrillar protein, and thus maintain the freshness of the fish.In this study, the myofibrillar protein content of 5P. crocea (large yellow croaker fish). The results showed that with the extension of storage time, the myofibrillar protein, total sulfhydryl content, and Ca2+‐ATP enzyme activity decreased more than the control group, whereas the surface hydrophobicity was higher than that of the control group. At the same time, the electron microscopy observations found that slurry ice can maintain the integrity of muscle fiber tightness better. Therefore, the −1℃ slurry ice treatment was shown to be best cold storage condition in this study. Slurry ice can maintain the protein characteristics of large yellow croaker, retard quality reduction, and maintain the integrity of the fish tissue.The effects of slurry ice were investigated in the meat of The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Feng Guan: Writing‐original draft (lead). Yirui Chen: Data curation (lead); Visualization (lead). Simin Zhao: Methodology (lead). Zhuo Chen: Writing‐original draft (supporting). Chen Yu: Methodology (supporting). Yongjun Yuan: Funding acquisition (lead); Supervision (lead).