The creation and continued development of antibiotics have revolutionized human health and disease for the past century. The emergence of antimicrobial resistance represents a major threat to human health, and practices that contribute to the development of this threat need to be addressed. Since the 1950s, antibiotics have been used in low doses to increase growth and decrease the feed requirement of animal-derived food sources. A consequence of this practice is the accelerated emergence of antimicrobial resistance that can influence human health through its distribution via animal food products. In the laboratory setting, sublethal doses of antibiotics promote the expansion of bacterial persister populations, a low energy, low metabolism phenotype characterized broadly by antibiotic tolerance. Furthermore, the induction of persister bacteria has been positively correlated with an increased emergence of antibiotic-resistant strains. This body of evidence suggests that the use of antibiotics in agriculture at subtherapeutic levels is actively catalyzing the emergence of antimicrobial-resistant bacteria through the expansion of bacterial persister populations, which is potentially leading to increased infections in humans and decreased antibiotic potency. There is an urgent need to address this debilitating effect on antibiotics and its influence on human health. In this review, we summarize the recent literature on the topic of emerging antimicrobial resistance and its association with bacterial persister populations.
Citation: Noah T Thompson, David A Kitzenberg, Daniel J Kao. Persister-mediated emergence of antimicrobial resistance in agriculture due to antibiotic growth promoters[J]. AIMS Microbiology, 2023, 9(4): 738-756. doi: 10.3934/microbiol.2023038
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The creation and continued development of antibiotics have revolutionized human health and disease for the past century. The emergence of antimicrobial resistance represents a major threat to human health, and practices that contribute to the development of this threat need to be addressed. Since the 1950s, antibiotics have been used in low doses to increase growth and decrease the feed requirement of animal-derived food sources. A consequence of this practice is the accelerated emergence of antimicrobial resistance that can influence human health through its distribution via animal food products. In the laboratory setting, sublethal doses of antibiotics promote the expansion of bacterial persister populations, a low energy, low metabolism phenotype characterized broadly by antibiotic tolerance. Furthermore, the induction of persister bacteria has been positively correlated with an increased emergence of antibiotic-resistant strains. This body of evidence suggests that the use of antibiotics in agriculture at subtherapeutic levels is actively catalyzing the emergence of antimicrobial-resistant bacteria through the expansion of bacterial persister populations, which is potentially leading to increased infections in humans and decreased antibiotic potency. There is an urgent need to address this debilitating effect on antibiotics and its influence on human health. In this review, we summarize the recent literature on the topic of emerging antimicrobial resistance and its association with bacterial persister populations.
Antibiotic-Free Feed;
Antibiotic Growth Promoter;
Antimicrobial Peptide;
Antimicrobial Resistance;
Conventional Feed;
Fluoroquinolone;
Guidance for Industry;
Minimum Duration for Killing;
Minimum Inhibitory Concentration;
non-Antibiotic Growth Promoter;
United States Food & Drug Administration;
Urinary Tract Infection
The location of interest is the southeast shoulder of the Rio Grande Rift on the boundary between southeast New Mexico and west Texas in the southwest of USA (Figure 1). This shoulder constitutes the west margin of the Great Plains; it is at the southwestern part of the North American Craton [1,2].
Figure 1. The stations deployed by USArray in the region; the blue dots represent the stations of the TA array and the red dots represent the stations of the XR array.The basement of the North American Craton is the Proterozoic Laurentia plate. Southwestern Laurentia constitutes the Mojave, Yavapai, Mazatzal and Grenville Precambrian provinces, ordered by age and located from northwest to southeast [3]. The basement of this region is the source of extensive geological, geophysical and geochemical research, but it remains poorly understood [3,4,5,6,7,8,9]. The fact that it is mostly covered with younger formations and located in the boundary between two countries (Mexico and USA) have contributed to the problem.
Mineral resources found in this region include rare earth elements, beryllium and molybdenum related to Tertiary igneous activity. Ignimbrites related to the large silicic event that constitutes the Sierra Madre Occidental are present in calderas at west Texas; some examples are Quitman, Eagle and Chinati [10,11,12,13,14]. Laccoliths are located at places such as Cornudas and Sierra Blanca, TX; stratovolcanoes such as Sierra Blanca, NM and the batholith at Capitan in New Mexico, also contain anomalously high concentrations of incompatible elements [12,15,16,17]. The magmas that created these igneous features rich in rare earth elements, beryllium and molybdenum have similar tholeiitic and alkalic composition to those of oceanic-island basalts indicating the possibility of having originated as asthenosphere derived melts in the lithospheric mantle during extension [18].
The seismic data used in this study is the Broad-band High-gain Z-component (BHZ) of the seismograms registered by two seismic projects: The transportable array (TA) [19] and the Flex array (XR) [6,20,21]. Both projects were deployed by EarthScope USArray, supported by National Science Foundation (NSF) and are available for download at the official website of the Incorporated Research Institutions for Seismology, IRIS [22]. We use the WILBER3 tool to download the data [23]. The distribution of the seismic stations is listed in Appendices 1 and Appendices 2, and shown in Figure 1. Three events were specially selected to make the analysis: The first was a magnitude 6.4 earthquake off the coast of Jalisco Mexico (17.52° N, 105.46° W) on September 24,2008; 02: 33: 05 UTC. The second was a magnitude 6.5 earthquake off the coast of Northern California (40.67° N, 124.47° W) on January 10,2010; 00: 27: 41 UTC. The third was a magnitude 8.1 earthquake in the Samoa Islands Region (15.5119° S, 171.9369° W) on September 29,2009; 17: 48: 11 UTC. The time window was considered to select the events; the TA array stations were deployed in the area of interest approximately from February 2008 to February 2010; and the Flex array from August 2008 to December 2011. The events were selected based on large event magnitude and teleseismic distances to produce the best amplitude and signal to noise ratio to model the crustal structure [24]. Large Rayleigh wave amplitudes, relatively low attenuations and long propagation paths have contributed significantly to our understanding of the seismotectonics in the region [25]. The frequent occurrence of earthquakes and growing number of seismic stations near the southeast flank of the Rio Grande Rift made possible the study of the area's Rayleigh wave group velocities. This procedure was selected because it allows the collection with relative ease of a dense distribution of paths using stations within or near the area of research [25,26]. The results shed new light on the seismotectonics of the region.
The geodetic model and isostatic residual gravity anomaly data were downloaded from USGS Mineral Resources On-Line Spatial Data's website [27]. In Figure 6, the contour of the zero isostatic anomaly is shown for reference [28,29]. Shore lines and borderlines are provided by Generic Mapping Tools, GMT [30,31]. The Elevation Model (ETOPO 1) was downloaded from National Oceanic and Atmospheric Administration's National Centers for Environmental Information, NOAA's NCEI [32].
The following procedure is based on cross correlation of filtered surface waves in to specific band-widths to approximate the inter-station empirical Green's functions using inter-station surface wave dispersion curves. This work was performed using transient seismic signals of the three specific events mentioned in the data section [24,33,34,35,36].
The selected data was processed using the Seismic Analysis Code (SAC) developed by B. Savage and A. Snoke and provided also by IRIS [37,38]. The distance, longitude and latitude of the event and the seismic stations are read from the header of the seismograms. The travel time of the group is calculated using cross correlation. No removal of instrument contribution was necessary because the data was obtained with identical instruments (Streckeisen STS-2 G3 coupled with Quanterra 330 Linear Phase), so they have the same response and same sensitivity; they are also calibrated under the same criteria because they were all deployed by USArray under the same project, EarthScope [21].
The distance between two stations is measured by the subtraction of the great circle path lengths connecting the event with the two stations. In this procedure we also use ray tracing, illustrated in Figures 2a, 2b and 2c, to choose the specific pair of stations; it elucidates the relative position and the order of the stations. The first station should be near, or directly on the seismic path between the event and the second station.
Figure 2a. Ray tracing of the events in Jalisco. Blue dots represent stations of TA array and red dots represent stations of XR array. The background colors represent elevation in meters over sea level.
Figure 2b. Ray tracing of the event in California. Blue dots represent stations of TA array and red dots represent stations of XR array. The background colors represent elevation in meters over sea level.
Figure 2c. Ray tracing of the event in Samoa. Blue dots represent stations of TA array and red dots represent stations of XR array. The background colors represent elevation in meters over sea level.Now that the two stations are identified to be along similar paths and the distance between the two stations is calculated, the seismic travel time between the first and the second station is then calculated using cross correlation. To achieve the cross correlation, the seismograms are loaded into SAC and filtered in the desired frequency band with a specific ban-width. Once the filtering is done, the output signal is corrected and cross-correlated to measure the difference in arrival times; Figure 3 and Figure 4 describe the process graphically [33,34,36].
Figure 3. This procedure was coded in SAC to obtain the correlation times.
Figure 4. Seismograms of an event off the coast of Jalisco. 4a: Are the rough seismograms; 4b: are the seismograms after filtered; 4c: the cross correlation; 4d: the square of the cross correlation. The x axis is in seconds and the y axis is relative amplitude.The filter is a band-pass Butterworth order six [39,40]. It is applied twice with the desired corners from where we define the group. After the correlation the signal is squared to facilitate the identification of the largest peak (maximum). Note that in Figure 4d the maximum amplitude is seen approximately at 19 seconds. The average velocity is then calculated as the ratio between the difference in distance and the correlation time.
We follow the same process for different groups spanning the frequencies available from the seismograms; in this part of the process the physical characteristics of the instrument establish the limitations (Nyquist is 20 Hz). The seismic average velocities of the group are plotted versus period to generate the dispersion curves (Figure 5a).
Figure 5a. Dispersion curves for the event in Jalisco. Horizontal axis represents speed in km/s, the vertical axes, on the left, represents the period in seconds and on the right, represents the approximate depths according to ak135. The line runs from station TA-223A to station TA-W23A approximately from 32° to 35° latitude along –106.25° longitude. Please see Figure 1 to identify the stations involved in the pairs along the line 3.The seismograms were filtered at different band-widths with initial period of 10 seconds. The frequency bandwidths span at increments corresponding to multiples of 5/10, 6/10, 7/10 and 8/10 [33,36]. Figure 5a shows dispersion curves for the stations listed with frequency limits and bandwidths calculated for the event in Jalisco. These dispersion curves and the blockmean tool of the GMT software provided the data to make the profile shown in Figure 5b. The approximate depths labeled in the plot of the dispersion curves in Figure 5a and the profile in Figure 5b on their right axes were taken from the inversion of the model ak135 [41,42]. The red dots in the profile of Figure 5b represent the depth of the Moho according to the receiver functions of the EarthScope Automated Receiver Survey, EARS [43].
Figure 5b. The seismic velocity profile created with the dispersion curves in Figure 5a. Horizontal axis is latitude; vertical axes on the left is period in seconds; on the right is approximate depths; colors are the seismic velocities. The red dots are the approximated depth to the Moho from receiver functions.The data obtained from the calculation of the dispersion curves were stored as matrices containing latitude, longitude, velocity and initial frequency (of the frequency band). These matrices were later used to generate the surface plots shown in Figures 7, 8 and 9. All these Figures were made with the matrices corresponding to band widths of fifth of a decade intervals or periods from 10 s to 20 s for the first band, 20 s to 40 s for the second band, 40 s to 80 s for the third band, 80 s to 160 s for the fourth band. The approximate depths of these frequency bands were approximated from inversion of the model ak135 and joint inversions made for LA RISTRA [8,41,42].
Figure 6. The isostatic residual gravity anomaly of the region. The contour lines denote the zero value of the isostatic anomaly. Some geological structures in the region are identified as: The Diablo Plateau (D), Fort Davis (d), Ouachita (O), Franklyn-Organ Mountains (F), Capitan (C), North Central Basin Platform (B), South Central Basin Platform (b), San Andres Mountains (A), Potrillos Mountains (P), Hueco Bolson (H), Delaware Basin (DB), Tularosa basin (T), Marfa basin (M), Hovey Channel (h), Mesilla basin (m), Sheffield Channel (S) and Salt basin (s). It also shows some of the tertiary REE deposits developed in the region; the dotted lines shows the boundary between Mazatzal and Grenville Precambrian provinces in brown, the alignment visible in the seismic profile for the Jalisco event in purple, the Delaware Basin in black and the Diablo Plateau west boundary in red.
Figure 7. Images represent seismic group velocities calculated using cross correlation for the event in Jalisco. The group periods span: In 7a from 10 to 20 seconds; in 7b from 20 to 40 seconds; in 7c from 40 to 80 seconds and in 7d from 80 to 160 seconds. The x axis represents longitude, the y axis latitude and the color range is seismic velocities in km/s.
Figure 8. Images represent seismic group velocities calculated using cross correlation for the event in California. The group periods span: In 8a from 10 to 20 seconds; in 8b from 20 to 40 seconds; in 8c from 40 to 80 seconds and in 8d from 80 to 160 seconds. The x axis represents longitude, the y axis latitude and the color range is seismic velocities in km/s.
Figure 9. Images represent seismic group velocities calculated using cross correlation for the event in Samoa. The group periods span: In 9a from 10 to 20 seconds; in 9b from 20 to 40 seconds; in 9c from 40 to 80 seconds and in 9d from 80 to 160 seconds. The x axis represents longitude, the y axis latitude and the color range is seismic velocities in km/s.● Between 10 km and 20 km approximated depth for the periods between 10 s and 20 s.
● Between 20 km and 50 km approximated depth for the periods between 20 s and 40 s.
● Between 50 km and 150 km approximated depth for the periods between 40 s and 80 s.
● Between 150 km and 350 km approximated depth for the periods between 80 s and 160 s.
Figure 6 was created to identify the geological structures that constitute this part of the North American Craton. The geological features are labeled on the top of isostatic anomaly map. The labels are:
● Stable structures identified in the region are: The Diablo Plateau (D), Fort Davis Caldera (d), Ouachita (O), Franklin-Organ Mountains (F), Capitan (C), North Central Basin Platform (B), South Central Basin Platform (b), San Andres Mountains (A) and Potrillos Mountains (P) [44].
● Some more flexible corridors surrounding these stable structures are the Hueco Bolson (H), Delaware Basin (DB), Tularosa Basin (T), Marfa Basin (M), Hovey Channel (h), Mesilla Basin (m), Sheffield Channel (S) and Salt Basin (s) [44].
●The lines shown represent the boundary between Mazatzal and Grenville Precambrian Provinces [3], the Delaware Basin, the west boundary of the Diablo Plateau and the diagonal line running from southeast to northwest passing through the center of the Delaware Basin characterizes the Jalisco event analysis. These lines are also shown in the results (Figures 7 to Figures 9) to correlate with the structures.
The results of the seismic velocity model in Figures 7 to 9 were plotted using linear Delaunay triangulation in octave [45,46], an open source alternative software of Matlab [47,48]. Figures 10 and 11 were performed for the estimation of the accuracy [49,50]. The resolution of the figures corresponds to a gridding size of 40 × 40 elements, each 1/100 of a degree in area. For the creation of the surface plot of the seismic velocities, first the Voronoi diagram was created, shown in Figure 10; then the area of interest was gridded into meshes of 5 × 5, 10 × 10, 20 × 20, 30 × 30, 40 × 40, 50 × 50 and 60 × 60 area bins and plotted using the Delaunay triangulation. Figure 11 shows the 5 × 5, 10 × 10, 40 × 40 and 60 × 60 meshes that can be compared to the Voronoi figure. The table in Figure 10 shows the number n of the n × n binning and the size of the area a of each bin. This table was created to determine the most appropriate size of the interpolation mesh; the values of the number n versus the size of the pixel as is shown in the plot next to the table.
Figure 10. Voronoi diagram showing the partitioned plane for the set of positions corresponding to Rayleigh wave velocities calculated for periods ranging from 10 s to 20 s for the event in Jalisco; the table shows the number of side bins n to be calculated per side in a square array of n × n bins of area a that are shown in the plot. The size of the pixel represents a surface in units of degrees square.
Figure 11. Plot of the 2D + 1 surface plot of the seismic velocities interpolated using Delaunay triangulation with area binned by 5 × 5, 10 × 10, 40 × 40 and 60 × 60 bins.The results shown in Figures 7, 8 and 9 were chosen because their wider bandwidths enhance the effects of notches and extinction that are consequences of the multipath trajectories that characterize surface wave propagation; this effect is more frequent when using narrower frequency bandwidths [24]. As an example, in Figure 5a, there is a gap for the dispersion curve of line 3 pair 1 (l3p1); no acceptable data was available in the range between 10.00 s to 14.29 s. The gap was compensated by the blockmean interpolation algorithm of GMT that was used to generate the vertical profile in Figure 7b and by reducing the resolution between stations 223A and 123A. The choice of narrower frequency bands increases the vertical resolution but we should expect more gaps in the seismic velocity matrices due to destructive interference of the multipath effect of seismic surface wave propagation [24]. To generate the plots in Figures 7 to 9 the grid size was chosen from the analysis made in Figures 10 and 11. Figure 11 shows the unrealistic approach of using a 5 × 5 or a 10 × 10 grid in the plots and show that grids greater than 40 × 40 does not provide further information to resolve the geological structures.
From the dispersion curves, Tables 1, 2, 3 and surface plots in Figures 7 to 9, the following structures were resolved: The Delaware Basin shows extreme anisotropy with complicated sub-structures that are resolved differently when the seismic velocities are calculated from sources at different distances and azimuths; its seismic velocities, ranges from 2.2 km/s up to 4.7 km/s. The seismic radiation from the event in California was the slowest, followed by that from the event in Jalisco, with the radiation from the event in Samoa being the fastest. For specific values of specific seismic velocities as function of depth and azimuth please refer to Tables 1, 2, 3. The Diablo Plateau showed seismic velocities, ranging from 3.2 km/s up to 4.3 km/s; but, for the Diablo Plateau, the seismic velocities calculated for the event in California are greater than those calculated for the event in Jalisco in opposition to the scenario in the Delaware Basin and the velocities for the event in Samoa were faster in both scenarios; the event in Jalisco resolves the seismic velocities in high correlation with the isostatic gravity anomaly, the event in Samoa somewhat and the event in California not well. The Tularosa Basin and Sheffield Channel show smaller variations in comparison with the Delaware Basin or the Diablo Plateau but still denote the anisotropy of the region; see Figures 7 to 9 and Tables 1 to 3. The area shows completely different images for every structure depending on the azimuth of the propagation of seismic waves leading to the conclusion of high Rayleigh wave anisotropy.
| Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in Jalisco. The seismic velocities are in kilometers per second | ||||||
| Geol. Feat. | Ref. Stat. | Azimuth | 15±5 km Depth | 35±10 km Depth | 100±50 km Depth | 250±100 km Depth |
| Tularosa Basin | Z23A | -2.4 | 2.4 | 3.3 to 3.5 | 3.8 | 3.9 to 4.3 |
| Diablo Plateau | 224A | -0.2 | 3.2 to 3.4 | 3.5 to 3.6 | 3.8 to 3.9 | 3.9 |
| Delaware Basin | SC61 | 6 | 3.2 to 3.5 | 3.5 to 3.7 | 3.9 to 4.1 | 3.9 to 4.2 |
| Fort Davis | SC61 | 6 | 3.3 | 3.6 | 3.9 | 4 |
| Sheffield Channel | SC73 | 9 | 3.3 | 3.4 | 3.7 | 3.8 |
DownLoad: CSV| Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in California. The seismic velocities are in kilometers per second | ||||||
| Geol. Feat. | Ref. Stat. | Azimuth | 15±5 km Depth | 35±10 km Depth | 100±50 km Depth | 250±100 km Depth |
| Tularosa Basin | Z23A | 111 | 2.7 to 2.9 | 1.3 to 1.7 | 3.7 to 3.8 | 4 |
| Diablo Plateau | 224A | 113 | 3.2 to 3.6 | 3.7 to 3.5 | 3.8 | 3.9 to 4.1 |
| Delaware Basin | SC61 | 110 | 2.2 to 3.5 | 3.5 to 3.7 | 3.9 to 4 | 4 to 4.3 |
| Fort Davis | 326A | 113 | 3.2 to 3.4 | 3.6 | 3.8 to 3.9 | 4.1 |
| Sheffield Channel | SC73 | 112 | 2.5 to 2.8 | 2.6 | 3.6 | 3.1 |
DownLoad: CSV| Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in Samoa. The seismic velocities are in kilometers per second | ||||||
| Geol. Feat. | Ref. Stat. | Azimuth | 15±5 km Depth | 35±10 km Depth | 100±50 km Depth | 250±100 km Depth |
| Sierra Blanca NM | SC19 | 51 | 1.1 | 1.5 | 1.8 | 1.8 |
| Tularosa Basin | Z23A | 51 | 3 | 4.2 | 4.8 | 5 to 5.3 |
| Diablo Plateau | 224A | 52 | 4 to 4.3 | 4.1 to 4.6 | 4.8 to 5.5 | 4.9 to 5.4 |
| Delaware Basin | SC61 | 53 | 3.7 to 4.7 | 4.1 to 5.1 | 4.6 to 6 | 4.5 to 4.7 |
| Fort Davis | 426A | 54 | 4.1 to 4.2 | 4.2 to 4.4 | 5.1 | 5.1 to 5.9 |
| Sheffield Channel | SC73 | 54 | 3.9 | 2.8 | 4.9 | 4.9 |
DownLoad: CSVThe use of Rayleigh wave cross correlation analysis is a good tool to identify geological structures of regional size in the crust and upper mantle if the vertical broad band (BHZ) seismic data is available. For this work, data was provided by TA and Flex arrays [19,20].
If you are planning to use Rayleigh wave cross correlation it is not recommended to combine the data from different events for the following reasons:
● The results of the seismic velocity calculations for different events have different averages and standard deviations. The contrast of the plot is affected by the standard deviation.
● Anisotropy causes huge differences in seismic velocities for surface waves travelling along different azimuths in a specific region. The seismic velocity of a region is a function of the angle of incidence of the seismic radiation.
● The uniqueness of the alignment in the strike and slip of the event (focal mechanism) produces specific distribution of stresses; it is elucidated by the moment tensor [51]. The geology reacts differently to different events.
For these reasons the plot performed for different events leads to completely different images. From observation of the results, the structures are best resolved by radiation patterns that are perpendicular to their boundaries.
● Surface waves have some disadvantages.
● Extinction when passing through any geological structure with content of liquid or melts. This effect is caused because shear waves do not propagate in liquids; then, melting, partial melting or content of fluid in the geological formation along the path can cause anomalous correlation or extinction. In this area we have this effect when seismic waves pass through the Mogollon-Datil volcanic field; due to this problem the area near the upper Rio Grande Rift is not possible to model for events like California or Jalisco.
● Another important problem arising from surface wave propagation is called multi-pathing; this problem can be understood better if we recall the Huygens-Fresnel's wave propagation principle instead of the ray tracing model [24,51]. Correlation between two stations that, according to ray tracing, follow similar paths due to the presence of nearby boundaries striking along the propagation with large differences in seismic velocities, leads to negative time correlations. In other words, if signals arriving in the first station came from nearby geological structures with faster seismic speed than that of the second station, then the time correlation leads to negative values. This can be seen as the gap in the dispersion curves shown for the Jalisco event in Figure 5a.
● In another scenario the difference in phase between the two paths superposes destructively causing extinction of the amplitude in the correlation. The reduction or lack of amplitudes in the signal is seen in the dispersion as a discontinuity commonly called notch; the presence of notches causes loss of resolution in the plot [24].
For the area of this investigation, the plot has lower resolution on the rift than on the plains (in the shoulder of the rift also called flank). For this reason, we decided to use the widest bandwidths to perform the plot shown in Figures 9 to 11.
The data used in this study were provided by Incorporated Research Institutions for Seismology Data Management Center (IRIS DMC), United States Geological Survey (USGS), National Oceanic and Atmospheric Administration's National Centers for Environmental Information (NOAA's NCEI) and General Mopping Tools (GMT).
All authors declare no conflicts of interest in this paper.
| NETWORK | STATION | LAT | LON | NETWORK | STATION | LAT | LON |
| TA | 121A | 32.5324 | –107.7851 | TA | TASL | 34.9454 | –106.4565 |
| TA | 122A | 32.6995 | –107.0005 | TA | TASM | 34.9455 | –106.46 |
| TA | 123A | 32.6349 | –106.2622 | TA | TASN | 34.9455 | –106.46 |
| TA | 124A | 32.7001 | –105.4544 | TA | TASO | 34.9455 | –106.46 |
| TA | 125A | 32.6588 | –104.6573 | TA | TASP | 34.9455 | –106.46 |
| TA | 126A | 32.6462 | –104.0204 | TA | TVZX | 34.0733 | –106.9196 |
| TA | 127A | 32.6764 | –103.3575 | TA | X21A | 34.4457 | –107.7857 |
| TA | 128A | 32.6213 | –102.485 | TA | X22A | 34.5058 | –107.0102 |
| TA | 221A | 32.0094 | –107.7782 | TA | X23A | 34.581 | –106.1881 |
| TA | 222A | 32.1046 | –107.1013 | TA | X24A | 34.5646 | –105.4349 |
| TA | 223A | 32.0062 | –106.4276 | TA | X25A | 34.5271 | –104.6621 |
| TA | 224A | 32.076 | –105.5226 | TA | X26A | 34.5508 | –103.8103 |
| TA | 225A | 32.1101 | –104.8229 | TA | X27A | 34.6469 | –103.0974 |
| TA | 226A | 32.0618 | –104.1014 | TA | X28A | 34.5185 | –102.1973 |
| TA | 226B | 32.0778 | –104.1654 | TA | Y21A | 34.0087 | –107.674 |
| TA | 227A | 32.012 | –103.2924 | TA | Y22A | 33.937 | –106.9652 |
| TA | 228A | 32.118 | –102.5918 | TA | Y22C | 34.0741 | –106.9211 |
| TA | 324A | 31.4425 | –105.4828 | TA | Y22D | 34.0739 | –106.921 |
| TA | 325A | 31.3711 | –104.9712 | TA | Y22E | 34.0742 | –106.9208 |
| TA | 326A | 31.3165 | –103.9786 | TA | Y22F | 34.0741 | –106.9209 |
| TA | 327A | 31.3691 | –103.4923 | TA | Y23A | 33.9315 | –106.0549 |
| TA | 328A | 31.3818 | –102.8097 | TA | Y24A | 33.9257 | –105.4361 |
| TA | 425A | 30.7862 | –104.9857 | TA | Y25A | 33.9229 | –104.6928 |
| TA | 426A | 30.6689 | –104.0293 | TA | Y26A | 33.9232 | –103.8246 |
| TA | 427A | 30.8498 | –103.4018 | TA | Y27A | 33.8839 | –103.1633 |
| TA | 428A | 30.7263 | –102.6847 | TA | Y28A | 33.9086 | –102.2479 |
| TA | 526A | 30.0609 | –104.0898 | TA | Z21A | 33.3086 | –107.6712 |
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DownLoad: CSV| [1] |
Hutchings MI, Truman AW, Wilkinson B (2019) Antibiotics: past, present and future. Curr Opin Microbiol 51: 72-80. https://doi.org/10.1016/j.mib.2019.10.008 
|
| [2] |
Van Boeckel TP, Pires J, Silvester R, et al. (2019) Global trends in antimicrobial resistance in animals in low- and middle-income countries. Science 365. https://doi.org/10.1126/science.aaw1944
|
| [3] | Tiseo K, Huber L, Gilbert M, et al. (2020) Global trends in antimicrobial use in food animals from 2017 to 2030. Antibiotics (Basel) 9. https://doi.org/10.3390/antibiotics9120918 |
| [4] |
Klein EY, Tseng KK, Pant S, et al. (2019) Tracking global trends in the effectiveness of antibiotic therapy using the Drug Resistance Index. BMJ Glob Health 4: e001315. https://doi.org/10.1136/bmjgh-2018-001315
|
| [5] |
Van den Bergh B, Fauvart M, Michiels J (2017) Formation, physiology, ecology, evolution and clinical importance of bacterial persisters. FEMS Microbiol Rev 41: 219-251. https://doi.org/10.1093/femsre/fux001
|
| [6] |
De Briyne N, Atkinson J, Pokludova L, et al. (2014) Antibiotics used most commonly to treat animals in Europe. Vet Rec 175. https://doi.org/10.1136/vr.102462
|
| [7] |
Manyi-Loh C, Mamphweli S, Meyer E, et al. (2018) Antibiotic use in agriculture and its consequential resistance in environmental sources: potential public health implications. Molecules 23. https://doi.org/10.3390/molecules23040795
|
| [8] |
Antimicrobial Resistance C (2022) Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 399: 629-655. https://doi.org/10.1016/S0140-6736(21)02724-0
|
| [9] |
Blair JM, Webber MA, Baylay AJ, et al. (2015) Molecular mechanisms of antibiotic resistance. Nat Rev Microbiol 13: 42-51. https://doi.org/10.1038/nrmicro3380
|
| [10] |
Lerminiaux NA, Cameron ADS (2019) Horizontal transfer of antibiotic resistance genes in clinical environments. Can J Microbiol 65: 34-44. https://doi.org/10.1139/cjm-2018-0275
|
| [11] |
Levin-Reisman I, Ronin I, Gefen O, et al. (2017) Antibiotic tolerance facilitates the evolution of resistance. Science 355: 826-830. https://doi.org/10.1126/science.aaj2191
|
| [12] |
Levin-Reisman I, Brauner A, Ronin I, et al. (2019) Epistasis between antibiotic tolerance, persistence, and resistance mutations. Proc Natl Acad Sci USA 116: 14734-14739. https://doi.org/10.1073/pnas.1906169116
|
| [13] |
Windels EM, Michiels JE, Van den Bergh B, et al. (2019) Antibiotics: combatting tolerance to stop resistance. Mbio 10: e02095-19. https://doi.org/10.1128/mBio.02095-19
|
| [14] |
Windels EM, Michiels JE, Fauvart M, et al. (2019) Bacterial persistence promotes the evolution of antibiotic resistance by increasing survival and mutation rates. Isme J 13: 1239-1251. https://doi.org/10.1038/s41396-019-0344-9
|
| [15] |
Bigger JW (1944) The bactericidal action of penicillin on staphylococcus pyogenes. Ir J Med Sci 19: 553-568. https://doi.org/10.1007/BF02948386
|
| [16] |
Balaban NQ, Merrin J, Chait R, et al. (2004) Bacterial persistence as a phenotypic switch. Science 305: 1622-1625. https://doi.org/10.1126/science.1099390
|
| [17] |
Wilmaerts D, Windels EM, Verstraeten N, et al. (2019) General mechanisms leading to persister formation and awakening. Trends Genet 35: 401-411. https://doi.org/10.1016/j.tig.2019.03.007
|
| [18] |
Moore PR, Evenson A, et al. (1946) Use of sulfasuxidine, streptothricin, and streptomycin in nutritional studies with the chick. J Biol Chem 165: 437-441. https://doi.org/10.1016/S0021-9258(17)41154-9
|
| [19] |
Chattopadhyay MK (2014) Use of antibiotics as feed additives: a burning question. Front Microbiol 5: 334. https://doi.org/10.3389/fmicb.2014.00334
|
| [20] |
Hosain MZ, Kabir SML, Kamal MM (2021) Antimicrobial uses for livestock production in developing countries. Vet World 14: 210-221. https://doi.org/10.14202/vetworld.2021.210-221
|
| [21] |
Martin MJ, Thottathil SE, Newman TB (2015) Antibiotics overuse in animal agriculture: a call to action for health care providers. Am J Public Health 105: 2409-2410. https://doi.org/10.2105/AJPH.2015.302870
|
| [22] |
Gaskins HR, Collier CT, Anderson DB (2002) Antibiotics as growth promotants: Mode of action. Anim Biotechnol 13: 29-42. https://doi.org/10.1081/ABIO-120005768
|
| [23] |
Niewold TA (2007) The nonantibiotic anti-inflammatory effect of antimicrobial growth promoters, the real mode of action? A hypothesis. Poult Sci 86: 605-609. https://doi.org/10.1093/ps/86.4.605
|
| [24] |
Coates ME, Fuller R, Harrison GF, et al. (1963) A comparision of the growth of chicks in the Gustafsson germ-free apparatus and in a conventional environment, with and without dietary supplements of penicillin. Br J Nutr 17: 141-150. https://doi.org/10.1079/BJN19630015
|
| [25] |
Broom LJ (2017) The sub-inhibitory theory for antibiotic growth promoters. Poult Sci 96: 3104-3108. https://doi.org/10.3382/ps/pex114
|
| [26] |
Visek WJ (1978) The mode of growth promotion by antibiotics. J Anim Sci 46: 1447-1469. https://doi.org/10.2527/jas1978.4651447x
|
| [27] | Jukes TH, Williams WL (1953) Nutritional effects of antibiotics. Pharmacol Rev 5: 381-420. |
| [28] |
Kim HB, Borewicz K, White BA, et al. (2012) Microbial shifts in the swine distal gut in response to the treatment with antimicrobial growth promoter, tylosin. Proc Natl Acad Sci USA 109: 15485-15490. https://doi.org/10.1073/pnas.1205147109
|
| [29] |
Plata G, Baxter NT, Susanti D, et al. (2022) Growth promotion and antibiotic induced metabolic shifts in the chicken gut microbiome. Commun Biol 5: 293. https://doi.org/10.1038/s42003-022-03239-6
|
| [30] |
Robinson K, Becker S, Xiao Y, et al. (2019) Differential impact of subtherapeutic antibiotics and ionophores on intestinal microbiota of broilers. Microorganisms 7: 282. https://doi.org/10.3390/microorganisms7090282
|
| [31] |
Torok VA, Allison GE, Percy NJ, et al. (2011) Influence of antimicrobial feed additives on broiler commensal posthatch gut microbiota development and performance. Appl Environ Microbiol 77: 3380-3390. https://doi.org/10.1128/AEM.02300-10
|
| [32] |
Agunos A, Gow SP, Leger DF, et al. (2019) Antimicrobial use and antimicrobial resistance indicators-integration of farm-level surveillance data from broiler chickens and Turkeys in British Columbia, Canada. Front Vet Sci 6: 131. https://doi.org/10.3389/fvets.2019.00131
|
| [33] | (2017) ECDC/EFSA/EMA second joint report on the integrated analysis of the consumption of antimicrobial agents and occurrence of antimicrobial resistance in bacteria from humans and food-producing animals: Joint Interagency Antimicrobial Consumption and Resistance Analysis (JIACRA) Report. EFSA J 15: e04872. https://doi.org/10.2903/j.efsa.2017.4872 |
| [34] |
Hiki M, Kawanishi M, Abo H, et al. (2015) Decreased resistance to broad-spectrum cephalosporin in escherichia coli from healthy broilers at farms in Japan after voluntary withdrawal of ceftiofur. Foodborne Pathog Dis 12: 639-643. https://doi.org/10.1089/fpd.2015.1960
|
| [35] |
Sundsfjord A, Simonsen GS, Courvalin P (2001) Human infections caused by glycopeptide-resistant Enterococcus spp: are they a zoonosis?. Clin Microbiol Infect 7: 16-33. https://doi.org/10.1046/j.1469-0691.2001.00055.x
|
| [36] |
Aarestrup FM, Kruse H, Tast E, et al. (2000) Associations between the use of antimicrobial agents for growth promotion and the occurrence of resistance among Enterococcus faecium from broilers and pigs in Denmark, Finland, and Norway. Microb Drug Resist 6: 63-70. https://doi.org/10.1089/mdr.2000.6.63
|
| [37] |
Kumar K, Gupta SC, Chander Y, et al. (2005) Antibiotic use in agriculture and its impact on the terrestrial environment. Adv Agron 87: 1-54. https://doi.org/10.1016/S0065-2113(05)87001-4
|
| [38] |
Sarmah AK, Meyer MT, Boxall ABA (2006) A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment. Chemosphere 65: 725-759. https://doi.org/10.1016/j.chemosphere.2006.03.026
|
| [39] |
Meyerhoff A, Albrecht R, Meyer JM, et al. (2004) US Food and Drug Administration approval of ciprofloxacin hydrochloride for management of postexposure inhalational anthrax. Clin Infect Dis 39: 303-308. https://doi.org/10.1086/421491
|
| [40] |
Chantziaras I, Boyen F, Callens B, et al. (2014) Correlation between veterinary antimicrobial use and antimicrobial resistance in food-producing animals: a report on seven countries. J Antimicrob Chem 69: 827-834. https://doi.org/10.1093/jac/dkt443
|
| [41] |
Norstrom M, Hofshagen M, Stavnes T, et al. (2006) Antimicrobial resistance in Campylobacter jejuni from humans and broilers in Norway. Epidemiol Infect 134: 127-130. https://doi.org/10.1017/S0950268805004814
|
| [42] |
de Been M, Lanza VF, de Toro M, et al. (2014) Dissemination of cephalosporin resistance genes between Escherichia coli strains from farm animals and humans by specific plasmid lineages. PLoS Genet 10: e1004776. https://doi.org/10.1371/journal.pgen.1004776
|
| [43] |
Woolhouse M, Ward M, van Bunnik B, et al. (2015) Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci 370: 20140083. https://doi.org/10.1098/rstb.2014.0083
|
| [44] |
Muloi D, Ward MJ, Pedersen AB, et al. (2018) Are food animals responsible for transfer of antimicrobial-resistant escherichia coli or their resistance determinants to human populations? a systematic review. Foodborne Pathog Dis 15: 467-474. https://doi.org/10.1089/fpd.2017.2411
|
| [45] |
Gutierrez A, Jain S, Bhargava P, et al. (2017) Understanding and sensitizing density-dependent persistence to quinolone antibiotics. Molecular Cell 68: 1147-1154 e1143. https://doi.org/10.1016/j.molcel.2017.11.012
|
| [46] |
Helaine S, Kugelberg E (2014) Bacterial persisters: formation, eradication, and experimental systems. Trends Microbiol 22: 417-424. https://doi.org/10.1016/j.tim.2014.03.008
|
| [47] |
Stapels DAC, Hill PWS, Westermann AJ, et al. (2018) Salmonella persisters undermine host immune defenses during antibiotic treatment. Science 362: 1156-1160. https://doi.org/10.1126/science.aat7148
|
| [48] |
Balaban NQ, Helaine S, Lewis K, et al. (2019) Definitions and guidelines for research on antibiotic persistence. Nat Rev Microbiol 17: 441-448. https://doi.org/10.1038/s41579-019-0196-3
|
| [49] |
Brauner A, Fridman O, Gefen O, et al. (2016) Distinguishing between resistance, tolerance and persistence to antibiotic treatment. Nat Rev Microbiol 14: 320-330. https://doi.org/10.1038/nrmicro.2016.34
|
| [50] |
Ronneau S, Hill PW, Helaine S (2021) Antibiotic persistence and tolerance: not just one and the same. Curr Opin Microbiol 64: 76-81. https://doi.org/10.1016/j.mib.2021.09.017
|
| [51] |
Barrett TC, Mok WWK, Murawski AM, et al. (2019) Enhanced antibiotic resistance development from fluoroquinolone persisters after a single exposure to antibiotic. Nat Commun 10: 1177. https://doi.org/10.1038/s41467-019-09058-4
|
| [52] |
Allison KR, Brynildsen MP, Collins JJ (2011) Metabolite-enabled eradication of bacterial persisters by aminoglycosides. Nature 473: 216-220. https://doi.org/10.1038/nature10069
|
| [53] |
Pontes MH, Groisman EA (2019) Slow growth determines nonheritable antibiotic resistance in Salmonella enterica. Sci Signal 12. https://doi.org/10.1126/scisignal.aax3938
|
| [54] |
Fridman O, Goldberg A, Ronin I, et al. (2014) Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations. Nature 513: 418-421. https://doi.org/10.1038/nature13469
|
| [55] |
Rowe SE, Wagner NJ, Li L, et al. (2020) Reactive oxygen species induce antibiotic tolerance during systemic Staphylococcus aureus infection. Nat Microbiol 5: 282-290. https://doi.org/10.1038/s41564-019-0627-y
|
| [56] | Tuomanen E, Cozens R, Tosch W, et al. (1986) The rate of killing of escherichia-coli by beta-lactam antibiotics is strictly proportional to the rate of bacterial-growth. J Gen Microbiol 132: 1297-1304. https://doi.org/10.1099/00221287-132-5-1297 |
| [57] |
Amato SM, Orman MA, Brynildsen MP (2013) Metabolic control of persister formation in Escherichia coli. Molecular Cell 50: 475-487. https://doi.org/10.1016/j.molcel.2013.04.002
|
| [58] |
Helaine S, Cheverton AM, Watson KG, et al. (2014) Internalization of Salmonella by macrophages induces formation of nonreplicating persisters. Science 343: 204-208. https://doi.org/10.1126/science.1244705
|
| [59] |
Hill PWS, Moldoveanu AL, Sargen M, et al. (2021) The vulnerable versatility of Salmonella antibiotic persisters during infection. Cell Host Microbe 29: 1757-1773. https://doi.org/10.1016/j.chom.2021.10.002
|
| [60] |
Korch SB, Henderson TA, Hill TM (2003) Characterization of the hipA7 allele of Escherichia coli and evidence that high persistence is governed by (p)ppGpp synthesis. Mol Microbiol 50: 1199-1213. https://doi.org/10.1046/j.1365-2958.2003.03779.x
|
| [61] |
Moyed HS, Bertrand KP (1983) Hipa, a newly recognized gene of escherichia-coli k-12 that affects frequency of persistence after inhibition of murein synthesis. J Bacteriol 155: 768-775. https://doi.org/10.1128/jb.155.2.768-775.1983
|
| [62] |
Wilmaerts D, Bayoumi M, Dewachter L, et al. (2018) The persistence-inducing toxin HokB forms dynamic pores that cause atp leakage. Mbio 9. https://doi.org/10.1128/mBio.00744-18
|
| [63] |
Zalis EA, Nuxoll AS, Manuse S, et al. (2019) Stochastic variation in expression of the tricarboxylic acid cycle produces persister cells. Mbio 10. https://doi.org/10.1128/mBio.01930-19
|
| [64] |
Verstraeten N, Knapen WJ, Kint CI, et al. (2015) Obg and membrane depolarization are part of a microbial bet-hedging strategy that leads to antibiotic tolerance. Mol Cell 59: 9-21. https://doi.org/10.1016/j.molcel.2015.05.011
|
| [65] |
Allison KR, Brynildsen MP, Collins JJ (2011) Heterogeneous bacterial persisters and engineering approaches to eliminate them. Curr Opin Microbiol 14: 593-598. https://doi.org/10.1016/j.mib.2011.09.002
|
| [66] |
Conlon BP (2014) Staphylococcus aureus chronic and relapsing infections: Evidence of a role for persister cells An investigation of persister cells, their formation and their role in S. aureus disease. Bioessays 36: 991-996. https://doi.org/10.1002/bies.201400080
|
| [67] |
Lewis K (2010) Persister cells. Annu Rev Microbiol 64: 357-372. https://doi.org/10.1146/annurev.micro.112408.134306
|
| [68] |
Bakkeren E, Huisman JS, Fattinger SA, et al. (2019) Salmonella persisters promote the spread of antibiotic resistance plasmids in the gut. Nature 573: 276-280. https://doi.org/10.1038/s41586-019-1521-8
|
| [69] |
Davies J, Davies D (2010) Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev 74: 417-433. https://doi.org/10.1128/MMBR.00016-10
|
| [70] |
Huddleston JR (2014) Horizontal gene transfer in the human gastrointestinal tract: potential spread of antibiotic resistance genes. Infect Drug Resist 7: 167-176. https://doi.org/10.2147/IDR.S48820
|
| [71] | Wang HQ, Feng MY, Anwar TM, et al. (2023) Change in antimicrobial susceptibility of Listeria spp. in response to stress conditions. Front Sustainable Food Systems 7. https://doi.org/10.3389/fsufs.2023.1179835 |
| [72] | Wu RA, Feng JS, Yue M, et al. (2023) Overuse of food-grade disinfectants threatens a global spread of antimicrobial-resistant bacteria. Crit Rev Food Sci Nutr . https://doi.org/10.1080/10408398.2023.2176814 |
| [73] |
Cohen NR, Lobritz MA, Collins JJ (2013) Microbial persistence and the road to drug resistance. Cell Host Microbe 13: 632-642. https://doi.org/10.1016/j.chom.2013.05.009
|
| [74] |
Douafer H, Andrieu V, Phanstiel O, et al. (2019) Antibiotic adjuvants: make antibiotics great again!. J Medl Chem 62: 8665-8681. https://doi.org/10.1021/acs.jmedchem.8b01781
|
| [75] |
Kitzenberg DA, Lee JS, Mills KB, et al. (2022) Adenosine awakens metabolism to enhance growth-independent killing of tolerant and persister bacteria across multiple classes of antibiotics. Mbio 13. https://doi.org/10.1128/mbio.00480-22
|
| [76] |
Meylan S, Andrews IW, Collins JJ (2018) Targeting antibiotic tolerance, pathogen by pathogen. Cell 172: 1228-1238. https://doi.org/10.1016/j.cell.2018.01.037
|
| [77] |
Wright GD (2016) Antibiotic adjuvants: rescuing antibiotics from resistance. Trends Microbiol 24: 862-871. https://doi.org/10.1016/j.tim.2016.06.009
|
| [78] |
Bengtsson-Palme J, Kristiansson E, Larsson DGJ (2018) Environmental factors influencing the development and spread of antibiotic resistance. Fems Microbiol Rev 42: 68-80. https://doi.org/10.1093/femsre/fux053
|
| [79] |
Blair JMA, Webber MA, Baylay AJ, et al. (2015) Molecular mechanisms of antibiotic resistance. Nat Rev Microbiol 13: 42-51. https://doi.org/10.1038/nrmicro3380
|
| [80] |
Darby EM, Trampari E, Siasat P, et al. (2023) Molecular mechanisms of antibiotic resistance revisited. Nat Rev Microbiol 21: 280-295. https://doi.org/10.1038/s41579-022-00820-y
|
| [81] |
Andersson DI, Hughes D (2014) Microbiological effects of sublethal levels of antibiotics. Nat Rev Microbiol 12: 465-478. https://doi.org/10.1038/nrmicro3270
|
| [82] |
Levy SB, Marshall B (2004) Antibacterial resistance worldwide: causes, challenges and responses. Nature Medicine 10: S122-S129. https://doi.org/10.1038/nm1145
|
| [83] | Ventola CL (2015) The antibiotic resistance crisis: part 1: causes and threats. P T 40: 277-283. |
| [84] |
Vega NM, Allison KR, Khalil AS, et al. (2012) Signaling-mediated bacterial persister formation. Nat Chem Biol 8: 431-433. https://doi.org/10.1038/nchembio.915
|
| [85] | Reeks BY, Champlin FR, Paulsen DB, et al. (2005) Effects of sub-minimum inhibitory concentration antibiotic levels and temperature on growth kinetics and outer membrane protein expression in Mannheimia haemolytica and Haemophilus somnus. Can J Vet Res 69: 1-10. |
| [86] |
Walsh SI, Peters DS, Smith PA, et al. (2019) Inhibition of protein secretion in escherichia coli and sub-mic effects of arylomycin antibiotics. Antimicrob Agents Chemother 63. https://doi.org/10.1128/AAC.01253-18
|
| [87] | MedlinePlus (n.d.) Tetracycline: Medlineplus drug information. Available from https://medlineplus.gov/druginfo/meds/a682098.html. |
| [88] | Pandey N, Cascella M Beta lactam antibiotics (2022). Available from https://www.ncbi.nlm.nih.gov/books/NBK545311/. |
| [89] | Yan A, Bryant EE Quinolones (2023). Available from https://www.ncbi.nlm.nih.gov/books/NBK557777/. |
| [90] |
Cowieson AJ, Kluenter AM (2019) Contribution of exogenous enzymes to potentiate the removal of antibiotic growth promoters in poultry production. Anim Feed Sci Technol 250: 81-92. https://doi.org/10.1016/j.anifeedsci.2018.04.026
|
| [91] |
Seal BS, Lillehoj HS, Donovan DM, et al. (2013) Alternatives to antibiotics: a symposium on the challenges and solutions for animal production. Anim Health Res Rev 14: 78-87. https://doi.org/10.1017/S1466252313000030
|
| [92] | Grassi L, Di Luca M, Maisetta G, et al. (2017) Generation of persister cells of pseudomonas aeruginosa and staphylococcus aureus by chemical treatment and evaluation of their susceptibility to membrane-targeting agents. Front Microbiol 8. https://doi.org/10.3389/fmicb.2017.01917 |
| [93] |
Hammerum AM, Heuer OE, Lester CH, et al. (2007) Comment on: withdrawal of growth-promoting antibiotics in Europe and its effects in relation to human health. Int J Antimicrob Agents 30: 466-468. https://doi.org/10.1016/j.ijantimicag.2007.07.012
|
| [94] |
Phillips I (2007) Withdrawal of growth-promoting antibiotics in Europe and its effects in relation to human health. Int J Antimicrob Agents 30: 101-107. https://doi.org/10.1016/j.ijantimicag.2007.02.018
|
| [95] |
Bywater RJ (2005) Identification and surveillance of antimicrobial resistance dissemination in animal production. Poult Sci 84: 644-648. https://doi.org/10.1093/ps/84.4.644
|
| [96] |
Casewell M, Friis C, Marco E, et al. (2003) The European ban on growth-promoting antibiotics and emerging consequences for human and animal health. J Antimicrob Chemother 52: 159-161. https://doi.org/10.1093/jac/dkg313
|
| [97] |
Acar J, Casewell M, Freeman J, et al. (2000) Avoparcin and virginiamycin as animal growth promoters: a plea for science in decision-making. Clin Microbiol Infect 6: 477-482. https://doi.org/10.1046/j.1469-0691.2000.00128.x
|
| [98] | Rahman MRT, Fliss I, Biron E (2022) Insights in the development and uses of alternatives to antibiotic growth promoters in poultry and swine production. Antibiotics (Basel) 11. https://doi.org/10.3390/antibiotics11060766 |
| [99] | U.S. Food & Drug Administration CfVMCVM GFI #213 new animal drugs and new animal drug combination products administered in or on medicated feed or drinking water of food-producing animals: recommendations for drug sponsors for voluntarily aligning product use conditions with GFI #209 (2013). Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/cvm-gfi-213-new-animal-drugs-and-new-animal-drug-combination-products-administered-or-medicated-feed |
| [100] | U.S. Food & Drug Administration CfVM2021 summary report on antimicrobials sold or distributed for use in food-producing animals (2022). Available from https://www.fda.gov/media/163739/download?attachment |
| [101] |
Li Y, Ed-Dra A, Tang B, et al. (2022) Higher tolerance of predominant Salmonella serovars circulating in the antibiotic-free feed farms to environmental stresses. J Hazard Mater 438: 129476. https://doi.org/10.1016/j.jhazmat.2022.129476
|
Figures(2)
Noah T Thompson, David A Kitzenberg, Daniel J Kao. Persister-mediated emergence of antimicrobial resistance in agriculture due to antibiotic growth promoters[J]. AIMS Microbiology, 2023, 9(4): 738-756. doi: 10.3934/microbiol.2023038
| Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in Jalisco. The seismic velocities are in kilometers per second | ||||||
| Geol. Feat. | Ref. Stat. | Azimuth | 15±5 km Depth | 35±10 km Depth | 100±50 km Depth | 250±100 km Depth |
| Tularosa Basin | Z23A | -2.4 | 2.4 | 3.3 to 3.5 | 3.8 | 3.9 to 4.3 |
| Diablo Plateau | 224A | -0.2 | 3.2 to 3.4 | 3.5 to 3.6 | 3.8 to 3.9 | 3.9 |
| Delaware Basin | SC61 | 6 | 3.2 to 3.5 | 3.5 to 3.7 | 3.9 to 4.1 | 3.9 to 4.2 |
| Fort Davis | SC61 | 6 | 3.3 | 3.6 | 3.9 | 4 |
| Sheffield Channel | SC73 | 9 | 3.3 | 3.4 | 3.7 | 3.8 |
DownLoad: CSV| Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in California. The seismic velocities are in kilometers per second | ||||||
| Geol. Feat. | Ref. Stat. | Azimuth | 15±5 km Depth | 35±10 km Depth | 100±50 km Depth | 250±100 km Depth |
| Tularosa Basin | Z23A | 111 | 2.7 to 2.9 | 1.3 to 1.7 | 3.7 to 3.8 | 4 |
| Diablo Plateau | 224A | 113 | 3.2 to 3.6 | 3.7 to 3.5 | 3.8 | 3.9 to 4.1 |
| Delaware Basin | SC61 | 110 | 2.2 to 3.5 | 3.5 to 3.7 | 3.9 to 4 | 4 to 4.3 |
| Fort Davis | 326A | 113 | 3.2 to 3.4 | 3.6 | 3.8 to 3.9 | 4.1 |
| Sheffield Channel | SC73 | 112 | 2.5 to 2.8 | 2.6 | 3.6 | 3.1 |
DownLoad: CSV| Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in Samoa. The seismic velocities are in kilometers per second | ||||||
| Geol. Feat. | Ref. Stat. | Azimuth | 15±5 km Depth | 35±10 km Depth | 100±50 km Depth | 250±100 km Depth |
| Sierra Blanca NM | SC19 | 51 | 1.1 | 1.5 | 1.8 | 1.8 |
| Tularosa Basin | Z23A | 51 | 3 | 4.2 | 4.8 | 5 to 5.3 |
| Diablo Plateau | 224A | 52 | 4 to 4.3 | 4.1 to 4.6 | 4.8 to 5.5 | 4.9 to 5.4 |
| Delaware Basin | SC61 | 53 | 3.7 to 4.7 | 4.1 to 5.1 | 4.6 to 6 | 4.5 to 4.7 |
| Fort Davis | 426A | 54 | 4.1 to 4.2 | 4.2 to 4.4 | 5.1 | 5.1 to 5.9 |
| Sheffield Channel | SC73 | 54 | 3.9 | 2.8 | 4.9 | 4.9 |
DownLoad: CSV| NETWORK | STATION | LAT | LON | NETWORK | STATION | LAT | LON |
| TA | 121A | 32.5324 | –107.7851 | TA | TASL | 34.9454 | –106.4565 |
| TA | 122A | 32.6995 | –107.0005 | TA | TASM | 34.9455 | –106.46 |
| TA | 123A | 32.6349 | –106.2622 | TA | TASN | 34.9455 | –106.46 |
| TA | 124A | 32.7001 | –105.4544 | TA | TASO | 34.9455 | –106.46 |
| TA | 125A | 32.6588 | –104.6573 | TA | TASP | 34.9455 | –106.46 |
| TA | 126A | 32.6462 | –104.0204 | TA | TVZX | 34.0733 | –106.9196 |
| TA | 127A | 32.6764 | –103.3575 | TA | X21A | 34.4457 | –107.7857 |
| TA | 128A | 32.6213 | –102.485 | TA | X22A | 34.5058 | –107.0102 |
| TA | 221A | 32.0094 | –107.7782 | TA | X23A | 34.581 | –106.1881 |
| TA | 222A | 32.1046 | –107.1013 | TA | X24A | 34.5646 | –105.4349 |
| TA | 223A | 32.0062 | –106.4276 | TA | X25A | 34.5271 | –104.6621 |
| TA | 224A | 32.076 | –105.5226 | TA | X26A | 34.5508 | –103.8103 |
| TA | 225A | 32.1101 | –104.8229 | TA | X27A | 34.6469 | –103.0974 |
| TA | 226A | 32.0618 | –104.1014 | TA | X28A | 34.5185 | –102.1973 |
| TA | 226B | 32.0778 | –104.1654 | TA | Y21A | 34.0087 | –107.674 |
| TA | 227A | 32.012 | –103.2924 | TA | Y22A | 33.937 | –106.9652 |
| TA | 228A | 32.118 | –102.5918 | TA | Y22C | 34.0741 | –106.9211 |
| TA | 324A | 31.4425 | –105.4828 | TA | Y22D | 34.0739 | –106.921 |
| TA | 325A | 31.3711 | –104.9712 | TA | Y22E | 34.0742 | –106.9208 |
| TA | 326A | 31.3165 | –103.9786 | TA | Y22F | 34.0741 | –106.9209 |
| TA | 327A | 31.3691 | –103.4923 | TA | Y23A | 33.9315 | –106.0549 |
| TA | 328A | 31.3818 | –102.8097 | TA | Y24A | 33.9257 | –105.4361 |
| TA | 425A | 30.7862 | –104.9857 | TA | Y25A | 33.9229 | –104.6928 |
| TA | 426A | 30.6689 | –104.0293 | TA | Y26A | 33.9232 | –103.8246 |
| TA | 427A | 30.8498 | –103.4018 | TA | Y27A | 33.8839 | –103.1633 |
| TA | 428A | 30.7263 | –102.6847 | TA | Y28A | 33.9086 | –102.2479 |
| TA | 526A | 30.0609 | –104.0898 | TA | Z21A | 33.3086 | –107.6712 |
| TA | 527A | 30.1456 | –103.6119 | TA | Z22A | 33.2555 | –106.9639 |
| TA | 528A | 30.1615 | –102.788 | TA | Z23A | 33.2621 | –106.2319 |
| TA | 529A | 30.1246 | –102.2204 | TA | Z24A | 33.3298 | –105.3649 |
| TA | 626A | 29.554 | –104.1335 | TA | Z25A | 33.2797 | –104.7171 |
| TA | 627A | 29.4528 | –103.3887 | TA | Z26A | 33.2716 | –103.9798 |
| TA | 628A | 29.4862 | –102.8885 | TA | Z27A | 33.315 | –103.2145 |
| TA | MSTX | 33.9696 | –102.7724 | TA | Z28A | 33.2884 | –102.3866 |
DownLoad: CSV| NETWORK | STATION | LAT | LON | NETWORK | STATION | LAT | LON |
| XR | SC04 | 34.5228 | –105.8119 | XR | SC40 | 32.9317 | –103.54 |
| XR | SC05 | 34.5715 | –105.0554 | XR | SC41 | 32.9833 | –103.2056 |
| XR | SC06 | 34.5145 | –104.2664 | XR | SC42 | 32.8728 | –102.8612 |
| XR | SC07 | 34.1838 | –105.6877 | XR | SC43 | 32.9426 | –102.5369 |
| XR | SC08 | 34.1567 | –105.4697 | XR | SC44 | 32.7572 | –105.947 |
| XR | SC09 | 34.1517 | –105.0013 | XR | SC45 | 32.6337 | –105.1552 |
| XR | SC10 | 34.1937 | –104.6666 | XR | SC46 | 32.654 | –104.3614 |
| XR | SC11 | 34.2323 | –104.2959 | XR | SC47 | 32.629 | –103.6257 |
| XR | SC12 | 34.2148 | –103.9116 | XR | SC48 | 32.6899 | –102.905 |
| XR | SC13 | 34.2135 | –103.5269 | XR | SC49 | 32.443 | –106.064 |
| XR | SC14 | 33.9682 | –105.7695 | XR | SC50 | 32.3895 | –105.6153 |
| XR | SC15 | 33.8308 | –105.0255 | XR | SC51 | 32.3673 | –105.1718 |
| XR | SC16 | 33.8903 | –104.3043 | XR | SC52 | 32.488 | –104.8272 |
| XR | SC17 | 33.893 | –103.5446 | XR | SC53 | 32.3766 | –104.3192 |
| XR | SC18 | 33.8774 | –102.8409 | XR | SC54 | 32.2837 | –104.0398 |
| XR | SC19 | 33.5188 | –105.9744 | XR | SC55 | 32.1712 | –103.6733 |
| XR | SC20 | 33.6042 | –105.5935 | XR | SC56 | 32.3554 | –103.3986 |
| XR | SC21 | 33.5975 | –105.1655 | XR | SC57 | 32.3691 | –102.8513 |
| XR | SC22 | 33.5682 | –104.7542 | XR | SC58 | 32.2888 | –102.5482 |
| XR | SC23 | 33.5995 | –104.3282 | XR | SC59 | 31.9694 | –105.1481 |
| XR | SC25 | 33.5806 | –103.5482 | XR | SC60 | 32.0937 | –104.4877 |
| XR | SC26 | 33.5044 | –103.1184 | XR | SC61 | 31.9895 | –103.6911 |
| XR | SC27 | 33.5385 | –102.8207 | XR | SC62 | 32.0119 | –102.9373 |
| XR | SC28 | 33.5662 | –102.4915 | XR | SC63 | 31.8029 | –104.8464 |
| XR | SC29 | 33.3102 | –105.6705 | XR | SC64 | 31.6996 | –104.4258 |
| XR | SC30 | 33.2738 | –105.17 | XR | SC65 | 31.727 | –104.0178 |
| XR | SC31 | 33.259 | –104.3415 | XR | SC66 | 31.6679 | –103.7363 |
| XR | SC32 | 33.1939 | –103.5979 | XR | SC67 | 31.7051 | –103.3951 |
| XR | SC33 | 33.2334 | –102.8343 | XR | SC68 | 31.8027 | –102.77 |
| XR | SC34 | 32.9513 | –105.8163 | XR | SC69 | 31.6905 | –102.588 |
| XR | SC35 | 32.9369 | –105.5153 | XR | SC70 | 31.3663 | –103.7374 |
| XR | SC36 | 33.0053 | –105.18 | XR | SC71 | 31.6463 | –103.0655 |
| XR | SC37 | 32.937 | –104.6192 | XR | SC72 | 31.1096 | –103.6346 |
| XR | SC38 | 32.9288 | –104.3402 | XR | SC73 | 30.9611 | –102.9875 |
| XR | SC39 | 33.0286 | –103.8453 | XR | SC74 | 31.0024 | –102.6771 |
| XR | SC75 | 31.8742 | –105.952 |
DownLoad: CSV| Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in Jalisco. The seismic velocities are in kilometers per second | ||||||
| Geol. Feat. | Ref. Stat. | Azimuth | 15±5 km Depth | 35±10 km Depth | 100±50 km Depth | 250±100 km Depth |
| Tularosa Basin | Z23A | -2.4 | 2.4 | 3.3 to 3.5 | 3.8 | 3.9 to 4.3 |
| Diablo Plateau | 224A | -0.2 | 3.2 to 3.4 | 3.5 to 3.6 | 3.8 to 3.9 | 3.9 |
| Delaware Basin | SC61 | 6 | 3.2 to 3.5 | 3.5 to 3.7 | 3.9 to 4.1 | 3.9 to 4.2 |
| Fort Davis | SC61 | 6 | 3.3 | 3.6 | 3.9 | 4 |
| Sheffield Channel | SC73 | 9 | 3.3 | 3.4 | 3.7 | 3.8 |
| Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in California. The seismic velocities are in kilometers per second | ||||||
| Geol. Feat. | Ref. Stat. | Azimuth | 15±5 km Depth | 35±10 km Depth | 100±50 km Depth | 250±100 km Depth |
| Tularosa Basin | Z23A | 111 | 2.7 to 2.9 | 1.3 to 1.7 | 3.7 to 3.8 | 4 |
| Diablo Plateau | 224A | 113 | 3.2 to 3.6 | 3.7 to 3.5 | 3.8 | 3.9 to 4.1 |
| Delaware Basin | SC61 | 110 | 2.2 to 3.5 | 3.5 to 3.7 | 3.9 to 4 | 4 to 4.3 |
| Fort Davis | 326A | 113 | 3.2 to 3.4 | 3.6 | 3.8 to 3.9 | 4.1 |
| Sheffield Channel | SC73 | 112 | 2.5 to 2.8 | 2.6 | 3.6 | 3.1 |
| Approximated Rayleigh seismic wave velocities for some of the Geological Features in the area of Interest Calculated with the event in Samoa. The seismic velocities are in kilometers per second | ||||||
| Geol. Feat. | Ref. Stat. | Azimuth | 15±5 km Depth | 35±10 km Depth | 100±50 km Depth | 250±100 km Depth |
| Sierra Blanca NM | SC19 | 51 | 1.1 | 1.5 | 1.8 | 1.8 |
| Tularosa Basin | Z23A | 51 | 3 | 4.2 | 4.8 | 5 to 5.3 |
| Diablo Plateau | 224A | 52 | 4 to 4.3 | 4.1 to 4.6 | 4.8 to 5.5 | 4.9 to 5.4 |
| Delaware Basin | SC61 | 53 | 3.7 to 4.7 | 4.1 to 5.1 | 4.6 to 6 | 4.5 to 4.7 |
| Fort Davis | 426A | 54 | 4.1 to 4.2 | 4.2 to 4.4 | 5.1 | 5.1 to 5.9 |
| Sheffield Channel | SC73 | 54 | 3.9 | 2.8 | 4.9 | 4.9 |
| NETWORK | STATION | LAT | LON | NETWORK | STATION | LAT | LON |
| TA | 121A | 32.5324 | –107.7851 | TA | TASL | 34.9454 | –106.4565 |
| TA | 122A | 32.6995 | –107.0005 | TA | TASM | 34.9455 | –106.46 |
| TA | 123A | 32.6349 | –106.2622 | TA | TASN | 34.9455 | –106.46 |
| TA | 124A | 32.7001 | –105.4544 | TA | TASO | 34.9455 | –106.46 |
| TA | 125A | 32.6588 | –104.6573 | TA | TASP | 34.9455 | –106.46 |
| TA | 126A | 32.6462 | –104.0204 | TA | TVZX | 34.0733 | –106.9196 |
| TA | 127A | 32.6764 | –103.3575 | TA | X21A | 34.4457 | –107.7857 |
| TA | 128A | 32.6213 | –102.485 | TA | X22A | 34.5058 | –107.0102 |
| TA | 221A | 32.0094 | –107.7782 | TA | X23A | 34.581 | –106.1881 |
| TA | 222A | 32.1046 | –107.1013 | TA | X24A | 34.5646 | –105.4349 |
| TA | 223A | 32.0062 | –106.4276 | TA | X25A | 34.5271 | –104.6621 |
| TA | 224A | 32.076 | –105.5226 | TA | X26A | 34.5508 | –103.8103 |
| TA | 225A | 32.1101 | –104.8229 | TA | X27A | 34.6469 | –103.0974 |
| TA | 226A | 32.0618 | –104.1014 | TA | X28A | 34.5185 | –102.1973 |
| TA | 226B | 32.0778 | –104.1654 | TA | Y21A | 34.0087 | –107.674 |
| TA | 227A | 32.012 | –103.2924 | TA | Y22A | 33.937 | –106.9652 |
| TA | 228A | 32.118 | –102.5918 | TA | Y22C | 34.0741 | –106.9211 |
| TA | 324A | 31.4425 | –105.4828 | TA | Y22D | 34.0739 | –106.921 |
| TA | 325A | 31.3711 | –104.9712 | TA | Y22E | 34.0742 | –106.9208 |
| TA | 326A | 31.3165 | –103.9786 | TA | Y22F | 34.0741 | –106.9209 |
| TA | 327A | 31.3691 | –103.4923 | TA | Y23A | 33.9315 | –106.0549 |
| TA | 328A | 31.3818 | –102.8097 | TA | Y24A | 33.9257 | –105.4361 |
| TA | 425A | 30.7862 | –104.9857 | TA | Y25A | 33.9229 | –104.6928 |
| TA | 426A | 30.6689 | –104.0293 | TA | Y26A | 33.9232 | –103.8246 |
| TA | 427A | 30.8498 | –103.4018 | TA | Y27A | 33.8839 | –103.1633 |
| TA | 428A | 30.7263 | –102.6847 | TA | Y28A | 33.9086 | –102.2479 |
| TA | 526A | 30.0609 | –104.0898 | TA | Z21A | 33.3086 | –107.6712 |
| TA | 527A | 30.1456 | –103.6119 | TA | Z22A | 33.2555 | –106.9639 |
| TA | 528A | 30.1615 | –102.788 | TA | Z23A | 33.2621 | –106.2319 |
| TA | 529A | 30.1246 | –102.2204 | TA | Z24A | 33.3298 | –105.3649 |
| TA | 626A | 29.554 | –104.1335 | TA | Z25A | 33.2797 | –104.7171 |
| TA | 627A | 29.4528 | –103.3887 | TA | Z26A | 33.2716 | –103.9798 |
| TA | 628A | 29.4862 | –102.8885 | TA | Z27A | 33.315 | –103.2145 |
| TA | MSTX | 33.9696 | –102.7724 | TA | Z28A | 33.2884 | –102.3866 |
| NETWORK | STATION | LAT | LON | NETWORK | STATION | LAT | LON |
| XR | SC04 | 34.5228 | –105.8119 | XR | SC40 | 32.9317 | –103.54 |
| XR | SC05 | 34.5715 | –105.0554 | XR | SC41 | 32.9833 | –103.2056 |
| XR | SC06 | 34.5145 | –104.2664 | XR | SC42 | 32.8728 | –102.8612 |
| XR | SC07 | 34.1838 | –105.6877 | XR | SC43 | 32.9426 | –102.5369 |
| XR | SC08 | 34.1567 | –105.4697 | XR | SC44 | 32.7572 | –105.947 |
| XR | SC09 | 34.1517 | –105.0013 | XR | SC45 | 32.6337 | –105.1552 |
| XR | SC10 | 34.1937 | –104.6666 | XR | SC46 | 32.654 | –104.3614 |
| XR | SC11 | 34.2323 | –104.2959 | XR | SC47 | 32.629 | –103.6257 |
| XR | SC12 | 34.2148 | –103.9116 | XR | SC48 | 32.6899 | –102.905 |
| XR | SC13 | 34.2135 | –103.5269 | XR | SC49 | 32.443 | –106.064 |
| XR | SC14 | 33.9682 | –105.7695 | XR | SC50 | 32.3895 | –105.6153 |
| XR | SC15 | 33.8308 | –105.0255 | XR | SC51 | 32.3673 | –105.1718 |
| XR | SC16 | 33.8903 | –104.3043 | XR | SC52 | 32.488 | –104.8272 |
| XR | SC17 | 33.893 | –103.5446 | XR | SC53 | 32.3766 | –104.3192 |
| XR | SC18 | 33.8774 | –102.8409 | XR | SC54 | 32.2837 | –104.0398 |
| XR | SC19 | 33.5188 | –105.9744 | XR | SC55 | 32.1712 | –103.6733 |
| XR | SC20 | 33.6042 | –105.5935 | XR | SC56 | 32.3554 | –103.3986 |
| XR | SC21 | 33.5975 | –105.1655 | XR | SC57 | 32.3691 | –102.8513 |
| XR | SC22 | 33.5682 | –104.7542 | XR | SC58 | 32.2888 | –102.5482 |
| XR | SC23 | 33.5995 | –104.3282 | XR | SC59 | 31.9694 | –105.1481 |
| XR | SC25 | 33.5806 | –103.5482 | XR | SC60 | 32.0937 | –104.4877 |
| XR | SC26 | 33.5044 | –103.1184 | XR | SC61 | 31.9895 | –103.6911 |
| XR | SC27 | 33.5385 | –102.8207 | XR | SC62 | 32.0119 | –102.9373 |
| XR | SC28 | 33.5662 | –102.4915 | XR | SC63 | 31.8029 | –104.8464 |
| XR | SC29 | 33.3102 | –105.6705 | XR | SC64 | 31.6996 | –104.4258 |
| XR | SC30 | 33.2738 | –105.17 | XR | SC65 | 31.727 | –104.0178 |
| XR | SC31 | 33.259 | –104.3415 | XR | SC66 | 31.6679 | –103.7363 |
| XR | SC32 | 33.1939 | –103.5979 | XR | SC67 | 31.7051 | –103.3951 |
| XR | SC33 | 33.2334 | –102.8343 | XR | SC68 | 31.8027 | –102.77 |
| XR | SC34 | 32.9513 | –105.8163 | XR | SC69 | 31.6905 | –102.588 |
| XR | SC35 | 32.9369 | –105.5153 | XR | SC70 | 31.3663 | –103.7374 |
| XR | SC36 | 33.0053 | –105.18 | XR | SC71 | 31.6463 | –103.0655 |
| XR | SC37 | 32.937 | –104.6192 | XR | SC72 | 31.1096 | –103.6346 |
| XR | SC38 | 32.9288 | –104.3402 | XR | SC73 | 30.9611 | –102.9875 |
| XR | SC39 | 33.0286 | –103.8453 | XR | SC74 | 31.0024 | –102.6771 |
| XR | SC75 | 31.8742 | –105.952 |
