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Identifying inhibitory compounds in lignocellulosic biomass hydrolysates using an exometabolomics approach
BMC Biotechnology volume 14, Article number: 22 (2014)
Inhibitors are formed that reduce the fermentation performance of fermenting yeast during the pretreatment process of lignocellulosic biomass. An exometabolomics approach was applied to systematically identify inhibitors in lignocellulosic biomass hydrolysates.
We studied the composition and fermentability of 24 different biomass hydrolysates. To create diversity, the 24 hydrolysates were prepared from six different biomass types, namely sugar cane bagasse, corn stover, wheat straw, barley straw, willow wood chips and oak sawdust, and with four different pretreatment methods, i.e. dilute acid, mild alkaline, alkaline/peracetic acid and concentrated acid. Their composition and that of fermentation samples generated with these hydrolysates were analyzed with two GC-MS methods. Either ethyl acetate extraction or ethyl chloroformate derivatization was used before conducting GC-MS to prevent sugars are overloaded in the chromatograms, which obscure the detection of less abundant compounds. Using multivariate PLS-2CV and nPLS-2CV data analysis models, potential inhibitors were identified through establishing relationship between fermentability and composition of the hydrolysates. These identified compounds were tested for their effects on the growth of the model yeast, Saccharomyces. cerevisiae CEN.PK 113-7D, confirming that the majority of the identified compounds were indeed inhibitors.
Inhibitory compounds in lignocellulosic biomass hydrolysates were successfully identified using a non-targeted systematic approach: metabolomics. The identified inhibitors include both known ones, such as furfural, HMF and vanillin, and novel inhibitors, namely sorbic acid and phenylacetaldehyde.
Lignocellulosic biomass, like bagasse, wheat straw, and corn stover, is the 2nd generation feedstock for biofuel production. Compared to fossil fuel, it is abundant, renewable and environmental friendly, while compared to 1st generation feedstock, like corn, it does not compete with world food supply [1, 2]. Lignocellulosic biomass is composed of cellulose, hemicellulose and lignin, of which cellulose is the homopolymer of glucose, while hemicellulose is a heteropolymer mainly composed of glucose and xylose [3, 4]. A pretreatment step is required to break down the structure of lignocellulosic biomass and expose cellulose for hydrolysis [5, 6]. The hydrolysis product, so-called biomass hydrolysate, is used as substrate for biofuel production through fermentation processes . During most biomass pretreatment processes, harsh conditions, like high temperature and high pressure, were adopted. This causes sugars and lignin in biomass hydrolysates to degrade, forming products that possess inhibitory effects towards fermenting hosts, thus resulting in reduced growth and productivity [8–11].
Research has been conducted in other laboratories to identify compounds in biomass hydrolysates that cause inhibitory effects [12–14]. For these studies, it was found that inhibitors fall into three categories, weak acids (e.g. acetic acid), furans (e.g. furfural, HMF) and phenolic compounds (e.g. vanillin) [15–17]. A variety of experimental and analytical methods were used in these studies for identifying inhibitory compounds. A common feature of these studies was the approach that was used , namely, the selection of inhibitory compounds to test for their toxicity based on literature research without hydrolysate composition analysis followed by hydrolysate toxicity test towards fermenting microorganisms [11, 19, 20]. Besides the identified inhibitors, evidence was obtained showing that other compounds present in biomass hydrolysates also display inhibitory effects [21, 22]. They were observed as unknown peaks in hydrolysate compositional analysis results, which reduced in size after detoxification . In this study a non-targeted exometabolomics approach was applied to identify novel inhibitory compounds in biomass hydrolysates. Generally, metabolomics is one of the ‘omics’ tools that studies the performance of research objects by analyzing their overall compositions [24, 25]. In this study, research objects are lignocellulosic biomass hydrolysates, which are used as fermentation media for bioethanol production. The performance of biomass hydrolysates as fermentation media vary due to the difference in their compositions, i.e. inhibitory compounds and their concentrations. By establishing the relationship between composition and yeast performance in different biomass hydrolysates statistically, compounds that possess inhibitory effect could be indicated in an unbiased way (Figure 1).
In metabolomics, the search for important metabolites responsible for a certain response, e.g. fermentability, is often performed with multivariate data analysis methods [26, 27]. These multivariate methods are able to search for the interactions between metabolites that are responsible for the response that is modeled. Partial least squares (PLS) is a multivariate data analysis method that is commonly used in metabolomics to search for the important metabolites . As an extension of the PLS method, also n-way PLS may be used when the data-set consists of a time series as in the case of our metabolomics experiments. Rigorous validation of these models is necessary as multivariate data analysis methods may lead to false positive correlations [29, 30]. Therefore, we decided to use double cross validation (2CV) to obtain unbiased prediction errors for the (n)PLS models [31, 32].
We report here the detailed procedure and the results of using an exometabolomics approach for identifying inhibitory compounds in lignocellulosic biomass hydrolysates. This includes the batch fermentability of 24 different biomass hydrolysates using baker’s yeast, S. cerevisiae CEN.PK113-7D, and the analysis results of the fermentation samples by two GC-MS methods; the statistical model building procedure for identifying potential inhibitory compounds, and the toxicity testing results of the identified potential inhibitors. The results of this study show that of the potential inhibitory compounds indicated by the statistical models, a large fraction indeed exhibited inhibitory effects on the growth of fermenting yeast. These compounds consist of both known inhibitors, such as furfural and HMF, and novel inhibitors.
Biomass hydrolysates preparation
To successfully identify inhibitory compounds in biomass hydrolysates with statistical models, acquiring hydrolysates with diverse performance is of importance . 24 different hydrolysates were prepared from six different biomass and by using four hydrolysate preparation methods to achieve this (see Section Biomass hydrolysate preparation and fermentation). Among the six biomass, wheat straw, barley straw and corn stover are agricultural wastes, bagasse is a sugar industry byproduct, and willow and oak are wood products. Each of the six biomass was pretreated with four different methods, which used 2% sulfuric acid, 72% sulfuric acid, lime, and peracetic acid, respectively. The resulting 24 hydrolysates were tested for their performance as fermentation media on a small scale (ml), showing that there was a significant diversity among these 24 hydrolysates . These hydrolysates were prepared in larger quantity (l) for the exometabolomics study. A batch fermentation of 1 l working volume was carried out for each hydrolysate based on previously developed procedures (see Section Biomass hydrolysate preparation and fermentation and ).
Identical batch fermentations were carried out for each of the 24 different hydrolysates generated. The fermentability was monitored by measuring OD600 (refer to as ‘OD’ in the following text), glucose and ethanol concentrations of the samples taken with a fixed time interval. To quantify the fermentability of the hydrolysates, four phenotypes were defined, which are lag-phase, glucose consumption rate (Glu CR), ethanol production rate (EtOH PR) and ethanol yield (EtOH Y). The definition of these four phenotypes are given in Equation 1 to 4 (Eq1 to Eq4), and the measurement results of the fermentation samples were used to calculate these phenotypes.
As shown in the phenotype definitions, lag-phase has time as unit (Eq1), which represents the duration before growth began. Since during lag-phase, the fermenting yeast adapt to the media composition for growth , a longer lag-phase indicates the presence of compounds that delay growth. Glucose consumption rate (Glu CR) is an indicator of the growth rate of the fermenting yeast, while ethanol production rate (EtOH PR) and ethanol yield (EtOH Y) describe the productivity of the fermenting yeast in a specific hydrolysate. For each of the 24 fermentations, these four phenotypes were calculated (Table 1). It should be mentioned that growth rate is one of the most commonly used phenotypes describing the performance of fermenting hosts. In this study, instead of using growth rate, we chose Glu CR to describe growth. This is because OD measurement results were not easily comparable due to sample characteristics, such as color differences among hydrolysates, and flocculation. To confirm that Glu CR is a good indicator of growth rate, we also measured growth rate based on OD development for some samples (Table 1). It can be seen that Glu CR has very similar trends as the OD based growth rate (Additional file 1). Since glucose measurements are more accurate than OD, we have decided to use Glu CR as an indicator of growth rate.
As shown in Table 1, all 24 hydrolysates had different glucose concentrations, indicating that biomass type as well as pretreatment method influenced the biomass hydrolysis efficiency. In general, mild alkaline (MA) pretreated biomass resulted in relatively low glucose concentration, while concentrated acid (CA) lead to higher hydrolysis efficiency . However, based on our previous results, glucose concentration in the range observed in Table 1 had no influence on fermentation performance (results not shown).
The performance of the 24 hydrolysates varied significantly as fermentation media, which was consistent with the screening experiments on milliliter scale . As far as lag-phase is considered, hydrolysates like Oak-CA and Willow-CA supported growth almost immediately after inoculation, while the fermenting yeast needed an adaptation period of as long as 10 hours in CS-CA and WS-CA hydrolysates. The Glu CR of the 24 hydrolysates ranged from 0.80 (Oak-CA) to 4.63 (WS-CA), which was comparable to that of EtOH PR (Additional file 2). This resulted in very similar ethanol yield among the hydrolysates, around 0.4 g/g (Table 1), which was also the ethanol yield of S.cerevisiae CEN.PK113-7D in mineral medium with 20 g/l glucose . This observation suggested that under anaerobic conditions, the effect of inhibitory compounds in hydrolysates had little effect on the ethanol yield of the fermenting yeast. Therefore, this phenotype was not used in building statistical models for the purpose of identifying hydrolysate inhibitors.
Some had similar performance in terms of the calculated phenotypes among the 24 hydrolysate fermentations (Additional file 2). Since the statistical models to be used for analyzing the relationship between fermentability and sample composition were based on linear regression, it is important to reduce overrepresentation of certain phenotype classes. In addition, it is also beneficial to minimize the amount of samples for exometabolomics analysis. Therefore, from the 24 fermentations, 16 were selected based on the variations in their phenotypes, biomass type and pretreatment method. The selected 16 hydrolysates contain all six biomass types and all four biomass pretreatment methods (Table 1), and the fermentability of these selected hydrolysates show a more or less even spread of the fermentation phenotypes (Additional file 2).
Hydrolysate fermentation sample analysis
After quantifying the performance of the hydrolysate fermentations with the four phenotypes, cell free time-point samples of the 16 selected fermentations were analyzed for their overall compositions. These samples were chosen based on the criteria that they should uniquely represent the whole fermentation process. The five fermentation time-point samples are listed in Table 2, which selection was based on the three fermentation phases, namely lag-phase, growth-phase and stationary-phase. The division of the three fermentation phases was consistent with the definition of the phenotypes, i.e. the end of lag-phase is when OD reaches 2% of the maximum OD, the end of growth-phase is when glucose consumption is completed, and the duration of stationary-phase is fixed at 10 hours after the end of growth phase. In this way, a total of 80 samples from 16 hydrolysate fermentations were selected for compositional analysis. Overall fermentation durations were within a 20 to 70 hours timeframe.
The focus of the compositional analysis was to identify potential inhibitory compounds in hydrolysate samples, which are believed to be mainly non-sugar compounds, such as weak acids, furans and phenols [8, 9, 15]. GC-MS was chosen as the analytical tool, as the method is capable of detecting a wide range of these compounds, including many unknowns [20, 36]. A crucial point in analyzing hydrolysate samples with GC-MS was to remove sugars, which are present in large quantities in the samples and severely interfere with the detection of non-sugar compounds [13, 37]. For this purpose, two sample work-up methods were developed, using ethyl acetate extraction orethyl chloroformate derivatization.
Ethyl acetate extraction GC-MS (EA-GC-MS) was previously described by Heer et al. . The method uses ethyl acetate (EA) as solvent, in which compounds that are apolar, e.g. with aromatic rings, are dissolved, while polar compounds, like sugars, remain in the water phase. In current study, the hydrolysate samples were extracted twice with EA to allow adequate recovery of the extracts. After removing EA phase from the water phase, it was dried through evaporation, thus concentrated and ready for analysis with GC-MS.
Only compounds dissolvable in EA were analyzed due to the nature of this sample work-up method, and since EA was removed through evaporation, volatile compounds were partially lost. It was found that sample pH influences the extraction, when pH was raised to above 6.0, significant decrease of recovery was observed with multiple aromatic standards. Therefore, all hydrolysate samples were extracted with EA at pH 5.0. In doing so, the recovery of aromatic compounds was satisfying, above 90%, while the recovery of furans was rather low and inconsistent due to evaporation. So the analysis results of aromatic compounds were considered more reliable than furans.
Ethyl chloroformate derivatization GC-MS (EC-GC-MS) was developed in our lab to complement the EA-GC-MS method. Ethyl chloroformate (EC) was used to convert acids to their ethyl ester form, thus compounds like carboxylic acids, amino acids, aromatic compounds and furans could be detected by MS. EC-GC-MS therefore has a larger coverage of compounds compared to EA-GC-MS, and is easier to operate. But due to the diverse reactivity of compounds with EC, it is possible that compounds present with high concentration could only be detected with low signal. The involvement of a derivatization step could also cause a single compound to have more than one derivatization product, which complicates the characterization of the compound. EC-GC-MS method not only complemented EA-GC-MS by detecting small carboxylic acids and furans, but also overlapped with EA-GC-MS by detecting aromatic compounds. As far as aromatic compounds are concerned, it seems that the results of EA-GC-MS were more reliable due to the reactivity diversity issue occurring in EC-GC-MS.
After analyzing all 80 samples with both methods, a ‘compound list’ was generated for each method by listing all peaks clearly visible in the chromatograms. Identities were assigned to some of the peaks by comparing the mass spectra of these peaks with the existing GC-MS compound library in our lab. With the EA-GC-MS method, in total 129 peaks were detected, among which 44 were identified; while with EC-GC-MS, there were 114 detected peaks, of which 56 were identified. The majority of the compounds detected by EC-GC-MS method were acids, including carboxylic acids, such as levulinic acid and succinic acid, phenolic acids, like phenylacetic acid and syringic acid, and 18 amino acids (Additional file 3). EA-GC-MS mainly detected phenolic compounds, containing phenolic aldehydes, alcohols and acids (Additional file 3).
Pseudo-quantities were assigned to all detected peaks from both identified compounds and unknowns by integrating their peak areas. Internal standards were measured in both blank and hydrolysate sample to correct for sample matrix effect. The peak area difference between blank and hydrolysate sample of the internal standards was calculated as a correction-factor, and was used to correct all the integrated peak areas of the same hydrolysate type. Thus, compound lists based on corrected peak areas were formed for both analytical methods.
Statistical model building
To identify inhibitory compounds in biomass hydrolysates, relationship between hydrolysate fermentability and fermentation sample composition was studied by building statistical models. The models used in this study were partial least square (PLS) and n-way PLS (nPLS), validated by conducting double cross validation (2CV), which was done by leave-one-out in the inner and outer loop [31, 38]. The purpose of both models was to point to compounds that are most responsible for a certain fermentation phenotype. This was done by predicting the phenotypes using the data-sets formed through analyzing fermentation samples with the two GC-MS methods (see Section Hydrolysate fermentation sample analysis and Additional file 3).
Lag-phase is the period before growth takes place in a fermentation process (Eq1), it is mainly influenced by the initial media composition. During lag-phase, fermenting yeast adapts to hydrolysate through adjusting its composition by either degrading or converting compounds [39, 40]. Therefore, it is reasonable to describe lag-phase by comparing the composition difference between sample t1 and t2 (model 1, 2 and 3 in Table 3). In addition, the composition of sample t1 and t2 represents the beginning and the end point of the lag-phase (Table 2), which was also used to build models for predicting lag-phase (models 4, 5 and 6 in Table 3).
In total, six models were constructed for lag-phase (Table 3), of which the data-sets of EA-GC-MS and EC-GC-MS methods were used both separately and combined. This is because the effect of fusing these two data-sets was unknown. The prediction results of the six models are shown in Table 4. It can be seen that among the six models, only ‘model 2’ and ‘model 5’ had a Q2 value above 0.5, indicating that these two models are valid and could be used to predict lag-phase (refer to PLS-2CV models in Method section for the definition of the Q2 value). As shown in Table 3 that the inputs of both ‘model 2’ and ‘model 5’ were from the EC-GC-MS data-set, suggesting that the compounds detected by EC-GC-MS method had more influence on lag-phase compared to those measured with EA-GC-MS.
Models which use EA-GC-MS data-set failed to predict lag-phase properly (‘model 1’ and ‘model 4’ in Table 4), but when combined with EC-GC-MS data-set, the prediction improved, resulting in models with Q2 value of 0.06 and 0.47 (‘model 3’ and ‘model 6’, respectively, in Table 4). As the Q2 value of ‘model 6’ is very close to 0.5, this model was still selected, together with models 2 and 5, to calculate the selectivity ratios (SR) assigned to each peaks in these data-sets.
For each detected peaks from EC-GC-MS data-set, the SR values of the three models in bold in Table 4 were summed, and ranked based on their SR-sum values; while for each detected peaks in EA-GC-MS data-set, the SR value of ‘model 6’ were ranked (refer to Section PLS-2CV models in Method section for the definition of the SR value). The top 40 peaks with the highest SR-sum values, 20 from EC-GC-MS data-set and 20 from EA-GC-MS data-set, were considered as the main contributors in predicting lag-phase. Among these 40 peaks, the ones with identity were tested for their effects on the fermenting yeast (Section Potential inhibitory compound testing, Table 5). The detailed ranking procedure of lag-phase model SR is shown in Additional file 4.
Glu CR and EtOH PR
Unlike lag-phase, samples taken at all five time-points influence Glu CR and EtOH PR according to their definitions (Eq2 and Eq3). These phenotypes were modeled by the data-sets of the five fermentation samples both individually, using the PLS-2CV model, and collectively, using the nPLS-2CV model.
PLS-2CV modeled Glu CR and EtOH PR with the data-sets of individual fermentation samples, which reveals the influence of these single time points on these two phenotypes. The modeling results show that EC-GC-MS data-sets failed to predict Glu CR and EtOH PR, as the resulting Q2 values were all negative (data not shown). On the contrary, the EA-GC-MS data-sets of sample t3, t4 and t5 successfully modeled the two phenotypes, as shown in Table 6, the resulting Q2 values were above 0.5. This suggests that, different from lag-phase, Glu CR and EtOH PR were relating to the compounds detected with EA-GC-MS method. Moreover, the prediction became meaningful only after time point t2 (Table 6, Q2 > 0), indicating that Glu CR and EtOH PR were not affected by the initial hydrolysate composition, but influenced by the composition after lag-phase and during growth. This confirms that the data-sets of time point t1 and t2 possess a different structure compared to the other three time points. This structure contains information that could properly describe lag-phase (Table 4), which ends after time point t2, but failed to predict Glu CR and EtOH PR, which describe a different phase of the fermentation process.
To include the effect of hydrolysate composition change during the fermentation process on Glu CR and EtOH PR, the five time-point samples were also analyzed collectively, with the nPLS-2CV model. Consistent with the PLS-2CV models, the prediction was only valid with EA-GC-MS data-set (Table 7). Since it was known from PLS-2CV models that data-set of sample t1 gave negative Q2 values (Table 6), nPLS-2CV models were also built with the data-set of sample t2 to t5. As shown in Table 7, the predictions of Glu CR and EtOH PR were improved when sample t1 was excluded from the data-set, indicating that the input of sample t1 data-set was negative.
Thus, for Glu CR and EtOH PR, three PLS-2CV models and a nPLS-2CV model were selected respectively for analyzing the contributions of the peaks in EA-GC-MS data-set to model predictions (models with ‘Bold’ in Table 6 and Table 7). With PLS-2CV models, similar to lag-phase, SR of the peaks were summed and ranked. The top 40 peaks with the highest SR values were considered as the main contributors of PLS-2CV models of either Glu CR or EtOH PR. While with nPLS-2CV models, the regression coefficient (‘reg’) values were used for ranking. The top 40 peaks with the highest absolute ‘reg’, 20 with positive values and 20 with negative values, were considered as the main contributor of nPLS-2CV model of either Glu CR or EtOH PR. Among the selected peaks, the ones with identity were tested for their effects on the fermenting yeast (Section Potential inhibitory compound testing, Table 5). The detailed ranking and selection procedure of the testing compounds are shown in Additional file 5. Interestingly, more than 80% of the compounds identified by Glu CR and EtOH PR models are identical. This indicates, from a statistical point of view, the correlation between Glu CR and EtOH PR.
Potential inhibitory compound testing
Two groups of potential inhibitory compounds were identified, one from lag-phase models and the other from Glu CR and EtOH PR models, through constructing statistical models and analyzing the compounds that contribute the most to the models with valid phenotype predictions. Growth tests using Bioscreen C Analyzer were conducted in mineral medium (MM) with 20 g/l glucose to study the effect of these compounds on the fermenting yeast, S. cerevisiae CEN.PK113-7D. The potential inhibitory compounds were added individually with the following three concentrations, 0.2, 0.5 and 1.0 g/l, respectively.
It should be noted that these testing concentrations could be much higher compared to that in actual biomass hydrolysates, i.e. less than 0.1 g/l [11, 14, 41, 42]. The toxicity threshold of a specific compound can be much lower compared to that was tested in synthetic medium due to the synergistic effects present in biomass hydrolysates. Although the testing concentrations were higher compared to that in biomass hydrolysates, the testing results still provide meaningful information.
The first group of compounds shown in Table 5 were identified by all three phenotype models, among which, furfural resulted in longer lag-phase at all three concentrations tested, while sorbic acid and syringaldehyde reduced growth rate. Suberic acid exhibited positive effect towards the fermenting yeast, mainly through shortening lag-phase. Since this phenomenon was only observed in MM, but not in YPD, which a much richer medium compared to MM, we reason that the acid was probably used as a nutrient by the yeast. HMF, though known as an important inhibitor in biomass hydrolysates [43–45], only exhibited inhibitory effect at 1.0 g/l on the growth rate of the fermenting yeast (Table 5). However, HMF seems to prolong lag-phase when tested together with other compounds identified by the lag-phase models. It can be seen that HMF triggered synergistic effect with levulinic acid, 2-furoic acid and pantoyllacton, respectively, at 0.5 g/l (Table 8). This may be the reason why HMF was identified, though little effect was observed when tested individually.
Furfural was identified as a key toxin in biomass hydrolysates [13, 46], and consistent with the current study, its main inhibitory effect was elongating lag-phase [47–49]. It was reported earlier that furfural as well as HMF are converted to their alcohol form (furfuryl alcohol and HMF alcohol) and eventually acid form (furoic acid and HMF acid) by the fermenting yeast due to detoxification [39, 40]. This was also observed in this study. During lag-phase, the concentration of furfural and HMF decreased, while their alcohols and acids were formed. Since the concentration of furfuryl alcohol and 2-furoic acid is showing an opposite pattern compared to furfural, and HMF alcohol to HMF, as could be expected, these compounds were also identified by analyzing the lag-phase models (Table 5).
The potential inhibitors identified by Glu CR and EtOH PR models were mainly phenolic compounds (Table 5). It is known from previous research that the toxic form of a phenolic compound is often the aldehyde, which is converted to its alcohol during the fermentation process due to detoxification [20, 22, 50]. Therefore, possible conversion of the phenolic alcohols identified by the models was checked. For those phenolic alcohol compounds with increased concentrations during the fermentation process, the aldehyde forms were used in the growth tests, assuming that the alcohols were the conversion products. These phenolic aldehydes are marked in italic in Table 5. In agreement with former studies, the compounds exhibited inhibitory effects were mostly aldehydes and acids (Table 5). The major inhibitory effects were reduced growth rate and lower final OD. Phenylacetaldehyde, vanillin and conifer aldehyde caused growth deficiency at 1.0 g/l (0.5 g/l for phenylacetaldehyde, Table 5).
Besides the compounds listed in Table 5, another group of compounds identified by the models was the amino acids, of which concentrations decreased during the fermentation process. This provides the possibility that the depletion of amino acids in hydrolysates worsened the fermentation performance of the fermenting yeast. However, as growth of the fermenting yeast in hydrolysates was not improved when amino acids were added (data not shown), this was apparently not the case. Another explanation would be that the presence of amino acids and possibly other nutrients compensates the inhibitory effects of the inhibitors. This assumption was verified by comparing the inhibitory effects of the compounds listed in Table 5 in MM and YPD medium, which contains abundant peptides and nutrients compared to MM. The inhibitory effects of all the tested compounds alleviated in YPD medium, particularly, the effects in italic in Table 5 were absent in YPD. This observation indicates that the toxicity of inhibitors was culture medium dependent, suggesting that the fermentability of biomass hydrolysates could be improved by adding extra nutrients like yeast extract .
Furfural and HMF are the two most studied inhibitors in biomass hydrolysates, in terms of their inhibitory effects as well as their conversion pathways [13, 40, 45]. However, research on the synergistic effects of these two compounds with other potential inhibitors in hydrolysates was seldom tackled. In this study, the combined inhibitory effects of furfural or HMF with one other potential inhibitory compound were tested using Bioscreen C Analyzer at 0.5 g/l in MM with 20 g/l glucose, and the compounds demonstrated synergistic effect with either furfural or HMF are listed in Table 8. It can be seen that HMF caused a notable synergistic effect with levulinic acid, 2-furoic acid, pantoyllacton and syringaldehyde, respectively. These compounds showed no inhibitory effect individually at 0.5 g/l, but when added together with HMF, they extended lag-phase as well as reduced growth rate (Table 8). Compared to HMF, furfural combined with the selected compounds caused minor negative synergism, since no significant lag-phase increase or growth rate reduction was observed when an extra compound was added (Table 8).
Lignocellulosic biomass is a natural resource that has the potential to become the major feedstock for biofuel production [52, 53]. A metabolomics approach was adopted in this study to identify inhibitory compounds in biomass hydrolysates. Compared to targeted methods, no compound pre-selection was made with the metabolomics approach, so that the inhibitor identification was not influenced by prior knowledge [18, 26]. The study results show that the metabolomics approach successfully identified compounds that influence the growth of the fermenting yeast, S. cerevisiae CEN.PK 113-7D. Some compounds prolonged lag-phase, like furfural and vanillin, while others reduced growth rates, such as HMF and benzaldehyde. Interestingly, without pre-selection, compounds that were previously known as inhibitors in biomass hydrolysates were identified in this study. This confirms that metabolomics is a relevant approach in studying the composition and identifying inhibitors of lignocellulosic biomass hydrolysates.
As the analysis targets were potential inhibitory compounds in biomass hydrolysates, which are weak acids, furans and phenolic compounds [15–17], GC-MS was chosen as the analytical tool . Either ethyl acetate (EA) extraction or ethyl chloroformate (EC) derivatization was conducted prior to sample analysis to prevent sugars from being in the final extracted hydrolysate samples. Due to the property difference of these two sample preparation methods, their target compound groups were also different. As mentioned in section Hydrolysate fermentation sample analysis, the EA method had reliable measurement for aromatic compounds, while the EC methods mainly detected carboxylic acids and furans. This difference in analytical method in relation to metabolomics results was also seen during statistical model building, as EA-GC-MS data-sets could predict Glu CR and EtOH PR properly, but failed to model lag-phase on its own, which was validly predicted by EC-GC-MS data. Accordingly, furans were mainly identified to prolong lag-phase, and aromatic compounds were mostly responsible for reduced growth. These results suggest that in a metabolomics study, it is important to have a wide coverage of detectable compounds, so that the chance of overlooking potential target compounds can be reduced [27, 54]. And one way of achieving this is to use multiple analytical tools for measuring the same sample.
Furfural and HMF were reported as the two most important inhibitors in biomass hydrolysates, which delay as well as reduce growth [13, 40, 48, 55]. In the growth test of this study, it was found that furfural indeed prolonged lag-phase at a concentration of 0.2 g/l, but HMF did not display any inhibitory effect until its concentration reached above 0.5 g/l (Table 5). However, when tested combined, HMF enhanced the negative effect of furfural on lag-phase, and reduced growth rate. When HMF was tested combined with other compounds, which showed no effect individually, like levulinic acid, 2-furoic acid and pantoyllacton, inhibition took place, resulting in extended lag-phase and decreased growth rate (Table 8). These observations suggest that HMF probably functions as a co-inhibitor in biomass hydrolysate, for which inhibition is mainly the result of synergistic effects. Furthermore, synergistic effect reduces the threshold concentration for inhibition. For instance, both HMF and syringaldehyde showed toxicity only at 1.0 g/l towards the fermenting yeast, but when tested combined, the inhibitory effect was present at 0.5 g/l (Table 5). So it is possible that when multiple inhibitors are present, the toxicity threshold of HMF and syringaldehyde reduce to below 0.1 g/l, which is close to their reported concentration in biomass hydrolysates [11, 14, 41, 42].
A group of compounds that were identified with Glu CR and EtOH PR models showed no effect in the growth test. This group of compounds consist of aromatic acids (Table 5). Earlier studies demonstrated that aldehyde was the most toxic form of aromatic compounds, the corresponding acids were less, while the alcohol form was the least toxic [20, 22, 50]. This was confirmed in this study, and was clearly illustrated with vanillin and vanillic acid, of which the acid form had no effect, while the aldehyde form almost abolished growth at 1.0 g/l (Table 5). Besides the identification of previously reported inhibitors in biomass hydrolysates [9, 15, 17, 50, 55, 56], two new compounds were found to be toxic, which are sorbic acid and phenylacetaldehyde. As shown in Table 5, both compounds already showed significant inhibitory effect on growth at 0.2 g/l. The high toxicity towards the fermenting yeast indicates that these two compounds are important inhibitors in biomass hydrolysates. Though not recorded as hydrolysate inhibitors, sorbic acid was described as a preservative weak acid, which disturbs yeast growth through uncoupling mechanism [8, 55, 57, 58], while phenylacetaldehyde was known of having antibiotic activity in maggot therapy . It should be mentioned that the enzyme cocktail used in this study also contains sorbic acid, so the sorbic acid detected in biomass hydrolysates was partially from addition of the hydrolyzing enzyme in most feedstock hydrolysates.
Of the potential inhibitory compounds identified by the statistical models, about half are unknowns. Some of these compounds are on the very top of the ranking lists, see Additional files 4 and 5. Since most of the known compounds identified by the models showed inhibitory effect towards the fermenting yeast in growth tests, it is expected that there are also important/novel inhibitors among the unknown compounds. Identification needs to be conducted for these unknown compounds to verify this, which will be the next step in identifying lignocellulosic biomass hydrolysate inhibitors.
The inhibition property of these compounds was linked to their presence in lignocellulosic biomass hydrolysates through applying metabolomics approach. To our knowledge, this is the first systematic study on identifying inhibitory compounds in lignocellulosic biomass hydrolysates using a non-targeted approach.
Inhibitory compounds in lignocellulosic biomass hydrolysates were successfully identified through applying an exometabolomics approach. The identification was conducted by relating the fermentability of biomass hydrolysates with their composition using statistical models, (n)PLS-2CV. The non-sugar composition of biomass hydrolysates were analyzed with two GC-MS methods, using ethyl acetate extraction and ethyl chloroformate derivatization to remove sample sugar contents, respectively. Besides the known inhibitors, sorbic acid and phenylacetaldehyde were for the first time identified as inhibitors among the identified compounds in lignocellulosic biomass hydrolysates.
Biomass hydrolysate preparation and fermentation
24 different hydrolysates were prepared from six types of biomass, by using four different hydrolysate preparation methods. The six types of biomass were sugar cane bagasse (Bag) (Zillor, Brazil), corn stover (CS) (University of Cape Town, South Africa), wheat straw (WS) (Oostwaardshoeve, The Netherlands), barley straw (BS) (Oostwaardshoeve, The Netherlands), willow wood chips (Willow) (Oostwaardshoeve, The Netherlands) and oak sawdust (Oak) (wood-flooring supplier ESCO, The Netherlands). Prior to pretreatment, biomass (except oak sawdust) was ground to pieces of average length 3 mm and dried at 80°C for at least 16 hours. To prepare 1 l hydrolysate, 300 g dried biomass was used. The four hydrolysate pretreatment methods were dilute acid (2% H2SO4), mild alkaline (3% Ca(OH)2), alkaline/peracetic acid and concentrated acid (72% H2SO4). The biomass pretreated with the first three methods was hydrolyzed enzymatically, using Accellerase 1500 (Genencor®), while acid hydrolysis was used for biomass pretreated with concentrated acid (40% and 15% H2SO4). The detailed pretreatment and hydrolysis procedure was described in Zha et al. (2012) . After hydrolysis, solid content was separated from the hydrolysate by filtration, and the filtrated hydrolysate was sterilized using filter sterilization and stored at 4°C before use.
Batch fermentations were carried out in 2 l New Brunswick fermentors, using 1 l of sterilized hydrolysate as substrate. The fermenting yeast was Saccharomyces. cerevisiae CEN.PK 113-7D (CBS 8340), and the inoculum was prepared in a 500 ml Erlenmeyer flask. The cells were harvested by centrifugation after incubating overnight in mineral medium (MM)  with 20 g/l glucose, and inoculated into fermentors with density of 0.1 g cell dry weight per 1 l hydrolysate. All fermentations were carried out at 30°C, under anaerobic conditions by sparging 0.5 l/min N2 continuously, and pH was set at 5 by adding 1 M H2SO4 or 2 M KOH.
For each of the 24 hydrolysates, one batch fermentation was conducted after checking its reproducibility . During the whole fermentation process, CO2 concentration in the off-gas was monitored automatically and samples were taken at fixed time intervals. These samples were kept at 4°C and used to measure optical density at 600 nm (OD), glucose and ethanol concentration with either Cobas® Mira Plus (Roche) or Arena® 20 Analyzer (Thermo Scientific).
Hydrolysate fermentation sample analysis
For each of the selected hydrolysate fermentations, cell free time samples were chosen for analyzing their overall compositions. Two GC-MS methods, namely ethyl acetate extraction (EA)-GC-MS and ethyl chloroformate derivatization (EC)-GC-MS, were used to analyze the fermentation samples.
For EA-GC-MS, the extraction was done by adding 550 μl ethyl acetate into 0.5 ml sample and vortex for 2 min. The mixture was centrifuged to separate the ethyl acetate fraction, of which 400 μl was transferred to a vial and dried under N2. The following internal standards in ethyl acetate were added to the same vial: phenylethanol-D5, cinnamic acid-D5 and hydroxybenzaldehyde-D4. The extraction and centrifugation process was repeated, and from the ethyl acetate fraction, another 400 μl was transferred to the same vial, after drying with N2, the following internal standards in pyridine were added: alanine-D4 and citric acid-D4. The extract was then oximized by adding 30 μl 56 mg/ml ethoxyamine · HCl, and incubating at 40°C for 90 min. Followed by adding dicyclohexylphtalate (DCHP) and difluorobiphenyl (DFB) in pyridine as injection standard, the oximized extract was silylated by adding 100 μl N-methyl-N-trimethylsilyl-trifluoroacetamide (MSTFA), and incubating at 40°C for 50 min. Measurement was carried out by 1 μl splitless injection in the PTV injector of an Agilent® 7890A GC with an Agilent® 5975C MS as detector. The analytical column used was a HP-5MS column (30 m × 0.25 mm × 0.25 μm).
For EC-GC-MS, the sample pH was brought above 10 by adding NaOH solution, followed by the addition of the following internal standards in pyridine: leucine-D3, succinic acid-D4 and cinnamic acid-D5. The injection standards, DCHP and DFB in pyridine, and 300 μl ethanol were also added to the sample. Then the ethylester formation was done by two rounds of adding 40 μl ethyl chloroformate into the sample and shaking it vigorously by hand for 15 sec. The reaction was stopped by adding 750 μl dichloromethane and 500 μl 1 M bicarbonate buffer. The formed derivates were extracted with dichloromethane, and the extraction was dried with Na2SO4. The measurement was carried out the same way as in EA-GC-MS method. The analytical column used was a DB-1 column (30 m × 0.32 mm × 1 μm).
The analysis results of EA-GC-MS and EC-GC-MS were reported separately in data-sets, with detected peaks as row and fermentation sample as column. The reported values were areas of the detected peaks after correction with internal standards.
Statistical model building
The two statistical models used were partial least square with double cross validation (PLS-2CV)  and n-way PLS with double cross validation (nPLS-2CV) . The 2CV version of the nPLS model was developed in house. The models were written as m-files in MATLAB environment (R2012a) with PLS toolbox 2.0 (Eigenvector).
PLS-2CV is a linear regression model, which predicts the fermentation phenotypes with the GC-MS analysis results of the fermentation samples (data-sets). The PLS-2CV models were assessed by calculating the so-called Q2 values, which indicate the prediction ability of the data-sets for a specific phenotype . The maximum value of Q2 is 1, representing that the model could perfectly predict the phenotypes. Generally, models with Q2 ≥ 0.5 were selected for analyzing the selectivity ratios (SR) assigned to each peaks in the data-sets. Similar to regression coefficient (‘reg’), SR is a measure for variable importance in discrimination models. Contrary to ‘reg’, SR is corrected for the influence of interfering compounds that are not related to the modeled response [61, 62]. Peaks with the highest SR values were considered having the primary contribution to the model building. Among these peaks, the identified ones were selected as potential inhibitory compounds, and tested in Bioscreen C Analyzer for their effects on the fermenting yeast (see Section Potential inhibitory compound test).
To model lag-phase, the data-sets containing the first two fermentation samples (t1 and t2) were used. As listed in Table 3, the difference as well as the combination of t1 and t2 data-sets were used to build PLS-2CV model. EA-GC-MS and EC-GC-MS data-sets were modeled both separately and combined. Thus, for lag-phase, in total six PLS-2CV models were built (Table 3). These data-sets were preprocessed by conducting a ‘square-root’ transformation to reduce the nonsymmetrical distributions of the peak areas for all compounds, and this also homogenizes the heteroscedastic measurement error. Afterwards, an ‘auto-scaling’ was carried out to reduce the effect that compounds with large peak areas would dominate the regression models [63, 64]. The phenotype values were ‘mean-centered’ before data analysis.
To model glucose consumption rate (Glu CR) and ethanol production rate (EtOH PR) (see Eq2 - 4), the data-sets of all five fermentation samples were used individually (t1 to t5, see Table 2). The data preprocessing was conducted in the same way as by lag-phase data-sets (Table 3).
N-way PLS (nPLS) handles multiway data-sets, and was used to model glucose consumption rate (Glu CR) and ethanol production rate (EtOH PR). In this study, the data-sets were three-way, the three ways were (1) fermentation batch, (2) time samples of each batch, and (3) analysis results of each sample. The analysis results of EA-GC-MS and EC-GC-MS methods were used both separately and combined. Similar to PLS-2CV model, the data-sets were arranged in two way and preprocessed by conducting ‘square-root’ and ‘auto-scaling’ before transforming to the three-way structure. The phenotype values were ‘mean-centered’ before model building. The nPLS-2CV models were assessed by calculating the Q2 values. In most cases, models with Q2 ≥ 0.5 were selected for analyzing the regression coefficient (‘reg’) of each peak in the data-sets, as SR for nPLS has not yet been developed. Peaks with highest absolute ‘reg’ values were considered having the most contribution for predicting the phenotypes. Among these peaks, the identified ones were selected as potential inhibitory compounds, and tested in Bioscreen C Analyzer for their effects on the fermenting yeast (see Section Potential inhibitory compound test).
Potential inhibitory compound test
Solutions of potential inhibitory compounds were prepared in both MM with 20 g/l glucose and YPD (Yeast extract Peptone Dextrose) medium with concentrations of 0.2, 0.5 and 1.0 g/l. For those compounds saturated at 1.0 g/l, these saturated solutions (100%) and 2- or 5-fold dilutions resulting in 50% and 20% of the saturated concentration were used. Therefore, in these cases (shown in bold in Table 5) the exact concentrations are not known.
The prepared solutions were adjusted to pH 5.0 and used as media in the growth test of the fermenting yeast, S. cerevisiae CEN.PK 113-7D. The growth test was conducted in triplets in honeycomb plates, using Bioscreen C Analyzer (Labsystems OY). Testing volume was 400 μl, and testing condition was 30°C, no shaking. Growth was monitored by measuring OD 420-580 nm with a time interval of 15 min during the whole experiment. The detailed procedure of Bioscreen test is described in Zha et al. .
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This project was (co) financed by the Netherlands Metabolomics Centre (NMC) which is part of the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research.
The authors declare that they have no competing interests.
YZ designed the study, carried out the hydrolysate preparation and fermentation experiments, performed the data analysis, and wrote the manuscript. JW supervised and participated in the data analysis. BM conducted the fermentation sample GC-MS analysis. KO supervised the fermentation experiments. BN participated in hydrolysate preparation and fermentation experiments. LC supervised fermentation sample GC-MS analysis. AS supervised design of the study and the data analysis. PP coordinated the whole study, participated in and supervised the drafting of the manuscript. All authors read and approved the final manuscript.
Electronic supplementary material
Additional file 1: Figure S1: Description of data:Trends comparison between growth rate calculated based on OD and glucose consumption rate. Growth rate: the slope of the linear part of the OD% curve (green); glucose consumption rate (Glu CR): the slope of the linear part of the glucose consumption curve (Eq2) (red). (PDF 87 KB)
Additional file 2: Figure S2: The calculation results of the four phenotypes, sorted from the smallest to the largest. The ‘red’ bars are the selected 16 fermentations for exometabolomics analysis. (PDF 700 KB)
Additional file 3: EA-GC-MS results (sheet 1), and EC-GC-MS results (sheet 2). Description of data: analysis results of the fermentation time-point samples with EA-GC-MS and EC-GC-MS methods. The 1st column is the sample names, the 1st row is the detected peaks, the data are peak areas after correction with internal standard. (XLSX 144 KB)
Additional file 4: Lag-phase modeling results; Description of data: ‘sheet 1’ provides the selectivity ratios (SR) of lag-phase model 2, 5 and 6 (see Table 3), and their sums; ‘sheet 2’ provides the ranking results of lag-phase models based on the sum SR; ‘sheet 3’ provides the selected potential inhibitory compounds and to-be-tested compounds based on the ranking results. (XLSX 57 KB)
Additional file 5: Glu CR and EtOH PR modeling results; Description of data: ‘sheet 1-3’ contain results of Glu CR models, and ‘sheet 4-6’ contain results of EtOH PR models. ‘sheet 1 and 4’ provide 1) the selectivity ratios (SR) of PLS-2CV models of t3, t4 and t5, and their sums, 2) regression coefficients (‘reg’) of nPLS-2CV model of t2-t5; ‘sheet 2 and 5’ provide the ranking results based on sum SR and ‘reg’; ‘sheet 3 and 6’ provide the selected potential inhibitory compounds based on the ranking results; ‘sheet 7’ combines the results in sheet 3 and 6, provides to-be-tested compounds. (XLSX 74 KB)
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Cite this article
Zha, Y., Westerhuis, J.A., Muilwijk, B. et al. Identifying inhibitory compounds in lignocellulosic biomass hydrolysates using an exometabolomics approach. BMC Biotechnol 14, 22 (2014). https://doi.org/10.1186/1472-6750-14-22
- Lignocellulosic biomass hydrolysate
- (n)PLS model
- Double cross validation

domain: chemistry


text:
It’s no secret that the Roarbots are fans of acrobatics and the whole “artistic circus” genre. (See our tour of Cirque du Soleil’s International Headquarters here.)
Cirque Dreams is in this same category and has been around since 1993, entertaining audiences around the world in theatres, casinos, theme parks, and at sea on cruise lines. They have a number of different performances touring at any one time.
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domain: artistic


text:
Sep 24, 2010, 1:49 PM EST
Matt Murton was a 2003 first-round pick who hit .286/.352/.436 in 1,058 plate appearances spread over 354 games for the Cubs, Rockies, and A’s, but for whatever reason never really got a shot to be an everyday player despite doing very well as a 24-year-old regular in 2004.
After spending most of last season at Triple-A he headed to Japan, signing with the Hanshin Tigers, and yesterday Murton became just the fourth player in Japanese history to reach 200 hits in a season. Not only that, but with 201 hits and 10 games remaining he has a chance to break the all-time record of 210 hits held by Ichiro Suzuki.
Murton is hitting .352 with 17 homers for Hanshin and has averaged 1.5 hits per game, which puts him on a 216-hit pace. However, as Daisuke Wakabayashi of the Wall Street Journal points out “there are past examples of Japanese pitchers refusing to challenge foreign players who are close to breaking Japanese baseball records.”
It’ll be interesting to see if Murton gets enough strikes to make a run at Suzuki’s record and if the big season in Japan will cause an MLB to give him another look.
- Daniel Murphy on Billy Bean: “I do disagree with the fact that Billy is a homosexual” 255
- Blue Jays sign Dayan Viciedo to a minor league deal 8
- Chris Sale will be sidelined for three weeks with foot fracture 11
- Aramis Ramirez says 2015 will be his last year 32
- Francisco Rodriguez re-signs with the Brewers 9
- If addiction is an illness — and it is — Josh Hamilton shouldn’t be suspended 306
- Pirates open to massive extension for Andrew McCutchen 18
- Report: Josh Hamilton had a relapse this offseason that “involved at least cocaine” 86
- If addiction is an illness — and it is — Josh Hamilton shouldn’t be suspended (307)
- Daniel Murphy on Billy Bean: “I do disagree with the fact that Billy is a homosexual” (279)
- Curt Schilling lowers the boom on some men tweeting threats against his daughter (136)
- San Francisco — and all of California — will consider a smokeless tobacco ban that includes MLB parks (131)
- Ichiro is happy to be away from Joe Girardi (88)

domain: news


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The book is available end of March signed £15 p&p ( London, mainland only)
There is an expression that says, “hurt people hurt people.” When it comes to being a survivor of abuse, one can agree that the pain becomes invisible. The scars left from the abuse experienced can travel with us from childhood to adulthood and beyond. Facing our traumas and abuse is the beginning of our healing journey. This book will inspire you to ignite your healing journey from the abuse you’ve experienced.

domain: psychology


text:
Salois this fall will attend the University of Rochester’s Ph.D. program in neuroscience, studying for a career in brain research.
An unexpected opportunity resulted in an impressive performance by students in Saginaw Valley State University's moot court program.Students Mark Babcock, a psychology major from Saginaw, and Felicia Jostock, a criminal justice major from North Branch, advanced as a team to the quarterfinals round of the American Collegiate Moot Court Association national invitational tournament in Chicago April 24-25.
The Saginaw Valley State University student was hired full-time as a math teacher at Bridgeport Middle School, weeks before earning a bachelor’s degree in mathematics education in May.
Brandon Skwirsk knew he wanted to become a doctor in the sixth grade.
Saginaw Valley State University student Cullen James recently won the American Society for Clinical Laboratory Science’s (ASCLS) 2015 viral video contest. James, a medical laboratory science major from Birch Run, submitted a 99-second video that promotes the benefits of being a member of the society.
After graduating from Saginaw Valley State University in May, she will move to North Carolina, where she is enrolled in the highly competitive Doctor of Physical Therapy program at Duke University.
After receiving her bachelor’s degree from Saginaw Valley State University in May, Marr plans to teach English in other countries.
In a 5-year span, Aldubayyan went from speaking little English to becoming one of the university’s student leaders.
Helping others doesn’t cost Shantinique Beverly energy. It provides the energy.
Since childhood, Terrance Moore has been planning a career in law enforcement. Thanks in large part to his experience at Saginaw Valley State University — and all the opportunities it presented — that career already has begun.


text:
1 - 8 businesses Add Your Business and Get Found - Free!
Matetsi Luxury River Lodge - Victoria Falls
Experience the magic that is Matetsi Victoria Falls: the ultimate safari experience and idyllic accommodation retreat from which to visit the largest waterfall on earth.
Operation of Hope
Operation of Hope Worldwide (formally Operacion Esperanza since 1989) is a volunteer medical team performing free surgeries for children in need.
Dariro.com offers an online platform for people and business to share information for developmental purposes. People are as developed as the information they can access.
The purpose of ZIMEXPO is to showcase Zimbabwe. Every year we have an expo in the United States, Canada, United Kingdom and South Africa. During ZimExpo events, we encourage both Zimbabweans and the local community to participate.
Property Of Africa was founded on a simple premise: to empower artists of African descent to reach their highest potential and aspirations in the United States music industry.
Car Junction Co. Ltd.
For purchasing a used quality Japanese vehicle contact firstname.lastname@example.org. Your desired vehicle can be shipped directly from Japan to your desired destination or you can make selection from our available stock in different countries of Africa.
Cheap Flights to Zimbabwe - Book Cheap flights and bargain airfares to, Zimbabwe, Search hundreds of flights deals online and get Bargain flight deals at http://cheapflightstozimbabwe.org.uk/ so call now 0207-993-5206.
We specialize in big groups, small groups, families & individuals, wether you are travelling in style or backpacking, we can assist you to make your stay memorable.

domain: travel


text:
In the past few decades scholars have come to realize the important function the Atlantic Ocean has served in connecting the continents, in furthering exchange and a sharing of ideas, people, and goods. This renewed focus on transatlantic experiences has produced a host of excellent and valuable scholarship and thus cemented its role as a vital component for the field of history. Florida International University’s Department of History Graduate Student Association seeks to showcase scholars’ original research focusing on aspects of the Transatlantic World in its annual scholarly publication. The Atlantic Millennium is published both in print and online and offers upcoming researchers the opportunity to share their work with a wide scholarly audience.
The Editorial Board of The Atlantic Millennium is now accepting submissions for the 2014/15 issue (to be published in Fall 2014). No restrictions apply with regard to historical time period and regional settings. Scholarly articles should be between 8,000 and 12,000 words. Scholarly reviews of books (published since 2012) should be between 700 and 1,200 words. All submissions must be formatted in Chicago Manual of Style (16th Edition).
Articles and book reviews must be accompanied by a brief CV of the author. All documents must be submitted electronically via email to AtlanticMillennium@gmail.com. Text files must be submitted as .doc, .docx, and .rtf file types. Photographs and other graphic depictions should be submitted as common picture file types.
All submissions to The Atlantic Millennium are carefully evaluated for content, analytical skill, and verbal style by an editorial board composed of senior graduate students with expertise in various areas. To view the latest issues of The Atlantic Millennium, visit dohgsa.fiu.edu.
Please submit your research by October 15, 2014.
Department of History Graduate Student Association Editorial Board
Florida International University
Modesto A. Maidique Campus
Deuxieme Maison, Room DM 397
11200 SW 8th Street
Miami, FL 33199
Send comments and questions to H-Net
Webstaff. H-Net reproduces announcements that have been submitted to us as a
free service to the academic community. If you are interested in an announcement
listed here, please contact the organizers or patrons directly. Though we strive
to provide accurate information, H-Net cannot accept responsibility for the text of
announcements appearing in this service. (Administration)

domain: library


text:
Apparently Seaprime crab meat can be used for all recipes, this time let's we try to cook Aceh noodles... Aceh noodles is rich in spices, for beginners, it's okay... not much different like the one in culinary places. Thanks a lot for the recipes to Seaprime... superb... it's delicious
Heat the oil pour pan after heat the onion input has been sliced, saute until fragrant, then input the spices that have been mashed, stir until cooked.
After finely cooked seasoning enter crab meat, cabbage leaves, mustard greens, bean sprouts, spring onions and celery. Give sweet soy sauce, soy sauce and salt, flavor correction, leave to boil after it enter the wet noodles, cook until cooked.

domain: food


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Activity will take place on Friday, February 4 and is organized by the Municipality of Paillaco together with the Sumemos Salud Corporation and sponsored by the Base Hospital of Valdivia.
In order to contribute to the investigation and opportune diagnosis, an oncology session will be held in the commune of Paillaco aimed at Primary Health Care teams. The activity will take place on Friday, February 4, within the framework of World Cancer Day and is organized by the Municipality of Paillaco, in conjunction with the Sumemos Salud Corporation and sponsored by the Valdivia Base Hospital (HBV).
On this initiative, the mayor, Dr. Miguel Ángel Carrasco, valued that there are organizations to fight against this pathology, mainly from the citizen path.
“The health discussion of the last two years has revolved around Covid-19 and its consequences, for this reason, together with the Sumemos Salud Corporation, we have emphasized the realization of this oncological day, where there will be a reflection from different perspectives together with officials and APS officials. It is necessary to put emphasis on health issues as dangerous as cancer and educate ourselves in its prevention and timely diagnosis “, detailed the communal authority.
In this regard, the president of the Sumemos Salud Corporation, Soledad Ojeda, stressed that “we have to work together from all areas to stop this other pandemic, that of cancer. It is the first cause of death in our country and we thank the mayor of Paillaco, Dr. Miguel Ángel Carrasco for his concern, for understanding that this has to be a priority because it is affecting us, it is killing us. And also to value the contribution of the outstanding exhibitors of our region that will participate in the day”, he pointed out.
CANCER IN CHILE
In Chile, in 2016, cancer was the second cause of death, registered only after cardiovascular diseases, and by 2023 it is expected to be the main one.
The cancers with the highest mortality and incidence in Chile in men are stomach, prostate and lung, while in women they are breast, lung and gallbladder and bile ducts.
The activity -aimed at health professionals- has the collaboration of Roche Chile and the Costanera Clinic of Valdivia.
The program includes:
– Presentation by the head of the Cancer Department of the Ministry of Health, Dr. Paula Escobar.
– Valdivia Hospital, High Complexity Oncology Center, Dr JUan Carlos Bertoglio, HBV director.
– “Breast cancer and regional reality”, by midwife Mabel Ríos from the HBV Breast Pathology Unit.
“Cervical uterine cancer”, Dr. Emil Schneider, gynecologist and breast surgeon.
-Testicular cancer, Dr. Luis Ebel, HBV urologist and director of the Chilean Society of Urology.
-The importance of imaging in oncology, Dr Carlos Olmos, HBV radiologist
-Advances in cancer treatments, Dr Hernán Letelier, oncologist and Vice President of the Chilean Society of Radiotherapy Oncology.
-Therapeutic patient education 2.0, nurse Angélica Hernández, specialist educator in psycho-oncology and NGOs.
The event is aimed at primary care teams and health professionals in the region, and will take place between approximately 9:00 and 1:00 p.m. at the Casona Cultural El Llolly. Spaces are limited, a mobility pass will be required from those attending and registration for both modalities can be made at http://www.sumemossalud.cl or in the mail [email protected]
If you are going to use content from our newspaper (texts or simply data) in any communication medium, blog or Social Networks, indicate the source, otherwise you will be committing a crime sanctioned by Law No. 17,336, on Intellectual Property. The foregoing does not apply to photographs and videos, since their reproduction for informational purposes is totally PROHIBITED.

domain: medical


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Mikato Steak and Sushi
Welcome to Mikato Steak and Sushi! We have been committed in serving our customers with the best sushi and Japanese steak in Louisville since 2008. We were voted and awarded with the “People’s Choice Award” for Louisville’s “Sushi in the City” in 2010 and 2011.
At Mikato Steak and Sushi our interior design is specially created to enhance your dining experience. Our staff members are humble and happy to serve you amazing sushi creations among other delectable entrees we offer. The ingredients used in every dish is of utmost freshness and excellent quality. We have so much love and passion for food it is prominently reflected in the presentation and taste of each and every spectacular dish. Come visit us and try our savory entrees or order carry out with our easy online ordering website.
Mikato Steak and Sushi - Crestwood
6001 Pleasant Colony Ct, Suite 8
Crestwood, KY 40014
Mikato Steak and Sushi - Shelbyville
2414 US Hwy 60
Shelbyville, KY 40065


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The new business is located in Oak Hill Plaza behind the Jasper Bowling Center off Highway 195. The Bead Shop sells handmade jewelry, beads, jewelry tools and offers jewelry making classes. Ackerman said she felt the business was a good idea because it was unlike any other business in Jasper.
“Jewelry makers in our area were having to go to Birmingham and Tuscaloosa to buy supplies,” Ackerman said. “It just seemed like a good idea to open a store here.”
Ackerman said the good idea has proven to be just that as the store has seen a steady growth in business since its opening in April.
“We’ve done very well so far,” she said. “People are hearing about us and they are coming to the store.”
Along with beads and items to make jewelry, the store also sells items from jewelry makers across Walker County. Ackerman said The Bead Shop currently has eight consigners and are looking for more.
“We want to get the word out that we have handmade jewelry,” she said. “The jewelry makers we have do an outstanding job and create some beautiful pieces.”
The Bead Shop also sells custom jewelry for weddings, proms and pageants. Gift baskets and T-shirts are also available for purchase.
“Within the next two weeks, we will have T-shirts for all the area high school football teams,” she said.
The Bead Shop is open Monday through Friday from 9 a.m. to 5 p.m. and Saturdays from 9 a.m. to noon. Ackerman said the shop will be closed several Saturdays in September due to selling jewelry at festivals across the area. The business also can be found on Facebook by searching Bead Shop Jasper. For more information, call the store at 205-471-2461.

domain: economics


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Chivalry is the combination of qualities expected of a knight: courage, honor, courtesy, justice, and readiness to help the weak. So, is it dead? Can it live on through Tinder?
Staying friends with your exes can be fine and healthy, right?
Is marriage is no longer necessary?
Are you a better husband or a better father? Can you be both at the same time?
The BITM crew answers these and other questions that challenge the status quo of what it is supposed to mean to be a man and a women in todays world.
Black in the Middle
Middle class, middle of map and just always stuck in between. This is a podcast about a three black friends who grew up in the burbs and how the battle of nature vs nurture turned them into the men they are today. Follow along as they layout a blueprint on addressing, engaging and making decisions based on cultural biases and varying perspectives.

domain: sociology


A charity group that donates tablets to nursing homes and hospitals has launched an online fundraiser to help those who are unable to see their families during the lockdown.
The initiative has allowed thousands of nursing home residents and hospital patients to connect with their families and friends during the coronavirus lockdown.
The COVID-19 Tablet Campaign has already seen nearly 160 nursing homes and 27 hospitals in Ireland receive nearly 500 devices.
Now they hope to raise €24,000 this Friday to help connect thousands more to their loved ones.
Here’s how you can take part in #10for10challenge:
1. Do 10 reps (or 10 seconds) of an activity (dancing, singing, sit ups, jumping jacks, anything at all)
2. Post on social media and tag @comfort4covid and use the hashtag #10for10challenge
3. Donate €10 to their charity GoFundMe Page
4. Share with others to do the same
Lily Jackson, patient in Clontarf Hospital, was delighted to when she could talk to her daughter thanks to the campaign.
She said “I had a lovely surprise when Anne (her daughter) walked into the room - well her face did!
“I haven’t seen Anne since the last day in March and the next day was my 92nd birthday.”
Vinny Caprani, resident in Talbot Lodge Nursing Home, said of using the tablets: “It really was wonderful to have the opportunity to see Suzanne (his daughter) and just to talk to her as if she was here with me.”
For the latest news and breaking news visit dublinlive.ie/news.
Get all the big headlines, pictures, analysis, opinion and video on the stories that matter to you.
Follow us on Twitter @DublinLive - the official Dublin Live Twitter account - real news in real time.
We're also on Facebook/dublinlive - your must-see news, features, videos and pictures throughout the day from the capital.


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Traditional grocers such as Kroger Co (NYSE:KR) and Delhaize Group SA (NYSE:DEG) are having an increasingly hard time dealing with competition from nontraditional grocers. Both of these firms have tried to adapt to the pressure stemming from rival discounters, with varying degrees of success. Whereas Kroger has been able to utilize its scale to leverage fixed costs, Delhaize has struggled to maintain margins, and is being forced to lower prices.
Fending off competitorsInvestment gurus John Hussman of Hussman Economtrics Advisors and Joel Greenblatt of Gotham Capital own considerable positions in Kroger, one of the largest retailers in the U.S. The firm currently operates over 2,400 supermarkets, 750 convenience stores, and 325 jewellery stores across 31 states. Shareholders have reason to be optimistic, as the company recently acquired Harris Teeter Supermarkets Inc (HTSI). The $2.44 billion deal means Kroger will be looking at an 8% increase in its store base and a 4% boost in revenue.
The purchase of Harris Teeter is part of Kroger’s strategy to fend off rival discounters, such as Wal-Mart Stores Inc (WMT), Costco Wholesale Corporation (COST)andTarget Corp(TGT). The firm intends to lower prices on items such as bread and milk, in order to attract more customers to the high-end products Harris Teeter has to offer. Thus, it will seek to drive traffic to its stores by using food as a loss leader, much the same way rivals operate. Reduced margins are expected, yet Kroger is still the best positioned traditional grocer to deal with the competition arising from non-traditional grocers. The firm’s 2,400 stores and $100 billion in annual sales, generate a scale which enables it to leverage fixed costs and thus compete with large rivals.
Kroger also offers gasoline at almost 50% of its locations, limiting the threat of competitors luring customers away with this service. In addition, the firm’s private-label products have achieved a 25% penetration rate. With 40% of this merchandise manufactured directly by Kroger, margins are amplified and profits increased considerably. The strong growth in same-store-sales, the firm’s acquired market share, and the 8.5% weighted average cost of capital estimate are indicative of the company’s solid business model. Finally, Kroger is trading at only 14.1 times its trailing earnings, translating to a 24.2% price discount to the industry average, which represents a good point for entry.
Crumbling under the pressure
The food retailer Delhaize operates over 3,400 stores across the U.S., Belgium, South East Europe, and Asia. The company operates 1,500 banners in the U.S., including stores such as Food Lion, Harveys, Bottom Dollar Food, and Hannaford. Despite reporting solid second-quarter financial results, increasing pressure from competitors could harm the firm’s U.S. margins and returns on invested capital.
Non-traditional grocers are challenging the food retailer by selling food at tiny or inexistent margins forcing Delhaize to lower its prices. The industry’s low customer switching costs and the company’s lack of cost advantages relative to rivals Wal-Mart, Costco, and Kroger leaves Delhaize in weak position. In order to counteract this pressure, the firm has plans to sell some of its smaller banners, such as Sweetbay, Harveys, and Reid’s for an estimated $265 million. Since the traditional grocer has no scale advantages over larger rivals, it will focus on the strong performing Food Lion banner, which should contribute to better results.
Delhaize’s European locations on the other hand are exposed to less competitive pressures, yet the hostile macroeconomic environment has put a lid on growth. Its market share of 25% in Belgium and 21% in Greece are significant, yet economic hardship has not allowed the firm to extract the margins it achieves in the U.S. With austerity measures looming and prolonged recession on the horizon, growth and profitability are threatened. With 20% of Delhaize’s sales stemming from Belgium and 8% of its stores located in Greece, this is certainly troubling.
The only investment guru to trade in Delhaize shares over the past years is David Dreman of Dreman Value Management. He has been adding shares to his portfolio steadily since 2009 and must trust the company to regain its foothold in the longer term. I personally feel bearish regarding this stock and believe the price tag is far too high regarding its current and projected performance. The firm is currently trading at 25 times its trailing earnings, entailing a 34.4% price premium relative to the industry median.
An easy choice
After looking at these two firms’ current and future prospects, I think Kroger represents the best investment option. It is the only traditional grocer with enough scale to compete with non-traditional rivals, and has been delivering positive returns on invested capital for years. The recent acquisition of Harris Teeter is an additional step in the right direction for the firm that has retained a large market share without falling prey to margin pressure.

domain: finance


text:
Eggplant and Kabocha Miso Gratin
My mother arrived last week from Japan to celebrate the holiday season with us and boy have we been busy these past few days!
We did some shopping on 5th avenue where the decorations are not to be missed this time of the year, gone to a couple of Broadway shows, ate some escargots (her favorite) and caught a Brooklyn Nets’ game. When friends or relatives come for a visit, it leaves me with very little time in the kitchen to cook.
That can easily become a worry when they start asking for home cooked meals after seeing all the recipes on my blog!
Thankfully, I have a large library of easy and yummy recipes on hand to whip up even if it’s late at night. Since my mother had never heard of a gratin made with miso before, I jumped at the opportunity to make her my favorite; an eggplant and kabocha miso gratin.
From the first bite she fell in love with the creamy, sweet and savory sauce of this miso gratin and asked for the recipe so she could make it back home.
Anything she requests the recipe for I consider a huge success! And just like me she cares about eating a sensible diet, so it’s a good thing that this cheesy eggplant and kabocha miso gratin is mostly made with healthy ingredients.
Plus, a little cheese never hurt anybody right? 😉
I like testing Japanese influenced recipes on my mother since she is such a fantastic chef. She is also the one who showed me that good Japanese food can be made even if all the required ingredients cannot be found.
She spent 25 years living in a teeny town in the province of Quebec where there were no Japanese grocery stores and still managed to make the best watermelon rind tsukemono, with only ajinomoto and salt.
Watching her make Japanese food is like going to culinary school; I’m always paying close attention to every step she takes and furiously jot down the dos and the don’ts of Japanese cooking.
But now that I am older and have developed my own way of cooking, I feel extremely flattered whenever she takes out her own note book and asks me for tips, such as for this miso gratin. One of her biggest qualities is her humility and because of that I never felt inferior to her, even as a little girl.
I grew up feeling confident in myself and that goes a long way as an adult! I owe my strong character and solid self-worth to her. Thanks mom!
And so the evening we went to see Jersey Boys, I prepared this eggplant and kabocha miso gratin in advance.
All that was left to do once we got back home was a quick zap in the microwave and voila! I served the gratin with a side of Japanese rice (my mother can’t live without rice), a seaweed salad and daikon pickles.
We ended the evening watching season 1 of Glee with some salt and vinegar chips, a glass of rose for the ladies, and a Yamazaki whiskey for the man of the house.
Eggplant and Kabocha Miso Gratin
A sweet, savory and extremely satisfying recipe for eggplant and kabocha miso gratin.
- Prep Time: 10 minutes
- Cook Time: 35 minutes
- Total Time: 45 minutes
- Yield: 2 people 1x
- Category: Side
- Method: Baking
- Cuisine: Japanese
- 2 small eggplant
- 1 pound kabocha squash (alternatively you can use acorn squash)
- 1 tablespoon vegetable oil
- 2 tablespoons miso paste (any type is good)
- 2 tablespoons sake
- 2 tablespoons soy sauce
- 1 tablespoon mirin
- 2 tablespoons heavy cream
- 1 tablespoon sugar
- 1 egg yolk
- 1/2 cup shredded mozzarella
- shredded Italian parsley (optional)
- Preheat oven to 400ºF.
- Slice eggplant in half and using a knife, score the inside in small squares.
- Slice kabocha in half, scoop out the seeds and peel the skin off. Chop into bite size pieces.
- In a pan over high heat, add oil and put the eggplant skin facing down.
- Cook for a few minute until skin is brown (3 to 4 minutes).
- Turn the eggplant over, add kabocha pieces and cook for another 3- 4 minutes, until eggplant is tender. Transfer eggplant to a plate and slice into bite size pieces. Cook kabocha for another 3-4 minutes, until tender.
- In a bowl, mix egg yolk and miso paste until smooth. Add sake, soy sauce, mirin, cream and sugar. Mix until sugar has dissolved.
- Divide eggplant and kabocha pieces among 2 ramekins. Equally pour miso sauce into both ramekins. Top with shredded cheese.
- Put ramekins on a cookie tray and add about an inch of water to the tray. Bake for 25 minutes.
- Top with shredded parsley before serving.
- Serving Size:
- Calories: 403
- Sugar: 18.4 g
- Sodium: 1222.4 mg
- Fat: 12.7 g
- Saturated Fat: 5.5 g
- Carbohydrates: 49.5 g
- Fiber: 8.6 g
- Protein: 19 g
- Cholesterol: 118.5 mg
Keywords: appetizer, casserole
Amazing and Delicious!
Hi, can you help me to make it vegan version? What would you suggest to replace heavy cream, mozzarella cheese, and egg please? Thanks so much look delish!!
Hi Tania! I’ve never made a vegan version of this so I’m just guessing here – how about replacing the heavy cream with soy or almond milk and whisking 1 tablespoon of flour instead of an egg (this would thicken the sauce). As for the cheese, I would simply replace it with vegan cheese since there are some good ones out there (Whole Foods has a great selection). I hope this helps and that it turns out yummy! Keep me posted!:)
Just do a vegan bechamel sauce. They bake great and form a “burned” crust when in the oven. That is what you would use in a Greek mousaka.
Your recipes look good. I’ll have to get a food dictionary to see what some of the spices are Awase miso paste and mirin. Just getting started to eating healthier. Jim
That’s wonderful Jim! Eating healthier doesn’t mean you have to eat boring, I hope you enjoy your journey! Awase miso is basically a combination of different types of miso paste (there are a variety of miso pastes ranging from sweet to very umami) and mirin is a common condiment used in Japanese cooking. It’s a sweet rice wine vinegar with a hint of sake flavor.
Mmmm! Looks delish!
Hi! Stopping by from Mom Bloggers Club. Great blog!
Have a nice day!
Hi Veronica! Thank you and happy new year!


The main forms of heat dissipation of the human body are radiation, convection, and evaporation. The overall heat radiation and convection can be calculated by the environmental temperature and the radiation temperature, while heat evaporation can be calculated by the environmental humidity and the finger surface humidity.
A revised heat transmission method is applied in this product to determine the blood flow velocity. A heat transfer bar with thermistors on both ends contacts the skin of the finger with one end, so the temperature change of the bar can be measured. Meanwhile, an infrared radiation temperature sensor is used to take the temperature of the finger surface. These values are combined to calculate the blood flow velocity.
The photoelectric method is adopted to determine the parameters of oxygen saturation and haemoglobin concentration. The surface of the finger is exposed to infrared light of a specific wave length in an assigned sequence and the light intensity penetrating the finger is detected. Based on the change of light intensity, the required parameters are calculated. Considering that the heat dissipating capacity cannot represent the local metabolic heat, the latter is then corrected by core temperature, dressing impedance and blood flow volume. The blood glucose value of the metabolic heat integration method can be calculated by local metabolic rate, blood flow volume, haemoglobin concentration, oxygen saturation and corrected value of blood glucose. The blood glucose value of the infrared method is calculated from photoelectric signals and the haemoglobin concentration. By combining the blood glucose values of the two methods, the blood glucose concentration in the human body is obtained.
This product integrates various of sensors for signal collection, a processing circuit for signal conversion and amplification, a microprocessor to calculate the values of the parameters (blood glucose concentration, oxygen saturation, haemoglobin concentration, blood flow volume, pulse, ambient temperature and humidity, and shell temperature and humidity), and finally displays the results.
This update is signed with Apple’s latest signing certificate. No new features are included.
- ESER Industrial Design Co., Ltd
- 69.8 MB
- Health & Fitness
Requires iOS 9.2 or later. Compatible with iPhone, iPad, and iPod touch.
English, Simplified Chinese, Traditional Chinese
- Age Rating
- Rated 4+
- © 2016 Shenzhen ESER Digital Scientific Development CO., Ltd
With Family Sharing set up, up to six family members can use this app.


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- Do Dogs have normal vision during night ?
Yes Dogs have better vision even better than us.
- Dogs and humans sense
Humans we have better tasting sense, Dogs have great sense of smell which 1000+ times than us.
- Do Dogs sweat like us?
No Dogs can sweat like us humans but they can through their paw pads.
- What is breed name of the dog which rescues humans on mountains in the cartoons i see?
Its is fluffy dog named St Bernard.
- Does dogs have teeth when they are born?
No dogs don’t have teeth and they don’t have vision when they are born.
- Just like us do dogs have different blood types?
Yes, we just have 4 principal blood types while dogs have 13.

domain: pet