5. Conclusions

The metal-support interaction is one of the most important parameters in the design of HDS catalysts. The use of silicon in the preparation of HDS catalyst support has been showed to weak the metal interaction, generating type II Co(Ni)- Mo(W)-S structures, which are characterized by: (i) a complete sulfidation of the oxidized phase, weakening the electronic interaction with the support or (ii) stacked structures so that the upper crystallites in the structure Mo(W)S2 have a low interaction with the support. The so-called type II Co(Ni)-Mo(W)-S structures are more active than the partially sulfide type I. The use of mesoporous silicates such as MCM-41 and SBA-15 has been proposed in the literature, with the intention to increase the active phase, acidity, the type II structures, and the dispersion of the active phase (Mo(W)S2). In the case of the MCM-41 support, the preparation conditions such as pH and the Si/Al molar ratio increase the number of oxidized species in octahedral coordination, which are precursors of the type II structures. However, these materials did not show better HDS performance in the DBT molecule compared with the alumina support. It would be very useful to evaluate this type of catalyst support (MCM-41) in a molecule more refractory to HDS, such as 4,6-DMDBT, and see if it is possible to increase the catalytic performance compared to the catalyst supported in alumina, since the 4,6-DMDBT molecule is more sensitive to the geometry of the Co(Ni)-Mo(W)-S structure than the DBT molecule. On the other hand, SBA-15 mesoporous silicate doped with a certain amount of Ti (Si/Ti = 60 molar ratio) showed to improve the catalytic performance in the HDS of the DBT molecule, through generating a greater population of type II structures; therefore, these materials are useful as HDS catalyst support.

Silicon Materials

Grafting SiO2 on the surface of γ-alumina or the use of mixed support (Al2O3-SiO2) are other alternatives to promote the formation of so-called type II Co (Ni)-Mo(W)-S structures. It has been reported that a small amount of SiO2 is enough to weaken the metal-support interaction and thus increase the extent of sulfidation and level promotion, which are fundamental parameters to improve the performance of HDS catalysts. So, it can be concluded that the use of silicon in the preparation of HDS catalyst support is a promising alternative to get better performance in HDS catalysts.

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*2.1. GUM Method*

Measurements are always influenced by unpredictable factors, so a result can only ever approximate the quantity of interest. Influence factors can, however, be identified and described in probabilistic terms. In this manner, a measurement process can be represented by a mathematical model. This is the approach taken in the GUM.

The first step is to identify a function called the measurement model that *contains every quantity, including all corrections and correction factors that can contribute a significant component of uncertainty to the measurement result* [3] [Section 4.1.2]. In the GUM, this is expressed as an explicit function:

$$\mathcal{Y} = f(X\_1, X\_2, \dots, X\_l) \; , \tag{1}$$

where *Y* is the quantity intended to be measured (the *measurand*) and the input arguments *X*1, *X*2, ··· , *Xl* are quantities that influence the measurement outcome. All arguments of *f*(···) are treated in the same way when evaluating uncertainty. Some of the input terms may represent other measured quantities. For example, electrical resistance could be measured by first measuring potential difference *V* and current *I* and then evaluating ratio *R* = *V*/*I*. Other input terms may represent nuisance factors that perturb the measurement, such as Johnson noise in a resistor. The measurement model can be thought of as a recipe for evaluating the measurand: if *X*1, *X*2, ··· , *Xl* were all known exactly, then *Y* could be determined. However, with only approximate values available for input quantities, an approximate value for *Y* will be obtained.

$$y = f(\mathbf{x}\_1, \mathbf{x}\_2, \dots, \mathbf{x}\_l, \mathbf{x}\_l) \,. \tag{2}$$

GUM uses a specific term, *standard uncertainty*, in relation to the unpredictability of measurement outcomes (i.e., the fact that *y* and *Y* differ by an unpredictable amount). A standard uncertainty is an estimate of the standard deviation of a probability distribution for the difference (error) between *y* and *Y*. There is a formula in the GUM to propagate standard uncertainties through a measurement model and obtain the standard uncertainty in a result. For a model in the form of Equation (1), the standard uncertainty of *y*, as an estimate of *Y*, is calculated as follows:

$$u(y) = \left[\sum\_{i=1}^{l} \sum\_{j=1}^{l} u\_i(y) \, r(\mathbf{x}\_i, \mathbf{x}\_j) \, u\_j(y)\right]^{1/2},\tag{3}$$

where *ui*(*y*) and *uj*(*y*) are *components of uncertainty* that relate small changes in input values to corresponding changes in *y*.

$$u\_i(\underline{y}) = \left. \frac{\partial Y}{\partial X\_i} \right|\_{X\_i = x\_i} u(x\_i) \qquad \text{and} \qquad u\_j(y) = \left. \frac{\partial Y}{\partial X\_j} \right|\_{X\_j = x\_j} u(x\_j) \,. \tag{4}$$

The terms *u*(*xi*) and *u*(*xj*) are the standard uncertainties in the input values *xi* and *xj*, respectively. The correlation coefficient attributed to a pair of input estimates is *r*(*xi*, *xj*), where *r*(*xi*, *xi*) = 1 when *i* = *j*.

Standard uncertainties are associated with a number called the *degrees of freedom*, usually denoted *ν*. The interpretation given to *x*, *u*(*x*), and *ν* in the GUM is analogous to familiar sample statistics: the sample mean, the standard error in the sample mean, and the degrees of freedom [8] [Chapter 9]. However, degrees of freedom are interpreted more broadly in the GUM, because uncertainty evaluation is not always based on a sample of data. When a standard uncertainty is considered to be known very accurately, the degrees of freedom is large (up to infinity), but a small number of degrees of freedom (as low as unity) signifies a very rough estimate of the underlying standard deviation. Again, the GUM provides an equation for propagating degrees of freedom, called the Welch–Satterthwaite formula. The number of degrees of freedom associated with a standard uncertainty *u*(*y*) is as follows.

$$\nu\_{\mathcal{Y}} = \frac{\mu^4(\mathcal{y})}{\sum\_{i=1}^l \frac{\mu\_i^4(\mathcal{y})}{\nu\_i}} \,. \tag{5}$$

However, there is an important restriction on the use of this equation. It is not valid when input estimates that have finite degrees of freedom are correlated with each other. This is not a rare occurrence. For example, estimates obtained from linear leastsquares regressions are often correlated and have finite degrees of freedom. Fortunately, an extended form of (5) can be used in some important special cases (see Appendix A.1) [9].

Equations (1)–(5) describe a methodology for evaluating measurement uncertainty that any GUM-compliant data processing should adhere to. However, in many situations, it is inconvenient, if not impossible, to formulate a single, complete, measurement model such as Equation (1). Usually, a traceable measurement is perceived as a staged process, and it is difficult to describe when approached at more than one stage at a time. However, there is a mathematically equivalent formulation of these calculations that allows staged models to be handled. This formulation leads to a new abstract data type called an *uncertain number*, which can represent inexactly known quantities [4]. The uncertain-number format satisfies the requirements for information exchange identified in the GUM [3] [Section 0.4].

#### *2.2. The Uncertain-Number Methodology*

A mathematical expression may often be decomposed into stages and evaluated algorithmically as a sequence of basic operations. For instance, the following is the case:

$$V = v(1 - E\_{\rm rel}) - E\_{\rm off} - E\_{\rm rmd}$$

and can be broken into four stages (also shown in Figure 1):

$$\begin{aligned} y\_1 &= 1 - E\_{\text{rel}} \\ y\_2 &= v \times y\_1 \\ y\_3 &= y\_2 - E\_{\text{off}} \\ V = y\_4 &= y\_3 - E\_{\text{rad}} \cdot \end{aligned}$$

**Figure 1.** Decomposition of *V* = *v*(1 − *E*rel) − *E*off − *E*rnd into a sequence of arithmetic operations.

V

This approach can be applied to measurement models. The evaluation of some arbitrary function *f*(*x*1, ··· *xl*) can be decomposed into *h* = 1, ··· , *m* stages, each producing an intermediate result:

*yh* = *fh*(Λ*h*) , (6)

with the final stage yielding the result *y* = *ym*. The set of inputs to the *h*th stage function, denoted here as Λ*h*, may include previous stage results *y*1, ··· , *yh*−1 and model inputs *x*1, ··· *xl*. Using the chain rule for partial differentiation, the components of uncertainty

defined in Equation (4) can be evaluated at each stage. The component of uncertainty in *yh* due to uncertainty in the *j*th model input is as follows.

$$
\mu\_j(y\_h) = \sum\_{z\_k \in \Lambda\_h} \frac{\partial f\_h}{\partial z\_k} \mu\_j(z\_k) \,. \tag{7}
$$

Thus, the set of components of uncertainty {*u*1(*yh*), *u*2(*yh*), ··· , *ul*(*yh*)}, corresponding to {*x*1, *x*2, ··· , *xl*}, can be evaluated stage-by-stage to finally obtain the set of components of uncertainty in the result, {*u*1(*y*), *u*2(*y*), ··· , *ul*(*y*)} (when *j* = *k*, the notation *uj*(*zj*) may be simplified to *u*(*zj*), which is the standard uncertainty of model input *xj*).

In GTC, an uncertain number is used to encapsulate results at each stage (*yh*, and the associated components of uncertainty, {*u*1(*yh*), *u*2(*yh*), ···}). Uncertain numbers provide a convenient and succinct representation for quantities. Their algebraic properties essentially match those of ordinary number types. Thus, data processing algorithms can be expressed with familiar mathematical operations applied to uncertain-number terms representing quantities in a model. There is no need to derive the expressions for components of uncertainty; this is handled algorithmically.

The results of uncertain-number calculations are also transferable: the result of one calculation may be used as an argument in further calculations (as is performed routinely in numerical calculation). This is an open-ended process that can, in principle, continue indefinitely. The transferability of results is needed to support metrological traceability in staged measurement models. This will be discussed further in the next section and in Section 3.6. The open-ended nature of uncertain-number computations is also illustrated in the example shown in Section 2.4.

## *2.3. Traceability Chains and Uncertainty*

Traceability provides accurate and reliable information about physical quantities that can be used to inform decisions. Because a quantity of interest can never be determined exactly, a decision based on the information available may not be correct; there will be some uncertainty—in a colloquial sense—about the correctness of a decision informed by data subject to measurement error. However, the risks associated with poor decision outcomes can be managed if the unpredictability of measurement results can be described in probabilistic terms (i.e., if the accuracy can be quantified). In this sense, the metrologist's use of the term measurement uncertainty is associated with a likely magnitude of measurement error. To address the need for results that can be relied upon, metrological traceability requires the careful evaluation of measurement uncertainty.

Traceable measurement can be thought of as a collaborative process that is carried out in stages. Ultimately, a traceable measurement is of benefit to a nominal 'end user' at the last stage of a traceability chain, who needs information about a quantity to inform a decision (e.g., measuring the weights of shipping containers to inform the loading distribution of a container ship). The accuracy of a final result depends on all the stages; thus, the sources of uncertainty must be traced as far back as the units of measurement realised at the beginning of the process. This ensures that the result is meaningful and comparable with other traceable measurements of the same quantity.

While the GUM's expression of a measurement model takes the form of a single all-encompassing Equation (1), the staged formulation in Section 2.2 handles the fact that people involved at one stage generally do not have detailed knowledge about processes carried out at other stages. For example, Figure 2 shows a traceable measurement in four parts (e.g., stages 1 and 2 correspond to the realisation of reference standards, stage 3 to the calibration of a measuring instrument using those standards, and stage 4 to an end-user measurement using the calibrated instrument). The staged model is described as follows;

$$\begin{aligned} \mathbf{Y}\_1 &= f\_1(\cdot \cdot \cdot) \\ \mathbf{Y}\_2 &= f\_2(\cdot \cdot \cdot) \\ \mathbf{Y}\_3 &= f\_3(\mathbf{Y}\_{1\prime} \mathbf{Y}\_{2\prime} \cdot \cdot) \\ \mathbf{Y}\_4 &= f\_4(\mathbf{Y}\_{3\prime} \cdot \cdot) \end{aligned}$$

where unspecified arguments '··· ' represent some subset of the influence quantities *X*1, *X*2, ··· , *Xl*. The end user can probably only formulate a model for stage 4, *f*4(*Y*3, ···); thus, information about earlier stages must be summarised and reported down the chain in a suitable format. If the model was expressed as a single function, the composition of the stages would provide the following.

$$f(X\_1, X\_2, \dots, X\_l) = f\_4(f\_5(f\_1(\cdot \cdot \cdot), f\_2(\cdot \cdot \cdot), \cdot \cdot \cdot), \cdot \cdot \cdot) \dots$$

Now, the outcome of data processing should not be affected by the expression of the model as a single function or a series of functions. This has a bearing on how information should be communicated along a traceability chain [10]. By reporting uncertain numbers between stages, final results can be obtained that are the same as would be found for a single model. Uncertain numbers realise the GUM's ideal method for evaluating and expressing the uncertainty of a result [3] [Section 0.4].

**Figure 2.** A measurement carried out in four stages. Arrows indicate the transfer of information about intermediate results. The unspecified function arguments '··· ' represent external quantities that influence the procedures. This figure does not represent a particular measurement, but the four stages may be regarded as follows: realisation of unit reference standards (stages 1 and 2), calibration of an instrument using the standards (stage 3), and a measurement made with the calibrated instrument (stage 4).

#### *2.4. A Simple Example*

This section presents an example of uncertain-number data processing applied to a simple electrical network. Figure 3 shows an electrical network with three resistors in series. A voltmeter can be connected between the lower terminal and any of the three terminals above, allowing the potential difference between terminals 0 and 1, 0 and 2, or 0 and 3 to be measured (*V*10, *V*20, or *V*30, respectively).

**Figure 3.** Three resistors in an electrical network. A voltage measurement can be performed between the bottom terminal and any terminal above.

We adopt a simple model for an imperfect voltmeter. The model has three sources of error (influence quantities) that affect the response of a meter (reading) to an input voltage *V*. A random error (noise), represented as *E*rnd, affects every reading; a systematic error, *E*off, contributes a fixed offset to every reading; a systematic relative error, *E*rel, contributes an error proportional to the reading itself (representing imperfect scaling or non-linearity of the instrument). The relationship between the input voltage, *V*, and a voltmeter reading, *v*, is expressed by the model (already shown in Section 2.2).

$$V = \upsilon (1 - E\_{\text{rel}}) - E\_{\text{off}} - E\_{\text{rmd}} \,. \tag{8}$$

The influence quantities *E*rel, *E*off, and *E*rnd are unknown, and so their effects cannot be corrected. However, the displayed value *v* is used as an approximation for *V*, because we assume that the instrument is properly adjusted. This is the same as assuming that the residual errors are small enough to be considered approximately zero. The uncertainty in the value of *v*, due to the estimates *e*rel = *e*off = *e*rnd = 0, can be found if the uncertainties *u*(*e*rel), *u*(*e*off), and *u*(*e*rnd) are known.

For uncertain number objects representing inputs to a measurement model, we find it helpful to adopt the term *elementary uncertain number*. Elementary uncertain numbers represent influence quantities. Numeric data must be provided when defining elementary uncertain numbers during the problem initialisation phase; this includes the following: a value (the estimate), a standard uncertainty, and a number of degrees of freedom.

We can use GTC to evaluate properties of the circuit, given measured values and some information about the voltmeter's characteristics. Objects of the class Voltmeter, shown below, are used for data processing. During initialisation of a new Voltmeter (execution of \_\_init\_\_()), elementary uncertain numbers representing the two systematic errors are created and stored as instance variables (ureal() creates the uncertain numbers).

```
from GTC import ureal, rp, result
```

```
class Voltmeter(object):
def __init__(self,


**Author details**

Eman Abdelmohsen Sanad Faculty of Medicine, Department of Nephrology and Internal Medicine, Ain Shams University, Cairo, Egypt

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Effect of Intra-Dialytic Physical Exercise on Depression in Hemodialysis Patients DOI: http://dx.doi.org/10.5772/intechopen.113360*


**6.1 Cost function methods**

These methods share the definition of a proper cost function followed by the search of its minimum within the 3D volume of a grid with the indexes (k, l, m) in X (east), Y (north), and Z (upward) directions. The computational complexity comprises four nested loops, three over the grid dimensions (k, l, m) and one fourth over the number of stations that recorded particular data. We present three cost function methods based on each of the three data sets (PGV, Tp, Tps) described before. We take the liberty to name the various methods after scientists who defined the principles of the relevant cost functions.


4.1.3. Results for Scenario 3: Fire Flow

Figure 6 and Table 6 show the correlation between reliability indices and hydraulic measures according to the application of Scenario 3. In Scenario 3, node-assigned fire flow causes increment of flow and head loss along a specific flow path from the source to the node at which the fire occurred. As seen in Figure 6 and Table 6, overall, the correlation coefficients in Scenario 3 are less than those of the other two abnormal scenarios.

**Figure 6.** Scatter plots of Scenario 3 (*x*-axis—Reliability index; *y*-axis—Hydraulic measure). The plots highlighted in yellow show high correlation coefficient of 0.95 or higher.

**Table 6.** Correlation coefficients for Scenario 3.

Note: The values highlighted in yellow indicate high correlation coefficient of 0.95 or higher.

**Ser2 0.97 0.97 0.97** 0.91 0.90 0.88 0.94 0.92 **0.97**

Overall, the hydraulic indices (RI, MRI, and API) and mixed hydraulic index (NRI) show higher correlations (*R* values are in the range of 0.90–0.99) compared to other indices (in the range of 0.76–0.91). It is interesting to observe that RRI shows strong correlation with Red2 and Rob2 under Scenarios 1–3 (*R* values are always ranged in 0.94–0.98). It should be noted that Red2 and Rob2 measure the minimum pressure at critical nodes and RRI represents the resilience of the network while maintaining the minimum required head.

The topological indices (AD and NE), entropic index (FE), and a hydraulic-gradient based mixed index (PHRI) show relatively low correlation with the measures under the fire-flow scenario; none of them yield a correlation coefficient of greater than 0.95.


*2.9. Analysis of the Isotopic Composition of Honey Using 13C/12C Isotope-Ratio Mass Spectrometry Measurement*

One of the spectrometric methods used to detect the adulteration of honey with the addition of cane or corn sugar and the incorrect declaration of the origin of honey is isotope-ratio mass spectrometry (IRMS) analysis. Isotope content is related to latitude, i.e., the climate prevailing in the place where the honey is obtained. This method is based on the use of proportions of isotopes characteristic of particular plant species. Its task is to estimate the amount or ratio of isotopes of one of the three basic elements (13C/12C, 180/16O, 2H/1H) and compare them with standard values [119–123].

This is a method with great potential, but it requires expensive equipment and specialized staff. In addition, its universal application does not guarantee the correctness of the results [124].


**2. Results**

In our continuous search for new bioactive molecules, the six new imidazoliumcontaining alkaloids discolins C–H (**1**–**6**) and one pyridinium-containing alkaloid dispyridine A (**7**) were isolated from the marine-derived bacterium *T. discolor* sv11 (Figure 1). The antimicrobial activity of these new compounds was investigated, among which, compounds **1** and **3** exhibited moderate activity against Gram-positive *Bacillus subtilis* DSM10, *Mycobacterium smegmatis* ATCC607, *Listeria monocytogenes* DSM20600 and *Staphylococcus aureus* ATCC25923. In vivo and in vitro experiments indicated that phenethylamine, tryptamine and tyramine residues of the new alkaloids are derived from an enzymatic decarboxylation.

**Figure 1.** Discolin A and new compounds isolated from *T. discolor* sv11.

Compound **1** was obtained as a yellowish oil. The HR-ESI-MS spectrum of **1** showed a molecular formula of C26H32N3+ based on the prominent peak [M]+ at *m/z* 386.2606 (calculated 386.2591, Figure S1). The analysis of 1H NMR and HSQC spectra of **1** revealed three methyl groups at *δ*H 0.80 (H-8), *δ*H 2.15 (H-9) and *δ*H 2.20 (H-10), six methylene groups at *δ*H 1.34 (H-7), *δ*H 2.41 (H-6), *δ*H 2.80 (H-7--), *δ*H 3.08 (H-10-), *δ*H 4.22 (H-8--) and *δ*H 4.30 (H-11-), as well as ten aromatic protons that resonated from *δ*H 7.00 to *δ*H 7.39 (Table 1). These NMR data exhibited a high similarity with the previously reported compound discolin A that was also isolated from *T*. *discolor* sv11 [27]. Therefore, a core structure of the 4,5-dimethyl-2-propyl imidazolium skeleton of **1** was elucidated based on the COSY spin system from H-6 to H-7 to H-8, as well as on HMBC correlations from both H-6 and H-7 to C-2, and from both, H-9 and H-10 to C-4 and C-5. The same phenylethyl moiety as present in discolin A was deducted from compound **1** based on the COSY spin system between H-7-- and H-8--, as well as between five benzene ring protons 7.16 (2H, H-2-- and H-6--), 7.28 (H-4--) and *δ*H 7.32 (2H, H-3-- and H-5--), together with the core HMBC correlations from H-7-- to C-1-- and C-2--, and from H-8-- to C-1-- (Figure 2). The significant difference between compound **1** and discolin A are the HMBC correlations from a singlet aromatic proton resonating at *δ*H 7.17 (H-2-) to C-3-, C-4- and C-9-, from H-5- to C-3-, C-4-, C-7- and C-9- (Figure 2), as well as the COSY spin system from H-5- to H-6-, to H-7- to H-8-. These results suggested an indole moiety instead of a phenyl residue in compound **1**. Together with the remaining COSY spin system between the two methylene groups H-10- and H-11- and the HMBC correlations from H-10- to C-2-, C-3-, C-4- and C-11-, a 3-ethylindole moiety (C10H10N) was elucidated from compound **1**, which is further supported by the MS/MS fragment [C10H10N]+ detected at *m/z* 144.0813 (calculated 144.0813, Figure S1). With the HMBC correlations from H-8-- to C-2 and C-4,

and from H-11- to C-2 and C-5, the above mentioned phenylethyl moiety and 3-ethylindole were supposed to be located at position 3 and 1 of the imidazolium skeleton (Figure 2). This assumption was proven by 1H-15N HMBC correlations from H-6, H-9, H-7-- and H-8-- to N-3 and from H-6, H-10, H-10- and H-11- to N-1 (Figure 2). The N-atom at position 1 was considered to be positively charged based on the detected chemical shift at *δ*N 178.5, while N-3 was at *δ*N 177.3 (Figures S7 and S8) [29–31]. An additional NMR measurement with added trifluoracetic acid (TFA) in DMSO-*d6* (ratio 1:3) was carried out to further prove this conclusion (Figures S9–S13). The methylene groups of H-8-- and H-7-- shifted to up-field with a deviation Δ*δ*H-8-- value of 0.13 and Δ*δ*H-7-- value of 0.10 ppm, while the deviation Δ*δ*H-11- and Δ*δ*H-10- values were 0.09 and 0.05 ppm, respectively. The addition of TFA lead to the protonation of the tertiary N-atom at position 3, which gives a higher influence on the chemical shift [32]. The detected different chemical shift deviations Δ*δ*N-3 (155.1) and Δ*δ*N-1 (154.1) further support this result (Figures S7, S8 and S13). Thus, the structure of compound **1** was elucidated as shown in Figure 1 and named discolin C.

**Table 1.** 1H (700 MHz) and 13C (175 MHz) NMR data of compounds **1**–**3** (DMSO-*d6*, *δ* in ppm).

**a** 1H NMR data of compound **1** with TFA added. **b** Signals overlapped. **c** NH at 1-- for compound **3**.

**Figure 2.** Key HMBC and 1H-1H COSY correlations of compounds **1**–**7**.

Compound **2** was obtained as a yellowish oil. The molecular formula of **2** was determined to be C26H32ON3+ (*m/z* = 402.2543, [M]+, calcd. 402.2540, Figure S14) based on the HR-ESI-MS spectrum. Comprehensive comparison of NMR data of compounds **1** and **2** revealed the high similarity except for the chemical shift of C-4--, which was shifted from 126.96 to 156.48, and one missing aromatic proton. Together with the detected 16 Da increase in the HR-ESI-MS spectrum of compound **2**, this suggested the presence of a 4-hydroxyphenylethyl moiety located at position 3 instead of a phenylethyl moiety (Table 1). This assumption was confirmed by the upfield chemical shifts of the benzene ring protons at *δ*H 6.70 (2H, H-3-- and H-5--) and *δ*H 6.91 (2H, H-2-- and H-6--), which showed similar behavior as the reported 4-hydroxyphenylethyl-containing compound *N*-Acetyltyramine [33]. This effect is explained by the fact that the hydroxyl group is an electron donor, which shields the protons of the benzene nucleus more strongly and leads to an upfield shift of the corresponding signals. Second, the hydroxyl group that appeared at C-4-- in compound **2** changes the spin system of this radical, and therefore influenced the shape of the proton multiplets of the benzene nucleus. Furthermore, the COSY spin system between H-2-- and H-3--, as well as the HMBC correlations from H-7-- to C-1--, C-2--, from H-3-- to C-1--, C-4--, and from H-8-- to C-2, C-4 and C-1-- proved the 4-hydroxyphenylethyl group in compound **2**. The detected MS/MS fragment at *m/z* 282.1963 ([C18H23N3 + H]+, calcd. 282.1965, Figure S14) of compound **2**, which lost the 4-hydroxyphenylethyl group (-C8H9O), strongly indicated the above mentioned assumption. Thus, the structure of compound **2** was elucidated as shown in Figure 1 and named discolin D.

Compound **3** was also obtained as a yellowish oil. The molecular formula of **3** was determined to be C28H33N4+ (*m/z* = 425.2702, [M]+, calculated 425.2700, Figure S20) based on the HR-ESI-MS spectrum. The core scaffold of compound **3** shared the 4,5-dimethyl-2- propyl imidazolium skeleton with compound **1** as deduced from a comparison of both, 1D and 2D NMR data (Table 1), which also indicated compound **3** to be a symmetric structure. Integration of the proton signals in the 1H NMR spectrum together with the COSY spin system of the aromatic protons and the HMBC correlations from H-2- to C-3-, C-4- and C-9-, from H-10- to C-2-, C-4- and C-11-, as well as from H-11- to C-3-, C-10-, C-2, and C-5 proved that two identical 3-ethylindole moieties were connected to the central imidazolium ring as shown in Figure 2. Hence, compound **3** is a member of the discolin family and was named discolin E.

Compounds **4** and **5** were each obtained as a yellowish oil. The molecula formulae of compounds **4** and **5** were identified as C24H31ON2+ and C25H33ON2+, respectively, based on the HR-ESI-MS signals [M]+ at *m/z* = 363.2442 (calculated 363.2431, compound **4**, Figure S26) and *m/z* = 377.2593 (calculated 377.2587, compound **5**, Figure S32), respectively. One phenylethyl moiety, one 4-hydroxyphenylethyl moiety and the 4,5-dimethyl-2-propyl imidazolium skeleton were disclosed as constituents of compound **4** by comparing the 1D and 2D NMR data with those of compounds **1** and **2** (Tables 1 and 2). The phenylethyl moiety and the 4-hydroxyphenylethyl moiety were assigned to be located at positions 1 and 3 of the imidazolium skeleton of compound **4**, based on the HMBC correlations from H-8- to C-2 and C-5 and from H-8-- to C-2 and C-4 (Figure 2). In compound **5**, identical phenylethyl and 4-hydroxyphenylethyl moieties were assigned to be located at the same positions as in compound **4**. Comparing the 1D and 2D NMR data of compounds **4** and **5**, the only difference is one ethyl group present in compound **5**, while compound **4** carries a methyl group (Table 2). The presence of an ethyl group in compound **5** is corroborated by the COSY correlation between H-9 and H-10, and the HMBC correlations from both, H-9 and H-10 to C-4. In contrast, in compound **4**, the methyl group is directly connected to the unsaturated carbon C-4 (Figure 2), thus verifying the assumed structural relationship between compounds **4** and **5**. The 14 Da molecular weight difference between both compounds further supports their structural relationship. Thus, the structures of compounds **4** and **5** were elucidated as shown in Figure 1, and the names discolin F and discolin G were proposed, respectively.

**Table 2.** 1H (700 MHz) and 13C (175 MHz) NMR data of compounds **4**–**6** (DMSO-*d6*, *δ* in ppm).

**a** Deduced from HSQC and HMBC spectra.

Compound **6** was also obtained as a yellowish oil. The molecular formula of compound **6** was established as C23 H29 N2 + based on the prominent [M]+ peak in HR-ESI-MS spectrum at *m/z* = 333.2329 (calculated 333.2325, Figure S37). Two identical phenylethyl moieties were deduced from the NMR spectra of compound **6** and connected at positions 1 and 3 of the core ring based on the HMBC correlations from H-8- to C-2 and C-5, as well as from H-8-- to C-2 and C-4. The remaining signals of **6** were assigned to the 4,5-dimethyl-2-ethyl imidazolium scaffold, which showed an ethyl group rather than a propyl group at position 2. This difference was clarified by the COSY correlation between H-6 and H-7, as well as the HMBC correlations from both, H-6 and H-7 to C-2 (Figure 2). Therefore, compound **6** provedtobearepresentativeofthediscolinfamilyandwasnameddiscolinH.

Compound **7** was isolated as a colorless powder. Its molecular formula was established as C22 H29 N2 + based on the prominent ion peak [M]+ observed at *m/z* 321.2322 (calcd. 321.2325, Figure S43). Comprehensive analysis of 1D and 2D NMR data of compound **7** revealed one 3-ethylindole moiety as found in compounds **1**–**3**, as well as two ethyl groups and one propyl group (Table 3). The remaining two aromatic protons at *δ*H 8.20 (H-4) and *δ*H 8.40 (H-6) in the 1H NMR spectrum and five aromatic carbons at *δ*C 153.07 (C-2), *δ*C 142.58 (C-3), *δ*C 144.54 (C-4), *δ*C 140.61 (C-5) and *δ*C 142.62 (C-6) in the 13C NMR spectrum were attributed to a pyridinium ring as apparent from comparison with the data of dispyridine, a pyridinium-containing alkaloid isolated previously [27]. The location of the 3-ethylindole moiety was determined from the HMBC correlations from H-11- to C-2 and C-6, which also confirmed the location of the aromatic proton H-6 resonating at *δ*H 8.40. The second aromatic proton resonating at *δ*H 8.20 was attributed to position 4, based on the HMBC correlations from H-4 to C-2, C-3 and C-6. The propyl group and the two ethyl groups attached to the pyridinium ring were located at C-2, C-3 and C-5, as inferred from HMBC correlations from H-7 to C-2 and C-3, from H-10 to C-2, C-3 and C-4, and from H-12 to C-4, C-5 and C-6 (Figure 2). Therefore, compound **7** was found to be a new member of the dispyridine family and named dispyridine A.

 q

1.01, t (7.5)

**Table 3.** 1H (700 MHz) and 13C (175 MHz) NMR data of compound **7** (DMSO-*d6*, *δ* in ppm).

**a** Signal overlapped with H2O.

13

 13.9, CH3

Based on previous research, it was known that phenylalanine can be converted to phenethylamine by the catalytic action of the decarboxylase DisA. This molecule could serve as building block to yield different derivatives [27]. Hence, the newly isolated imidazolium-containing alkaloids (**1**–**6**) were also supposed to be produced via the same biosynthetic route, i.e., first an enzymatic decarboxylation of the aromatic-*L*-amino acid tryptophan or tyrosine yielding tryptamine or tyramine, respectively, followed by a nonenzymatic condensation to form the central imidazolium ring. To confirm this hypothesis, the candidate enzyme DisA from *T. discolor* sv11 was analyzed in vivo in a heterologous system. Therefore, the previously constructed transgenic host strain *E. coli* ROSETTA (disA) carrying the respective *disA* gene and *E. coli* ROSETTA (pRSF) (negative empty vector control) were cultivated in LB medium, whereby 2 mM tryptophan and tyrosine

were added as substrates, respectively. After 24 h incubation, tryptamine and tyramine were only detected in the extract of *E. coli* ROSETTA (disA), while only the substrates, i.e., tryptophan and tyrosine were detected in the negative control (Figure 3). To further validate these results, a His-tagged version of DisA was purified using affinity chromatography and assayed in vitro. This confirmed that tryptamine and tyramine can be obtained from tryptophan and tyrosine by a DisA-dependent catalytic conversion (Figure 3).

**Figure 3.** In vivo and in vitro decarboxylation of tryptophan and tyrosine catalyzed by DisA. (**A**) In vivo decarboxylation of tryptophan to tryptamine. Extracted ion chromatograms (EICs) of tryptophan (C11H13N2O2 205.0977 [M + H]+, in red) and tryptamine (C10H13N2 161.1079 [M + H]+, in blue). (**B**) In vivo decarboxylation of tyrosine to tyramine. Extracted ion chromatograms (EIC) of tyrosine (C9H12NO3 182.0817 [M + H]+, in red) and tyramine (C8H12NO 138.0919 [M + H]+, in blue). In A and B, the base peak chromatogram of the extracts is given in grey. (**C**) Comparative HPLC analysis of the in vitro decarboxylation of tryptophan. (**D**) Comparative HPLC analysis of the in vitro decarboxylation of tyrosine. In C and D, the UV absorbance at 254 nm is shown.

All isolated compounds **1**–**7** were investigated for their bioactivity against bacteria (*B. subtilis* DSM10, *M. smegmatis* ATCC607, *L. monocytogenes* DSM20600, *S. aureus* ATCC25923, and *E. coli* ATCC25922) and fungi (*Candida albicans* FH2173). As shown in Table 4, discolin C (**1**) showed activity against *M. smegmatis* ATCC607 and *B. subtilis* DSM10 with MIC values ranging from 4 μg/mL to 8 μg/mL and moderate to weak activity against *S. aureus* ATCC25923 and *L. monocytogenes* DSM20600 with MIC values ranging from 16 μg/mL to 32 μg/mL. Discolin E (**3**) exhibited activity against four tested Grampositive bacteria with MIC values ranging from 4 μg/mL to 8 μg/mL and moderate activity against *C. albicans* FH2173 with an MIC value of 16 μg/mL. The other compounds (**2**, **4**–**7**) were inactive against all the tested microorganisms in the range tested.

**Table 4.** MIC values (μg/mL) for Compounds **1**–**7**.

**a** Isoniazid was used as positive control. **b** Nystatin, tebuconazole and amphotericin B were used as positive controls.

#### **3. Conclusions and Discussion**

In conclusion, seven new alkaloids were obtained from the crude extract of *T. discolor* sv11 after fermentation in LB medium. In vivo and in vitro experiments proved the decarboxylase DisA to catalyze the decarboxylation of the aromatic-*L*-amino acids phenylalanine, tryptophan and tyrosine to phenethylamine, tryptamine and tyramine, respectively. These molecules serve as substrates for the formation of the central imidazolium ring of discolins A–H through a non-enzymatic condensation. Hence, by combining an enzymecatalyzed and a non-enzymatic reaction, the bacterium generates a mix of structurally related molecules. Besides the understanding of the biosynthetic mechanisms of the discolins, some insights into the structure–activity relationship of the antibacterial discolins A-H were also obtained [27]. Discolin A and discolin H feature the same molecular skeleton, except for the length of the carbon chain linked to C-2 of the central imidazolium ring. Both molecules showed the similar moderate bioactivity, which suggests that the substructure at position 2 can be altered without affecting the activity. The structural differences and changes in the bioactivity of discolins C-F and discolin A indicated that the substructures at position 1 and 3 of the central ring instead play an important role concerning antibacterial activity. Earlier evidence indicated that the activity of imidazolium salts is highly dependent upon the substituents on the nitrogen atoms of the imidazolium cation [34], which is in agreement with our observation. In summary, our finding, together with previous reports, clearly indicates that the genus *Tenacibaculum* exhibits a high potential to produce nitrogen-containing heterocycles with a unique structure and various biological activities. This includes positively charged imidazolium-containing natural products.

#### **4. Materials and Methods**

#### *4.1. General Experimental Procedures*

The 1D and 2D NMR spectra were recorded in DMSO-*d*6 using a Bruker Avance Neo 700 MHz spectrometer equipped with a 5 mm CryoProbe Prodigy TCI (1H,15N,13C Z-GRD) (Bruker, Ettlingen, Germany). The LC-HRMS data for new compounds were recorded on a micrOTOF-QII mass spectrometer (Bruker, Billerica, MA, USA) equipped with an ESI-source coupled to an Agilent Infinity 1290 UHPLC system using an ACQUITY UPLC BEH C18 Column, 130 Å, 1.7 μm, 2.1 mm×100 mm (Waters, Eschborn, Germany) with

an ACQUITY UPLC BEH C18 VanGuard Pre-column, 130 Å, 1.7 μm, 2.1 mm × 5 mm (Waters, Eschborn, Germany). HPLC was performed using a Shimadzu HPLC system (Shimadzu Deutschland GmbH, Duisburg, Germany) for analysis (EC 250/4.6 Nucleodur C18 Gravity-SB, 5 μm; Macherey-Nagel, Düren, Germany), and for semi-preparative purification (VP 250/10 Nucleodur C18 Gravity-SB, 5 μm; Macherey-Nagel, Düren, Germany). MPLC was performed on the Interchim Puriflash 4125 chromatography system (Interchim, Montluçon, France).

#### *4.2. Extraction and Isolation*

A fermentation (36 L) of *T. discolor* S11 was performed in 5 L flasks that contained 1.5 L of LB medium and were incubated at 30 ◦C and 140 rpm for 8 days, followed by an extraction using EtOAc (volume ratio 1:1) for three times, affording 11.9 g crude extract. Thirteen fractions (Fr. 1–13) were collected from reversed phase flash chromatography (Interchim Puriflash 4125 chromatography system with Puriflash C18-AQ30 μm F0120 column) with an elution gradient starting from 10% MeOH/H2O to 100% MeOH over 1.5 h. Fr. 11 (973.8 mg) was further subjected to size exclusion chromatography on a Sephadex LH-20 column and eluted with 100% MeOH to give 10 subfractions (Frr. 11.1–11.10). Frr. 11.4 (229.4 mg) was further subjected to reversed phase flash chromatography (Interchim Puriflash 4125 chromatography system with Puriflash C18-HP30 μm F0025 Flash column) using an elution gradient from 10% MeOH/H2O to 100% MeOH over 4 h to give 10 subfractions (Frrr. 11.4.1–11.4.10). Frrr. 11.4.6 was further purified by semi-preparative HPLC (0–1 min, 22% MeCN; 1–46 min, gradient increased from 22% to 37% MeCN) to yield compounds **1** (2.0 mg, *t*R = 43 min) and **3** (0.6 mg, *t*R = 45 min). Frrr. 11.4.5 was fractionated by semi-preparative HPLC (0–38.5 min, gradient increased from 10% to 46% MeOH) to give 6 subfractions (Frrrr. <IP_ADDRESS>–<IP_ADDRESS>). Frrrr. <IP_ADDRESS> was again purified by semipreparative HPLC (0–57 min, isocratic gradient with 29% MeOH) to yield compounds **2** (1.2 mg, *t*R = 48.5 min), **4** (2.1 mg, *t*R = 39 min) and **7** (1.5 mg, *t*R = 44.4 min). Purification of Frrrr. <IP_ADDRESS> by semi-preparative HPLC (0–5 min, 5% MeCN; 5–50 min, gradient increased from 5% to 35% MeCN) yielded compound **5** (0.3 mg, *t*R = 49.3 min). Compound **6** (0.3 mg, *t*R = 51.2 min) was obtained from Frrrr. <IP_ADDRESS> by semi-preparative HPLC (0–5 min, 5% MeCN; 5–56 min, gradient increased from 5% to 39% MeCN).

Discolin C (**1**): yellowish oil; the 1H NMR (DMSO-*d*6, 700 MHz) and 13C NMR (DMSO*d*6, 175 MHz) data are given in Table 1; HR-ESI-MS *m/z* 386.2606 [M]+ (calculated for C26 H32 N3 +, 386.2591, Figure S1).

Discolin D (**2**): yellowish oil; the 1H NMR (DMSO-*d*6, 700 MHz) and 13C NMR (DMSO*d*6, 175 MHz) data are given in Table 1; HR-ESI-MS *m/z* 402.2543 [M]+ (calculated for C26 H32ON3 +, 402.2540, Figure S14).

Discolin E (**3**): yellowish oil; the 1H NMR (DMSO-*d*6, 700 MHz) and 13C NMR (DMSO*d*6, 175 MHz) data are given in Table 1; HR-ESI-MS *m/z* 425.2702 [M]+ (calculated for C28 H33 N4 +, 425.2700, Figure S20).

Discolin F (**4**): yellowish oil; the 1H NMR (DMSO-*d*6, 700 MHz) and 13C NMR (DMSO*d*6, 175 MHz) data are given in Table 2; HR-ESI-MS *m/z* 363.2442 [M]+ (calculated for C24 H31ON2 +, 363.2431, Figure S26).

Discolin G (**5**): yellowish oil; the 1H NMR (DMSO-*d*6, 700 MHz) and 13C NMR (DMSO*d*6, 175 MHz) data are given in Table 2; HR-ESI-MS *m/z* 377.2593 [M]+ (calculated for C25 H33ON2 +, 377.2587, Figure S32).

Discolin H (**6**): yellowish oil; the 1H NMR (DMSO-*d*6, 700 MHz) and 13C NMR (DMSO-*d*6, 175 MHz) data are given in Table 2; HR-ESI-MS *m/z* 333.2329 [M]+ (calculated for C23 H29 N2 +, 333.2325, Figure S37).

Dispyridine A (**7**): colorless powder; the 1H NMR (DMSO-*d*6, 700 MHz) and 13C NMR (DMSO-*d*6, 175 MHz) data are given in Table 3; HR-ESI-MS *m/z* 321.2322 [M]+ (calculated for C22 H29 N2 +, 321.2325, Figure S43).

#### *4.3. Enzymatic Activity of Dis A*

To investigate the enzymatic activity of Dis A in vivo, *E. coli* ROSETTA (disA) was cultured in 30 mL kanamycin-containing (50 μg mL−1) LB medium at 30 ◦C overnight as pre-culture. A volume of 100 μL of this pre-culture was used to inoculate at 37 ◦C in two 300 mL Erlenmeyer flasks with 100 mL kanamycin-containing (50 μg mL−1) LB medium; 0.1 mM IPTG was added into the medium when the cultures reached an OD600 of 0.5 and were cultured at 30 ◦C for 3 h. Then, 2 mM tryptophan or tyrosine were added to the medium and cultured at 30 ◦C overnight. Next, 2 mL medium was harvested, dried *in vacuo*, re-dissolved in 200 μL DMSO and analyzed by UPLC-HRMS. The *E. coli* ROSETTA strain harboring the empty vector pRSF without the target *disA* gene was cultivated under the same conditions and analyzed by UPLC-HRMS as the negative control.

An in vitro enzymatic characterization was carried out after the purification of the His-tagged DisA. An inoculum of 15 mL of same pre-culture prepared for in vivo assay was used to inoculate 1.5 L kanamycin-containing (50 μg mL−1) LB medium; 0.1 mM IPTG was added to the medium when the cultures reached an OD600 of 0.5 and were cultured overnight. Cells were collected by centrifugation at 4 ◦C with 10,000 rpm and resuspended in lysis buffer (50 mM NaH2PO4, 300 mM NaCl and 10 mM imidazole; pH 8.0). The resulting suspensions were sonicated and centrifuged at 4 ◦C at maximum speed for 30 min. The supernatant was loaded onto a pre-equilibrated 750 μL Qiagen® Ni-NTA column. After washing with a 3 mL lysis buffer and 3 mL wash buffer (20 mM imidazole lysis buffer), the His-tagged protein DisA was eluted from the column using an elution buffer (250 mM imidazole lysis buffer) (Figure S49). The protein was resuspended into an imidazole-free buffer (50 mM NaH2PO4, 300 mM NaCl; pH 8.0) and concentrated using the Amicon® Ultra-15 centrifugation membrane column.

Enzymatic reactions were performed in 50 mM lysis buffer without imidazole (50 mM NaH2PO4, 300 mM NaCl, pH 8.0), containing 100 μM tryptophan (or 20 μM tyrosine) and 5 μM DisA in a total volume of 0.5 mL. After incubation at 30 ◦C overnight, the same volume of MeOH was added to quench the reactions. The reaction mixture was then centrifuged and the supernatant was dried and re-dissolved in 50 μL 50% MeOH and analyzed by analytical HPLC (0–16 min, 5% MeCN; 16–26 min, gradient increased from 5% to 100% MeCN).

## *4.4. Bioactivity Tests*

Determination of the minimum inhibitory concentration (MIC) of purified compounds **1**–**7** was carried out by micro broth dilution assays in 96 well plates as described previously [27]. All compounds were dissolved in dimethyl sulfoxide (DMSO, Carl Roth GmbH + Co., Karlsruhe, Germany) with a concentration of 3.2 mg/mL and tested in triplicate. Dilution series (64 −0.03 μg/mL) of rifampicin, tetracycline, and gentamicin (all Sigma-Aldrich, St. Louis, MS, USA) were prepared as positive controls for *B. subtilis* DSM10, *L. monocytogenes* DSM20600, *S. aureus* ATCC25923, and *E. coli* ATCC25922. Same dilution series of rifampicin, tetracycline, and isoniazid for *M. smegmatis* ATCC607. For fungi (*C. albicans* FH2173), tebuconazole (Cayman Chemical Company, Ann Arbor, MI, USA.), amphotericin B (Sigma-Aldrich, St. Louis, MS, USA) and nystatin (Sigma-Aldrich, St. Louis, MS, USA) were used as the positive control with same dilution series.

**Supplementary Materials:** The supporting information is available free of charge at: https://www. mdpi.com/article/10.3390/md20100620/s1, Figures S1–S48: HR-ESI-MS, HR-ESI-MS/MS and NMR spectra for compounds 1–7; Figure S49: SDS-PAGE gel of the purified His-tagged DisA (PDF).

**Author Contributions:** Conceptualization, L.W. and T.F.S.; methodology, L.W. and M.M.; data analysis, L.W., M.M., U.M. and Y.L.; writing—original draft preparation, L.W. and Y.L.; writing— review and editing, all authors; visualization, L.W.; supervision, project administration, and funding acquisition, T.F.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** L.W. was funded by the China Scholarship Council (CSC NO.<PHONE_NUMBER>77). The Schäberle lab is part of the German Center of Infection Research (DZIF) and supported by the Hessen State Ministry of Higher Education, Research and the Arts (HMWK) via the LOEWE Center for Insect Biotechnology and Bioresources.

**Acknowledgments:** The authors would like to thank Heike Hausmann (Justus-Liebig-University Giessen, Germany) for measuring NMR spectra for structure elucidation.

**Conflicts of Interest:** The authors declare no conflict of interest.