esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Significant Wave Height Results from SWAN
Consequently, in the comparison of significant wave height, the extreme values from the FNL-driven simulation are higher than those from the ERA5-driven simulation. Figure 14: Comparison of Significant Wave Height Simulations in Typhoon Neperatk. (**a**) shows the WRF simulation driven by FNL, (**b**) shows the WRF simulation driven by ERA5, (**c**) shows the empirical parameterized typhoon model Holland, and (**d**) shows the empirical parameterized typhoon model Jelesnianski. Figure 13: The location of the reference point near the coast, and the time series of significant wave height at this point for Typhoon Surigae, the red point is the location of the reference point, the red line represents the FNL-driven WRF simulation, the blue line represents the ERA5-driven WRF simulation, the green line corresponds to the Holland case, and the yellow line corresponds to the Jelesnianski case. A reference point was selected to extract the time series of significant wave heights. Figure 15 presents the simulation results for Typhoon Nepartak. Throughout the entire time series, the significant wave height at the reference point simulated by the Holland empirical parameterized typhoon model is consistently higher than that of the Jelesnianski model. This is similarly attributed to the smaller maximum wind radius range in the Jelesnianski model compared to the Holland model. Additionally, the peak significant wave heights simulated by both the Holland and Jelesnianski models are noticeably higher than those simulated using FNL- and ERA5-driven WRF. This is because the reference point is closer to the typhoon's central path, where the advantage of empirical parameterized typhoon models in simulating extreme wind speeds becomes dominant.
## 2 Materials and Methods
### ROS Analysis  
Intracellular ROS production as a consequence of EMFs exposure was investigated by the green fluorescence emitted from dichlorofluorescein (DCF) [25]. Briefly, isolated PBLM cells were plated in twelve-well plates (\(3\times 10^{3}\) cells/well) and incubated. After 24 h, 2 uM 2,7-dichlororescin diacetate (H2 DCF-DA), obtained from Sigma-Aldrich (Milano, Italy), was added. Afterwards, the cells were incubated for 30 min, during which H2 DCF-DA is converted by ROS in DCF. Then the cells were washed twice with PBS and the green DCF fluorescence emission was measured with a microplate reader (Wallac 1420, Perkin Elmer, Ramsey, MN, USA) by using the 485 nm and 535 nm wavelengths for excitation and emission, respectively. The fluorescence images were obtained by means of an Olympus IX71 fluorescence microscope (Olympus, Tokyo, Japan), with a 50x objective.
# Super-Resolution Reconstruction of Depth Image Based on Kriging Interpolation
## 2 Depth Image super-resolution Algorithm
### Interpolation Algorithm Based on Kriging Interpolation
To identify this relationship, we perform the following steps on the observed data set \(\{\mathrm{z}(\mathrm{x}_{1},\mathrm{y}_{1}),\mathrm{z}(\mathrm{x}_{2},\mathrm{ y}_{2}),\cdots,\mathrm{z}(\mathrm{x}_{n-1},\mathrm{y}_{n-1}),\mathrm{z}(\mathrm{x}_{n}, \mathrm{y}_{n}),\}\). First, we calculate the distance between any two points  
\[\mathrm{d}_{\mathrm{ij}}=\sqrt{\left(\mathrm{x}_{i}-\mathrm{x}_{j}\right)^{2}+ \left(\mathrm{y}_{i}-\mathrm{y}_{j}\right)^{2}} \tag{29}\]  
and semi-covariance  
\[\mathrm{r}_{\mathrm{ij}}=\frac{1}{2}\left(\mathrm{z}_{i}-\mathrm{z}_{j}\right) ^{2} \tag{30}\]  
It is possible to obtain \(\mathrm{n}^{2}\) (\(\mathrm{d}_{\mathrm{ij}}\), \(\mathrm{r}_{\mathrm{ij}}\)) data pairs by plotting all the \(\mathrm{d}\) and \(\mathrm{r}\) values in a scatter plot to find an optimal curve to fit \(\mathrm{d}\) and \(\mathrm{r}\) and obtain a functional relationship. \[\mathrm{r}=\mathrm{r}(\mathrm{d}) \tag{31}\]Furthermore, for any two points \(z(x_{i},y_{i}),z(x_{j},y_{j})\), we first calculate their distance, \(d_{ij}\). Next, according to the functional relationship, we obtain the semi-covariance \(r_{ij}\). Considering the computational complexity, simple Kriging is used for the interpolation of depth images. The ordinary Kriging formula is as follows. \[\hat{Z}_{o}=\sum_{i=1}^{n}\lambda_{i}z_{i} \tag{32}\]  
The simple Kriging formula is as follows. \[\hat{Z}_{o}-c=\sum_{i=1}^{n}\lambda_{i}z_{i}-c \tag{33}\]  
where \(c\) is the mathematical expectation of the attribute value, i.e., \(E[z]=c\). That is, in ordinary Kriging interpolation, the attribute value of an unknown point is considered to be a weighted sum of the attribute values of known points. The simple Kriging Formula (33) completes the derivation of the estimation of the values of depth images by interpolation, according to which different peripheral depth values can be selected for interpolation. Based on this theoretical derivation, a depth image interpolation algorithm is constructed to super-resolve depth images by Kriging interpolation. The method first down-samples the image by 50% and then up-samples it by 200%, where the sampling algorithm uses Kriging interpolation. The super-resolution results are shown in Figure 7 which shows that the Kriging interpolation algorithm is affected by edges and voids. It can also be seen from the output of the Kriging interpolation algorithm that rapid changes at edges can lead to highly discrepant interpolation results from the actual situation.
## 2 Materials and Methods
### Measurements of Samples  
Before and after the decomposition experiment, the samples were subjected to various measurements. After the samples were collected, they were washed with water to remove the attached soil, freeze-dried, and stored in a desiccator until the amount of water adsorbed was equal to that before the decomposition experiment. The weight-loss ratio was then calculated by measuring the weight and comparing it with that before the decomposition experiment. The solid-state NMR spectra in this study were recorded using an Avance III HD-500 instrument (Bruker Corp., Billerica, MA, USA) equipped with a double-resonance 4.0 mm MAS probe operating at 500.13 MHz for \({}^{1}\)H and 125.76 MHz for \({}^{13}\)C. In solid-state NMR measurements, \({}^{13}\)C-CP/MAS measurements and \({}^{1}\)H static measurements were performed to collect data on the structural information of NMR spectra [20]. The \({}^{13}\)C-CP/MAS spectra of PLA were recorded at an MAS frequency of 12 kHz at 299 K. In the \({}^{13}\)C-CP/MAS measurements, the spectral width was 200 ppm, the observed points were 2048, and the number of scans was 1024. The \({}^{1}\)H wide-line NMR spectra of PLA were recorded at an MAS frequency of 0 kHz at 299 K. In \({}^{1}\)H static measurements, the spectral width was 400 ppm, the observed points were 32,768, and the number of scans was 32. All NMR spectra were pre-processed using TopSpin and Mnova software. The weight average molecular weight (Mw) of the sample was determined by gel permeation chromatography (GPC) analysis using LaChrom Elite system (Hitachi, Tokyo, Japan) equipment with a Shodex GPC K-805L column. Chloroform was used as the eluent, and the sample concentration was 10 mg/mL. When the measurement was taken, the column temperature was 30 \({}^{\circ}\)C, the sample injection volume was 50 \(\upmu\)L, the flow velocity was 1.0 mL/min, and the relative molecular weight distribution was measured using an ultraviolet detector. Polystyrene standard was used for the calibration curve. Differential scanning calorimetry (DSC) was performed using DSC3500 Sirius (Netzsch Geratebau GmbH, Selb, Germany). A sample of approximately 12 mg was weighed in an aluminum pan and measured at a nitrogen flow rate of 40 mL/min. The temperature was lowered from room temperature to 20 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, stabilized for 10 min, and then raised to 200 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. For sample exchange, the sample was kept at 200 \({}^{\circ}\)C for 5 min and then cooled to 25 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min. Cold crystallization enthalpy (\(\Delta Hcc\)) and melting enthalpy (\(\Delta Hm\)) were measured. Then, crystallinity was evaluated according to the following equation:  
\[\text{Crystallinity }[\%]=\left(\Delta Hcc+\Delta Hm\right)/135 \tag{2}\]  
where 135 J/g is the theoretical maximum value of \(\Delta Hm\)[15].
# Control of _Francisella tulareis_ Virulence at Gene Level: Network of Transcription Factors
## 6 Proteins Essential for Gene Transcription  
### RNA Polymerase Subunits and Sigma Factors  
Critical and absolutely necessary for gene transcription is the RNA polymerase and sigma factors that ensure specific promoter recognizing by the RNA polymerase. The Francisella RNA polymerase is composed of two \(\alpha\) subunits, the \(\beta\), \(\beta^{\prime}\) and \(\omega\) subunits (_rpoA1_, _rpoA2_, _rpoB_, _rpoC_, _rpoZ_, respectively) [30]. Francisella contains two _rpoA_ genes that encode two non-identical \(\alpha\) subunits, which are able to form both a heterodimer and a homodimer [30; 56]. In addition, Francisella contains at least two sigma factors. The major sigma factor \(\sigma^{70}\) is encoded by the _rpoD_ gene and an alternative sigma factor, which is a homolog of the \(\sigma^{32}\) heat-shock family proteins, is encoded by the _rpoH_ gene [30; 57]. Transcripts of several genes were observed to have increased level of expression during heat shock (Hsp40, GroEL, GroES, DnaK, DnaJ, GrpE, ClpB, ClpX, ClpP, and HtpG), and six of them were found to be more abundant during overproduction of RpoH in the live vaccine strain (LVS) (Hsp40, HptG, DnaK, DnaJ, GroES, and GroEL) [57], which are proteins included in the heat stress response and are designated as heat-shock proteins (HSP) [57]. The HSP proteins are under direct regulation of RpoH in _E. coli_[58], suggesting the same model of gene regulation during heat shock conditions in _F. tularensis_ subsp. _halarctica_ LVS [57]. The response to heat shock is crucial for intracellular bacteria such as Francisella. Most of the RNA-polymerase-regulated genes are required for intracellular growth and full virulence of _F. novicida_[124]. Probably the most important of them is ClpB [125] that directly interacts with DnaK, which is under RpoH control, [59] and a strain defective for the _clpB_ gene is an auspicious candidate as a live vaccine strain [126; 127].
# Study on Degradation of Benzothiazolium-Based Ionic Liquids by UV-H\({}_{2}\)O\({}_{2}\)
## 3 Results
### Effect of Chloride Ion Concentration on Degradation  
Chloride ions can react with hydroxyl radicals to form chlorine-containing inorganic radical ions (Equations (6)-(10)). The chlorine-containing radical ion can further generate chlorine radicals. The reactivity of chlorine radicals is much smaller than that of hydroxyl radicals. Chloride ions and chlorine radicals further generate dichlor radicals. Finally, the generated chlorine molecules terminate the transfer of free radicals [31]. Chloride ions are widely found in ocean and river water and also in industrial wastewaters, so the effect of chloride ion concentration on oxidative degradation was considered in this study.  
Different concentration levels of NaCl were added to an aqueous solution containing 0.1 mM IL and 100 mM H2O2 to make chloride anion concentrations of 0.001 M, 0.01 M, and 0.1 M. The photo-assisted oxidation reaction was carried out in a water bath at a temperature of 45 \({}^{\circ}\)C. The results are shown in Figure 5a, where it can be seen from the figure that the chloride ion had a significant inhibitory effect on the degradation of benzothiazole ILs and the degradation efficiency was significantly reduced when the concentration of chloride ion was only 0.001 M. The mechanism by which chloride ions inhibit the degradation reaction was as follows:  
\[\mathrm{Cl^{-}+^{\circ}OH}\rightarrow\mathrm{HOCI^{-}}, \tag{6}\]  
\[\mathrm{HOCI^{-}+H^{+}}\rightarrow\mathrm{Cl^{+}+H_{2}O}, \tag{7}\]  
\[\mathrm{Cl^{+}+Cl^{-}\rightarrow\mathrm{Cl_{2}}^{-}}, \tag{8}\]  
\[2\mathrm{Cl_{2}}^{-}\to 2\mathrm{Cl^{-}+Cl_{2}\left(aq\right)}, \tag{9}\]  
\[2\mathrm{Cl^{+}\rightarrow\mathrm{Cl_{2}}}, \tag{10}\]  
Figure 4: Effect of temperature on degradation (**a**); effect of temperature (**b**) on kinetic constant; [C4 BthPF6] = 0.1 mM, pH = 5.06, [H2O2] = 100 mM.
## 2 Transmission Setup and Principles  
### Transmission Setup  
Figure 1 depicts the 9-channel 32-Gbaud Nyquist-spaced superchannel transmission system using the following modulation formats: DP-QPSK, DP-16 QAM, DP-64 QAM. At the transmitter (Tx), a 9-line 32-GHz spaced laser comb with a central wavelength of 1550 nm is employed as the phase-locked optical carrier for each sub-channel. Each optical carrier is split into two branches using a polarization beam splitter (PBS) and is then fed into two in-phase and quadrature (I-Q) modulators, respectively. The transmitted data sequences in all channels and polarizations are mutually independent based on the use of pseudo-random binary codes (PRBS) with a pattern length of 216. The Nyquist pulse shaping (NPS) is performed using an ideal root-raised cosine (RRC) filter with a roll-off coefficient of 0.1%.  
After the modulation, two orthogonally polarized signal sequences are combined using a polarization beam combiner (PBC) and are then injected into the standard single-mode fiber (SSMF). Numerical simulation of the SSMF is implemented using the split-step Fourier method based on the nonlinear Schrodinger equation (NLSE) with a step size of 0.1 km. Simulation parameters of SSMF include an attenuation coefficient of 0.2 dB/km, CD coefficient of 17 ps/nm/km, and a nonlinear coefficient of 1.2 /w/km, which represents a typical set of SSMF according to the Corning SMF-28 datasheet. An erbium-doped fiber amplifier (EDFA) is used to compensate for the loss of fiber in each span during the transmission. The noise figure of EDFA is 5.0 dB, which is a typical value for standard EDFA [1]. The fiber length of each span is 80 km. At the receiver (Rx), the received signal is mixed with the LO laser to achieve an ideal coherent detection of I-Q components in each polarization state. The signals are sampled using analog-to-digital converters (ADCs) and are then processed by DSP modules which include a dispersion compensation module using EDC, nonlinearity compensation module using MC-DBP, matched filter, CPE, and symbol demodulation. In our simulation, the frequency domain equalizer (FDE) is used for CD compensation [23], and the MC-DBP is used for NLC [24]. The MC-DBP algorithm is implemented based on the reverse split-step Fourier solution of the NLSE and the compensation bandwidth is set as 32 GHz (for 1-channel DBP), 160 GHz (for 5-channel DBP), and 288 GHz (for 9-channel DBP, FF-DBP), respectively. Before the MC-DBP module, an ideal RRC filter with a roll-off of 0.1% is applied to select the compensation bandwidth and to remove the unwanted amplifier spontaneous emission noise (ASE). An ideal CPE is used for the compensation of phase noise, achieved using the conjugate multiplication between the received signal and the extracted intrinsic laser phase noise [13; 25]. This is to isolate the impact from EEPN, where no amplitude noise mitigation effect is employed in the CPE module [15; 26]. BER and MI estimation are carried out to evaluate the transmission performance. The PMD and the frequency offset are neglected in all simulations.
## 2 Materials and Methods
### Numerical Analyses of Real Buildings  
#### 2.2.1 Subsoil Model  
After a review of the works [50; 51; 52; 53; 54], Modified Cam-Clay was chosen as the subsoil model. Its basic assumption is that for the primary load, there is a logarithmic relationship between the mean effective pressure \(p_{0}\) and the porosity index \(e\) (isotropic Normal Consolidation Line--NCL). NCL described as the compression index on the ln\(p\)'-\(e\) plane is a straight line with a slope of \(\lambda\). The final stress level ever achieved on this line is called isotropic preconsolidation pressure \(p_{po}\). [55; 56; 57]. During unloading and reloading up to preconsolidation stress, there is a line with slope of \(\kappa\), referred to as unloading-reloading index. In reality, on the \(p\)'-\(e\) plane, there is an infinite number of recompression curves - secondary load curves, each of which corresponds to a specific \(p_{po}\) value [58; 51]. Plane \(p\)'-\(e\) is shown in Figure 9(a).  
Figure 10: Assumptions of the Modified Cam-Clay model: (a) \(p\)’-\(e\) plane; (b) \(p\)-\(q\) plane.  
Figure 9: The course of settlement of benchmarks over time.  
Yield surface, illustrated in Figure 10b, in the Modified Cam-Clay model is described by the ellipse equation, symmetrical in relation to the \(p_{c}\) line:  
\[q^{2}-M^{2}(p_{c}p-p^{2})=0, \tag{3}\]  
where \(q\) is the stress deviator; \(M\) is the slope of the Critical State Line on the \(p\)-\(q\) surface (Figure 8b); \(p_{c}\) is the isotropic stress equivalent to preconsolidation stress; and \(p\) is the average normal stress.  
The \(M\) parameter is related to the effective angle of internal friction \(\varphi^{\prime}\) with the following relationship:  
\[M=\frac{6\sin\varphi^{\prime}}{3-\sin\varphi^{\prime}}, \tag{4}\]  
The extent of yield surface \(a_{0}\) in the Modified Cam-Clay model is described by the equation:  
\[a_{0}=\frac{1}{2}\exp\left(\frac{e_{1}-e_{0}-\kappa\ln p_{0}}{\lambda-\kappa} \right), \tag{5}\]  
where \(e_{1}\) is the porosity index at the intersection of the NCL with the \(e\) axis on the ln\(p\)'-\(e\) plane; \(e_{0}\) is the initial porosity index; and \(p_{o}\) is preconsolidation stress.  
Summing up, the main parameters of the Modified Cam-Clay model necessary to model soil behaviour are \(\lambda\), \(\kappa\), \(M\), \(a_{0}\), \(p_{0}\). These parameters can be determined directly in laboratory triaxial compression and oedometric tests. There are also approximate methods for estimating these parameters based on indirect research.
## 2 Materials and Methods
### Data Acquisition and Preprocessing  
#### 2.2.1 Forest Soil CO\({}_{2}\) Efflux (Fco\({}_{2}\)) Measurements  
The automatic cavity ring-down spectrophotometer (CRDS) analyzer (Picarro G2508 Greenhouse Gas Analyzer: Picarro Inc., Santa Clara, CA, USA) with a thick-walled stainless steel chamber (the internal diameter: 19.5 cm and height: 15 cm) is used to measure the FCO\({}_{2}\). This instrument can simultaneously and accurately measure multiple fluxes (e.g., CO\({}_{2}\), CH\({}_{4}\), N\({}_{2}\)O, NH\({}_{3}\), and H\({}_{2}\)O). Moreover, the unique Picarro algorithm can automatically correct the error of the CO\({}_{2}\) concentration caused by the water vapor [47]. Therefore, the Piccaro analyzer has been widely used for measuring soil carbon efflux as reported by many studies [29; 48; 49; 50].  
This study measured the FCO\({}_{2}\) in the dry season starting from 28 September 2019 to 4 January 2020 at CHD and GY (Figure 1), respectively. For each area, the measurements were performed two times each month. The default company settings were considered in this study throughout the whole field measurements [47], and we referred to the previous studies [29; 51] for the detailed method of FCO\({}_{2}\) measurements in the present study. To minimize the daily variation in FCO\({}_{2}\) and to better represent the daily mean FCO\({}_{2}\) over a 24-h cycle, we carried out the measurements of FCO\({}_{2}\) between 10:00 h and 15:00 h (local time) [52; 53; 54; 55; 56]. One week before each measurement, the aboveground plants were removed, and the chamber was inserted directly into the soil to a depth of about 1-3 cm. The FCO\({}_{2}\) concentrations were recorded with intervals of 1-s, and the measuring process at each sampling site lasted for 2-5 min with five repetitions [49]. The final FCO\({}_{2}\) concentration of each spot was obtained by the repeated measurements, and the soil efflux is calculated by the slope of the linear regression of CO\({}_{2}\) concentration with time. It is worth noting that when the R\({}^{2}\) value of the slope of the CO\({}_{2}\) concentration gradient with time is less than 0.85, the data is not included in our study [53]. The final FCO\({}_{2}\) was calculated using the following equation [57].  
\[\text{FCO}_{2}=\rho\cdot\frac{V}{A}\cdot\frac{\Delta C}{\Delta T} \tag{1}\]  
where the FCO\({}_{2}\) represents the forest soil CO\({}_{2}\) efflux (g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)); \(\rho\) is the density of CO\({}_{2}\); \(A\) and \(V\) denote the footprint area (m\({}^{2}\)) and volume (m\({}^{3}\)) of the chamber, respectively; \(\frac{\Delta C}{\Delta T}\) represents the slope of the linear regression of soil CO\({}_{2}\) concentration gradient over time.  
In this study, 70% and 30% of the collected FCO\({}_{2}\) data were randomly used for model training and model validation, respectively.
## 2 Discussion
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Molecules** & **Omps** & **Species** & \
\begin{tabular}{l} **Recalculated Flux at 1 \(\upmu\)M** \\ **Molecules/Second** \\ **Mentioned Gradient** \\ \end{tabular} &
\begin{tabular}{l} **Reported Flux** \\ **Molecules/Second at the Specific** \\ **Mentioned Gradient** \\ \end{tabular} \\ \hline \multirow{3}{*}{Cettazidime} & OmpF & _E. coli_ & 34 & \(\approx\)1000 at gradient 30 \(\upmu\)M [38] \\ \cline{2-5}  & OmpC & _E. coli_ & 17 & \(\approx\)500 at gradient 30 \(\upmu\)M [38] \\ \cline{2-5}  & OprE & _P. aeruginosa_ & \(\leq\)1 & \(\approx\)0.4 at gradient 10 \(\upmu\)M [153] \\ \hline \multirow{2}{*}{Cefotaxime} & OprE & _P. aeruginosa_ & \(\leq\)1 & \(\approx\)0.1 at gradient 10 \(\upmu\)M [153] \\ \hline \multirow{2}{*}{Ampicillin} & OmpF & _E. coli_ & 24 & \(\approx\)237 at gradient10 \(\upmu\)M [54] \\ \cline{2-5}  & Benzylpenicillin & OmpF & _E. coli_ & 12 & \(\approx\)120 at gradient 10 \(\upmu\)M [54] \\ \hline \multirow{3}{*}{Carbenicilin} & OprE & _P. aeruginosa_ & \(\leq\)1 & 0.04 at gradient 10 \(\upmu\)M [153] \\ \cline{2-5}  & OmpF & _E. coli_ & 1.5 & \(\approx\)15 at gradient 10 \(\upmu\)M [39] \\ \cline{2-5}  & OmpC & _E. coli_ & \(\leq\)1 & \(\approx\)8 at gradient 10 \(\upmu\)M [39] \\ \cline{2-5}  & LamB & _E. coli_ & \(\leq\)1 & \(\leq\)1 at gradient 10 \(\upmu\)M [39] \\ \cline{2-5}  & Chip & _E. coli_ & \(\leq\)1 & \(\approx\)3 at gradient 10 \(\upmu\)M [39] \\ \hline \multirow{3}{*}{Kanamycin} & OmpF & _E. coli_ & 1 & \(\approx\)10 at gradient 10 \(\upmu\)M [39] \\ \cline{2-5}  & OmpC & _E. coli_ & 1 & \(\approx\)11 at gradient 10 \(\upmu\)M [39] \\ \cline{2-5}  & LamB & _E. coli_ & \(\leq\)1 & \(\leq\)1 at gradient 10 \(\upmu\)M [39] \\ \cline{2-5}  & Chip & _E. coli_ & \(\leq\)1 & \(\approx\)2 at gradient 10 \(\upmu\)M [39] \\ \cline{2-5}  & Chip & _E. coli_ & \(\leq\)1 & \(\approx\)0.6 at gradient 10 \(\upmu\)M [153] \\ \hline \multirow{3}{*}{Amikacin} & OmpF & _E. coli_ & \(\leq\)1 & \(\approx\)0.1 at gradient 10 \(\upmu\)M [153] \\ \cline{2-5}  & LamB & _E. coli_ & \(\leq\)1 & \(\leq\)1 at gradient 10 \(\upmu\)M [39] \\ \cline{2-5}  & Chip & _E. coli_ & \(\leq\)1 & \(\approx\)2 at gradient 10 \(\upmu\)M [39] \\ \hline \multirow{2}{*}{Sodium Glutamate} & OprE & _P. aeruginosa_ & \(\leq\)1 & \(\approx\)0.6 at gradient 10 \(\upmu\)M [153] \\ \hline \multirow{2}{*}{Arginine Chloride} & OprE & _P. aeruginosa_ & \(\leq\)1 & \(\approx\)0.1 at gradient 10 \(\upmu\)M [153] \\ \cline{2-5}  & \multicolumn{1}{c}{} & & & \\ \end{tabular}
\end{table} 
Table 3: _Cont._Recent studies have further highlighted the direct role of outer membrane porins in transporting antibiotics across bacterial outer membranes [6; 7; 23; 38].