esa-sceva/satcom-chunk-collection huggingface dataset:
# RBC Storage Lesion Studies in Humans and Experimental Models of Shock
###### Abstract  
We have performed a series of studies over the course of a decade with a large animal model of shock and meta-analysis techniques to study transfusion trials. Although clinical trials have established the safety of the current practice of transfusing 18- to 22-day-old stored blood, observational studies have suggested that the oldest units (aged 35-42 days) when transfused have increased risks. These risks are dependent on host factors. Transfused older blood has increased hemolysis with the release of cell free hemoglobin (CFH) and iron. Subjects with infection may be harmed by transfusing iron which promotes bacterial growth. In situations where nitric oxide (NO) is important, such as coronary disease, CFH scavenging of NO may increase risk to patients.  
b 22 January 20204 March 2020204 March 202  
in cold storage [2; 3; 4; 5], this article will focus on the insights gained by translational research undertaken by our lab over a decade in a large animal model of shock on the effects of blood transfusion during states of critical illness. These translational animal studies and clinical meta-analytical techniques have given us valuable insights into the pathophysiology of RBC transfusion. RBCs undergo extensive physical and metabolic changes while in cold storage that constitute \"storage lesion(s)\" [6; 7]. Early investigators settled on in vivo RBC recovery and survival at the conclusion of the allowable storage period of 42 days as the key measures of quality. Neither of these surrogate measures of RBC quality has been correlated with patient outcomes. The US Food and Drug Administration (FDA) licenses laboratories that store and distribute RBCs, containers and storage solutions. To date, for licensing, the FDA still relies primarily on these two surrogate measures of efficacy and safety: 24-h survival of \(>\)75% of radiochromium-labelled RBCs, and hemolysis of \(<\)1% in the storage bag at the end of storage. We sought to determine if transfusion of longer-stored RBCs altered outcomes in critically ill subjects.  
We reasoned that clinical trials of transfusing the longest vs. shortest stored RBCs to patients in specific clinical states would pose logistical and ethical problems so we focused on adapting canine models of critical illness (hemorrhagic and septic shock). To minimize unintentional bias, we used mortality as an end point to the RBC transfusion setting. We believe such a model was capable of elucidating any potential risks of transfusing longer-stored RBCs, and if such a risk existed, would allow us to define potential biomarkers of that risk in stored RBCs, explore the mechanism(s) of injury, and test potential interventions.
# Optimization of Multilayer Absorbers Using the Bald Eagle Optimization Algorithm
## 3 Results
### Normal Incidence
#### 3.1.3 Design-3  
Five-layer microwave absorbers were optimized for a 2-18 GHz FR for normal incidence (Design-3). The materials and thicknesses of layers were optimized to minimize the RC in this FR. Optimization was carried out by scanning the FR with 0.1 GHz frequency increments.  
The optimized design parameters are compared with similar designs in the literature (Table 4). The maximum RC for the proposed design is \(-17\) dB and the overall total thickness is 3.20 m. The visual comparison of the RC versus FR is also given in Figure 7.
## 3 Numerical Modeling of Concrete Beams Reinforced with SFCB  
### Fiber Element Modeling Method  
The analyses were conducted using Open System for Earthquake Engineering Simulation (OpenSees) software [21]. This software has advanced capabilities for modeling and analyzing the nonlinear response of systems using a wide range of material models, elements, and solution algorithms. OpenSees software is stable and suitable for simulating beams under monotonic or cyclic lateral loading. This software has an important feature that includes many materials in it. Furthermore, fiber-based finite element analysis is the most economical and accurate method for modeling the nonlinear global response of RC structures [22; 23; 24]. In the fiber-based finite element method, the element sections are discretized by uniaxial fiber elements, to which corresponding material constitutive laws are assigned in the form of uniaxial stress-strain relationships. The fibers shorten or elongate so that plane sections remain plane after deformation.  
Each frame beam was modeled as one nonlinear Euler Bernoulli beam-column element with five integration points, which was discretized by uniaxial fiber elements. the element model based on the displacement method, namely the \"Disp Beamcolumn element\" model in OpenSees. The element allows stiffness change along the length direction of the element, it obtains resistance and the top displacement of components through the integration of the node displacement. Considering the test situation, the component end is set as a fixed end, regardless of the torsional deformation of the component, and the specific model setting is shown in Figure 11. Figure 11a shows the fiber element model of the beam. Figure 11b presents a discretized fiber section for a typical strengthened beam, including concrete fibers, steel fibers, and composite bar fibers, which were assigned with corresponding measured material properties. The lateral load was applied using the imposed displacement, and P-Delta effects were included in the analyses. Three algorithms, namely [PERSON] algorithm, Broyden algorithm and Newton-Line-Search algorithm were adopted successively for nonlinear solution; and the tolerance was defined as 1.0 \(\times\) 10\({}^{-6}\) in OpenSees.  
Figure 10: Load-displacement curves (test value). (**a**) S12B43 beam; (**b**) S12B97 beam.
## 2 Mathematical Modelling of Propagating Signal
### Modelling the Acoustic Signal  
For signal modelling, we begin with the acoustic wave equation,  
\[c^{2}\frac{\partial^{2}\textbf{p}}{\partial x^{2}}=\frac{\partial^{2}\textbf{ p}}{\partial t^{2}} \tag{3}\]  
where **p** is the acoustic pressure, \(x\) is the direction of the signal propagation, and \(c=1480\) m/s is the velocity of the acoustic wave through water. In (3), **p** can be expressed as  
\[\textbf{p}=f_{1}(y+ct)+f_{2}(y-ct) \tag{4}\]  
with amplitude \(\alpha\), wave number \(k\) (where \(k=2\pi/\lambda\) and \(\lambda\) is the signal wavelength, and rearranging (4),  
\[y=\frac{1}{k}\cos^{-1}\left(\frac{\textbf{p}}{\alpha}\right)+ct \tag{5}\]  
and inserting (5) in (2), we obtain  
\[\ddot{\textbf{p}}\frac{\textbf{p}^{2}}{\alpha^{2}}-\textbf{p}(\dot{\textbf{p} })^{2}+\beta(t)\dot{\textbf{p}}\frac{\textbf{p}^{2}}{\alpha^{2}}-\beta(t)ck \textbf{p}-\omega^{2}(t)\textbf{p}\cos^{-1}\left(\frac{\textbf{p}}{\alpha} \right)-\omega^{2}(t)ctk\textbf{p}=0. \tag{6}\]  
If \(y=ct\) satisfies (6), we can write the so-called [PERSON]'s equation [20] with \(\textbf{p}=\alpha\) to obtain  
\[\ddot{\textbf{p}}-\textbf{p}(\dot{\textbf{p}})^{2}+\beta(t)\dot{\textbf{p}}- \beta(t)ck\textbf{p}-\omega^{2}(t)ctk\textbf{p}=0. \tag{7}\]Here, (7) is the well-known Milne oscillator equation. Essentially, (7) represents the evolution of the acoustic pressure field with time and can be solved for \(\mathbf{p}\) to calculate the effective receive signal strength (Milne energy), effective phase shift, and time-delay experienced by the travelling signal.
# Large-Scale Multimodal Piano Music Identification Using Marketplace Fingerprinting +
## 4 Discussion
### Database Coverage  
The third analysis is to characterize how well the fingerprint database covers the IMSLP dataset. A single column in a bootleg score is considered \"covered\" if there is at least one n-gram fingerprint in the database anchored at that position. Coverage for a single PDF file is defined as the proportion of covered positions in the corresponding bootleg score. Each curve in Figure 7 shows the coverage for all piano PDF files in IMSLP, where the PDFs are sorted from lowest coverage (left) to highest coverage (right). There are five curves corresponding to five different systems: the marketplace system with 128 GB RAM (solid green), 64 GB RAM (dashed green), and 32 GB RAM (dotted green), as well as the full dynamic n-gram system with 128 GB RAM under two different settings of its system hyperparameter \(\gamma\) (solid blue and dashed blue). The figure excludes PDFs in IMSLP that had no bootleg score information. Note that the x-axis is on a log scale.  
There are two things to notice about Figure 7. First, the marketplace database with 128 GB RAM has much better coverage than the dynamic n-gram system. This can be seen from the fact that the solid green curve is well above the solid blue curve across the entire x-axis range. This does not necessarily mean that every single PDF has better coverage under the marketplace system (since the two curves have different orderings of the PDFs), but it does suggest that the marketplace system generally improves coverage across the entire database and not just on a select subset of pieces. Second, we notice that having more memory does lead to substantially better coverage, particularly of poorly represented parts of the database. This can be seen from the fact that the three green curves have substantial separation between them, with the separation being largest at the leftmost part of the curve and smallest at the rightmost part of the curve. This suggests that having more memory improves coverage the most for PDFs with the lowest coverage. Based on these observations, we would expect the marketplace system to show improved retrieval accuracy on more diverse test sets as database size increases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DB Size** & **Dynamic n-Gram** & **Marketplace** \\ \hline
16 GB & 0.709 & 0.840 \\
32 GB & 0.858 & 0.876 \\
48 GB & 0.858 & 0.887 \\
64 GB & 0.854 & 0.896 \\
80 GB & 0.854 & 0.900 \\
96 GB & 0.854 & 0.903 \\
128 GB & 0.853 & 0.905 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The effect of database size on retrieval accuracy (MRR) for the dynamic n-gram and marketplace systems.
# Sustainable Operation of Active Distribution Networks
## 3 Agents' Modeling
### Local Agents  
3.1.1. Building Thermal Load Agent (BTL/A)  
BTL/A calculates the internal temperature of each residential building it supervises over the optimization horizon according to the thermal model described below [16].  
\[\rho\cdot C_{b}\cdot V_{b}\cdot\frac{dT_{in,b}}{dt}=\dot{Q}_{ex,wall,b}+\dot{Q} _{in,wall,b}+\dot{Q}_{win,b}+Q_{in,b}+\dot{Q}_{sw,b}+\dot{Q}_{sg,b}-Q_{HVAC,b} \tag{1}\]  
With  
\[\dot{Q}_{ex,wall,b}=\sum_{y\in\epsilon}U_{wall,y}\cdot F_{wall,y^{\prime}}(T_ {out}-T_{in,b}) \tag{2}\]  
\[\dot{Q}_{win,b}=\sum_{y\in\epsilon}U_{win,y^{\prime}}F_{win,y^{\prime}}(T_{ out}-T_{in,b}) \tag{3}\]  
\[\dot{Q}_{sw,b}=\sum_{y\in\epsilon}a_{w}\cdot R_{se}\cdot U_{wall,y^{\prime}} \cdot F_{wall,y^{\prime}}\cdot I_{T,b} \tag{4}\]  
\[\dot{Q}_{sg,b}=\sum_{y\in\epsilon}v_{win}\cdot\text{SC}\cdot F_{win,y^{\prime }}\cdot I_{T,b} \tag{5}\]  
\[\dot{Q}_{in,wall,z}=\sum_{x\in\mathcal{I}}U_{wall,x}\cdot F_{wall,x^{\prime} }(T_{in,nz}-T_{in,z}) \tag{6}\]  
where the \(b\) index denotes the building's number; the \(y\) index denotes the \(y\)th external wall/window; \(\epsilon\) is the external walls set \(\tau_{win}\) is the window glass transmission coefficient; \(a_{w}\) is the external wall absorbance coefficient; \(C_{b}\) is the specific heat capacity; \(F_{wall(win)}\)is the wall (window) surface area; \(I_{T,b}\) is the total solar radiation on the \(b\)th building; \(\rho\) denotes the air density; \(Q_{HVAC,b}\) denotes the power production for the cooling of the \(b\)th building; \(\dot{Q}_{ex,mall,b}\) denotes the external walls' heat exchange of the \(b\)th building; \(Q_{in,b}\) denotes the internal heat gains of the \(b\)th building; \(\dot{Q}_{sg,b}\) denotes the solar radiation through the windows of the \(b\)th building; \(\dot{Q}_{sw,b}\) denotes the heat gain by the external walls' solar radiation of the bth building; \(\dot{Q}_{win,b}\) denotes the heat transfer across the windows of the bth building; \(R_{se}\) is the heat resistance of the external surface; \(T_{in,b}\) denotes the bth building's internal temperature (\({}^{\circ}\)C); \(T_{out}\) denotes the bth building's outdoor temperature (\({}^{\circ}\)C); \(SC\) denotes the shading coefficient of the windows; and \(U_{null(win)}\) denotes the factor of the heat exchange of the external wall (window).  
The estimated indoor temperature of the building was used with its upper and lower limits to suitably define the flexibility of each building to increase (\(FL_{b}^{\uparrow}\)) or decrease (\(FL_{b}^{\downarrow}\)) its thermal power demand, as in the following equations.  
\[FL_{b}^{\uparrow}(t)=\frac{T_{in,b}(t)-T_{min,b}}{T_{max,b}-T_{min,b}} \tag{7}\]  
\[FL_{b}^{\downarrow}(t)=\frac{T_{max,b}-T_{in,b}(t)}{T_{max,b}-T_{min,b}} \tag{8}\]
# Performance Analysis of Multihop Underground Magnetic Induction Communication
## 4 Numerical Results  
In this section, we summarise the results of the analytical model proposed in Section 3 for an MI-WUSN system's performance evaluation. We present the end-to-end system performance for single-hop and multihop MI-WUSNs under assumptions of similar and different channel states determined by the noise power. A numerical analysis was performed using the system model presented in Section 2 and the system performance analysis methodology proposed in Section 3. The default values for the evaluation parameters listed in Table 2 have been adapted from [14] since they have been extensively tested and used in experiments [29] and testbeds [36] alike. All the coils used in the proposed system, i.e., transmitter, relay and receiver, have the same size and dimension for the sake of consistency. To overcome the proximity effect of the coils placed in a close vicinity, a small operational frequency of 10 MHz is used with small number of turns, i.e., 5. Though, this limits the achievable system bandwidth to 2 kHz, this is enough for the practical sensing and monitoring applications like disaster or breach detection in mines or borders, and smart farming applications to sense the water content. Processing on the data can be done on cloud using the external gateways connected at the receiver to maximize the battery lifetime of the overall system, since battery replacement or node re-deployment are challenging and costly operations for MI-WUSN.  
We analyse the system using the approach shown in Figure 4.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Parameter**}} & \multicolumn{2}{c}{**2 Weeks**} & \multicolumn{2}{c}{**4 Weeks**} & \multicolumn{2}{c}{**6 Weeks**} & \multicolumn{2}{c}{**_p-Value_**} \\ \cline{2-7}  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **ANOVA \(\pm\) SD** & **ANOVA \(\pm\) SD** & **ANOVA \(\pm\) \(\sigma\)** & **Duncan Test 2 vs. \(\ell\)** & **4 vs. \(\ell\)** \\ \hline Red wine intake (g/kg body weight) & 52.43 \(\pm\) 13.7 \({}^{\rm\,a}\) & 47.31 \(\pm\) 9.13.3  & \(<\)0.05 & NS & \(<\)0.05 & NS \\ Ethanol intake (g/kg body weight) & 5.46 \(\pm\) 1.43 \({}^{\rm\,a}\) & 4.93 \(\pm\) 0.95 \({}^{\rm\,a}\) & 3.69 \(\pm\) 0.61 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ TPA (mg/kg body weight) & 0.78 \(\pm\) 0.20 & 0.71 \(\pm\) 0.14 & 0.53 \(\pm\) 0.09 \({}^{\rm\,a}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ CA (mg/kg body weight) & 0.017 \(\pm\) 0.00 \({}^{\rm\,a}\) & 0.015 \(\pm\) 0.00 \({}^{\rm\,a}\) & 0.011 \(\pm\) 0.00 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ FA (mg/kg body weight) & 0.07 \(\pm\) 0.02 \({}^{\rm\,a}\) & 0.06 \(\pm\) 0.01 \({}^{\rm\,a}\) & 0.04 \(\pm\) 0.01 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ HRA (mg/kg body weight) & 0.54 \(\pm\) 0.14 \({}^{\rm\,a}\) & 0.49 \(\pm\) 0.09 \({}^{\rm\,a}\) & 0.37 \(\pm\) 0.06 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ SA (mg/kg body weight) & 0.009 \(\pm\) 0.00 & 0.009 \(\pm\) 0.00 & 0.006 \(\pm\) 0.00 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ PCA (mg/kg body weight) & 0.15 \(\pm\) 0.04 \({}^{\rm\,a}\) & 0.13 \(\pm\) 0.03 \({}^{\rm\,a}\) & 0.09 \(\pm\) 0.02 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ TA (mg/kg body weight) & 3.29 \(\pm\) 0.98 \({}^{\rm\,a}\) & 3.03 \(\pm\) 0.65 \({}^{\rm\,a}\) & 1.72 \(\pm\) 0.32 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & \(<\)0.01 \\ C (mg/kg body weight) & 0.07 \(\pm\) 0.02 \({}^{\rm\,a}\) & 0.06 \(\pm\) 0.01 \({}^{\rm\,a}\) & 0.04 \(\pm\) 0.01 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ C3G (mg/kg body weight) & 0.39 \(\pm\) 0.10 \({}^{\rm\,a}\) & 0.35 \(\pm\) 0.07 \({}^{\rm\,a}\) & 0.26 \(\pm\) 0.04 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ C3R (mg/kg body weight) & 0.06 \(\pm\) 0.02 \({}^{\rm\,a}\) & 0.05 \(\pm\) 0.01 \({}^{\rm\,a}\) & 0.04 \(\pm\) 0.01 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ D (mg/kg body weight) & 0.16 \(\pm\) 0.04 \({}^{\rm\,a}\) & 0.15 \(\pm\) 0.03 \({}^{\rm\,a}\) & 0.11 \(\pm\) 0.02 \({}^{\rm\,b}\) & \(<\)0.05 & NS & \(<\)0.05 & NS \\ P (mg/kg body weight) & 2.62 \(\pm\) 0.81 \({}^{\rm\,a}\) & 2.41 \(\pm\) 0.55 \({}^{\rm\,a}\) & 1.26 \(\pm\) 0.24 \({}^{\rm\,b}\) & \(<\)0.01 & NS & \(<\)0.01 & \(<\)0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Daily intake of red wine, ethanol, phenolic acids, and anthocyanins from wine, recalculated per kg of body weight, assessed for the studied rats during the experiment.
## 3 Results
### Experimental Setup  
The aim of a set of tests was to check the performance of the algorithm for map-matching. The measure of performance was the error in location. We carried out three different tests, each of which had two trials. At the beginning of the tests, we established a set of points with known coordinates. The goal of this set of coordinates was a quantitative comparison of the positions obtained from INS and the positions corrected by our map-matching algorithm.  
The tests consist in moving an individual along the selected path, stopping at designated points, recording the position indicated by INS and moving to the next point. Then, the error was calculated as the relative distance of points indicated by INS and the real coordinates. The tests were repeated for locations with and without the collision avoidance velocity-based map matching algorithm. The consecutive tests were organized as follows:  
**Test I.** The individual leaves the anchor at the starting point and starts to move along the designated path, through the middle of the corridor. The individual has to stop successively at five designated points with known coordinates. We record the position indicated by INS at each of these points. During the return to the starting point, the individual repeats the same activities as shown in Figure 8.  
Figure 8: Marked stop points (known coordinates) for the path in Test I.  
**Test II.** The individual leaves the anchor at the starting point and starts to move along the designated path, close to one wall of the corridor. The individual stops successively at five designated points with known coordinates. The position received from INS is recorded. During the return to the starting point, the individual has to move along the second, opposite wall of the corridor and repeats the same activities for the next five points. The main aim of this test was to check how our algorithm works in close proximity to the walls. Figure 9a,b illustrate Test II.  
**Test III.** The aim of the last test was to check the navigation system with transition to other compartments. The path was selected to cross doors. The individual leaves the anchor at the starting point and starts to move along the designated path. The individual stops successively at five designated points with known coordinates. The position returned by INS is recorded. During the return to the starting point, the individual enters another compartment and stops at seven additional points with known coordinates, as shown in Figure 10a,b.  
**Figure 10**: Test III. (**a**) Marked stop points for the path in Test III; (**b**) The movement across the furnished compartment during Test III.  
Figure 9: Test II. (**a**) Marked stop points for the path in Test II; (**b**) The movement along the wall during Test II.  
The test results were collected in a form, as is presented in Table 1.  
The first column is the number of steps taken by an individual from the starting point. The second column contains the real coordinates of check points. The third column is the position indicated by INS. For each of the tests, we collected the data in such a table.
## 4 Conclusions  
The SPMESH sheet approach yielded improvements in throughput and automation. Even with a 1 h equilibration, we could prepare, extract and analyze 24 samples from grapes to data in less than 3 h. This is a considerable improvement over SPME-GC-MS which, at best, could analyze 24 samples in 12 h not including sample preparation time. DART-MS analysis time can be further reduced by increasing positioning stage scanning speed, as demonstrated earlier [18]. This approach would result in a loss of sensitivity but may be appropriate for higher concentration targets like lanalool in Muscat-type grapes, where concentrations are in excess of the methodological detection limit.  
SPMESH sheet-DART-MS was used to measure two quality-related volatiles (linalool and IBMP) at native concentrations in a broad range of grape samples. Good correlations between SPMESH sheet-DART-MS and SPME-GC-MS were observed (R\({}^{2}\) = 0.66-0.70). The SPMESH approach required 10-fold less instrument time than SPME-GC-MS. During SPMESH analyses, an offline heating and agitation step was necessary to equilibrate native IBMP with the labeled standard and achieve good equilibration. The use of tandem MS improved the detection limit for IBMP by an order of magnitude, to 4 ng/L, as compared to previous SPMESH-DART-MS work. This work demonstrates the suitability of SPMESH sheet-DART-MS as a rapid benchmarking platform for trace level IBMP and low-level lanalool. The technique should also be amenable for adoption for analysis of volatile markers in either foods or beverages--especially those volatiles that are amenable to DART-MS analysis (e.g., can undergo proton-transfer reactions).  
The following are available online at [[http://www.mdpi.com/2304-8158/9/4/409/s1](http://www.mdpi.com/2304-8158/9/4/409/s1)]([http://www.mdpi.com/2304-8158/9/4/409/s1](http://www.mdpi.com/2304-8158/9/4/409/s1)), Figure S1: Correlations plots of lanalool by SPMESH-DART-MS vs. summed \(m\)/z = 137 peaks by SPME-GC-MS  
**Author Contributions**: Conceptualization, N.K.D.; Data curation, M.Y.B.-D. and B.S.P.; Formal analysis, M.Y.B.-D. and B.S.P.; Funding acquisition, G.L.S.; Investigation, M.Y.B.-D. and B.S.P.; Methodology, M.Y.B.-D. and G.L.S.; Resources, N.K.D. and G.L.S.; Supervision, N.K.D. and G.L.S.; Validation, M.Y.B.-D.; Writing--original draft, M.Y.B.-D.; Writing--review & editing, M.Y.B.-D., H.F., B.S.P., N.K.D. and G.L.S. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Correlation between lanalool equivalents measured by SPMESH sheet-DART-MS, compared to SPME-GC-MS (solid phase microextraction-gas chromatography-mass spectrometry). Shaded area represents the 95% confidence interval of the linear regression.  
**Funding:** This project was funded by E&J Gallo Winery, the NY Wine Grape Foundation Enology Program (I-34), and Agriculture and Food Research Initiative Award 2017-67007-25940 from the USDA National Institute of Food and Agriculture.  
**Acknowledgments:** We acknowledge [PERSON] for his assistance with the Orbitrap HRMS.  
**Conflicts of Interest:** The authors declare no conflict of interest.