UA_L-DoTT.UA_L-DoTT (University of Alabama’s Large Dataset of Trains and Trucks) is a collection of camera images and 3D LiDAR point cloud scans from five different data sites. Four of the data sites targeted trains on railways and the last targeted trucks on a four-lane highway. Low light conditions were present at one of the data sites showcasing unique differences between individual sensor data. The final data site utilized a mobile platform which created a large variety of viewpoints in images and point clouds. The dataset consists of 93,397 raw images, 11,415 corresponding labeled text files, 354,334 raw point clouds, 77,860 corresponding labeled point clouds, and 33 timestamp files. These timestamps correlate images to point cloud scans via POSIX time. The data was collected with a sensor suite consisting of five different LiDAR sensors and a camera. This provides various viewpoints and features of the same targets due to the variance in operational characteristics of the sensors. The inclusion of both raw and labeled data allows users to get started immediately with the labeled subset, or label additional raw data as needed. This large dataset is beneficial to any researcher interested in machine learning using cameras, LiDARs, or both. The current dataset is publicly available at  All sensors collected loosely synchronized data of the same targets. Time synchronized data allows for comparison of the different data collection systems. This could further support the research done in 11.11.2Timestamp files utilize their own naming convention with only two fields: site and sensor. Each are separated by underscores as well. There are timestamp files associated with each of the individual sensors that were utilized at a given site. For example, the Site 2 timestamps for the 32MR are, “site2_32mr_timestamps.txt”. Additionally, there is a file that contains all the timestamps from all the LiDARs at that site.1.3The LiDAR scans utilize the Point Cloud Data (PCD) file format, which is a standard file format natively supported by the Point Cloud Library (PCL) 1.4Raw camera images utilize the JPEG image standard. The corresponding labeled image data is saved in text files. These text files consist of information describing the rectangular, axis-aligned bounding boxes drawn around targets of interest in the raw images. An example of these bounding boxes overlaid with an image can be seen in When multiple targets exist within an image, each bounding box within the text file is separated by a newline. If the image is a negative scan, then an empty text file containing no targets is created. The contents of one such label file from Site 4 can be seen in 1.5Timestamp files contain a list of the raw file names for the associated sensor with each file’s corresponding POSIX timestamp. The timestamp files allow users to correlate different sensor scans and camera images to help visualize the point clouds. A subsection of the OS2 timestamp file for Site 4 can be seen in 2This dataset was collected across five different sites. Each one had a slightly different collection methodology that matured from site to site. These differences are discussed in the individual site sections below. More information regarding site locations and universal sensor parameters is discussed in Collection Parameters.For train sites, collection began when the train horn was heard, and stopped when the train was no longer in sight. This provided negative scans (scans where no target of interest exists within it) before and after the train that were integrated into the dataset. For the truck site, data was collected continuously as trucks passed through the sensor field-of-view (FOV). As such, there are many more negative scans. This required careful selection of data to be labeled to create a more meaningful dataset, discussed further in the Site 3 section.For collections, all sensors were mounted to a custom built frame, which was then mounted to tripods or a mobile platform. This mount was built using t-slot aluminum extrusion which was anodized black. This held and elevated the sensors in a stationary position throughout the collection. Sensors were re-arranged from site to site, so details of positioning are discussed in the individual site sections. Upright members assisted in preventing interference between the various sensors, with the black anodizing helping to further mitigate this. These uprights also allowed the sensors to be positioned to create vertical scan lines. Any instances of this are discussed in the individual site sections.2.1During collections, all LiDARs were connected to a 1-gigabit network switch. This was then connected to a laptop so all LiDARs were communicated with simultaneously. A custom driver was written in C++ that could collect a scan from each sensor and save it directly to the disk in PCD format. This driver evolved with each collection as issues were identified and corrected. These changes and how they impacted the overall collection are outlined in each of the site sections.The driver saved each scan with a POSIX timestamp in the filename. After collection, the scans were renamed to meet the previously discussed naming convention, and the timestamps were used to create the timestamp files mentioned previously.Any saved LiDAR scan that did not include at least 2,000 points  was categorized as an incomplete scan and was discarded. Therefore, all point clouds in this dataset have a width of at least 2,000 points. A summary of the percentage of incomplete scans across all LiDARs at each site is outlined in To label the point clouds, a custom labeling tool was written in C++ utilizing PCL to annotate them. This tool allowed the research team to go through and manually select any points that were associated with a train or truck. The tool then auto-generated the PCD label file with the previously discussed fields.2.2The Blackfly camera was equipped with a lens with a focal length of 16 mm and an optical format of 1/1.8”. At each site, the camera was connected to the same 1-gigabit network switch as the LiDARs. It was then connected to a laptop executing Windows 10 for interfacing. SpinView 2.0.0.146 included in the Spinnaker SDK provided by the manufacturer was used on the laptop to capture images. Within the software, the acquisition frame rate was enabled and set to 10 FPS to match the speed of the Ouster LiDARs. The recording mode was set to buffered to minimize dropped frames. The compression quality on a range from 0–100 was set to the default of 75. This prioritized image quality over image compression while still reducing overall file size. Additional settings were adjusted for low lighting conditions, discussed in the Site 4 section.Labeled data was produced with a custom labeling tool. This tool enabled manual labeling by clicking on two opposite corners of the target of interest. This created the axis-aligned bounding box. Users adjusted the box size and position to create a more accurate label. The tool then auto-generated the text files based upon the labels.2.3The distance from the sensors to the targets across all sites ranged from 42 m–240 m. Site specific distance ranges are noted in each site section. All data collection was done during sunny to partly cloudy weather except for nighttime collection at Site 4. Minor occlusions existed within the FOV of every site including foliage, utility poles, road signage, chain-link fences, construction equipment, and automobiles.At each site, all sensors collected data using their base collection rates shown in 2.4Site one targeted six trains running on two separate parallel tracks. Distance to targets of interest ranged from 42m–93m. The sensors were stationary as the trains moved both left-to-right and right-to-left through their FOV. The height of the sensors is specified in 2.5During this collection, it was found that a significant portion of the scans were incomplete, as outlined in As mentioned, the custom mount allowed for LiDARs to be attached vertically, creating vertical scan lines. This was the only site where this was done. While changing orientations, trains came through that resulted in a mix of horizontal, vertical, or missing sensors during the changes. Due to the unique combination of different LiDAR positions varying between trains at this site, the orientation for each sensor is outlined in When labeling the data, the static relationship between the trains and the LiDARs was exploited. Since trains will always appear in the same region, a script was written to automatically label every point within a volume aligned with the train tracks. This script was executed on every point cloud collected at this site. Afterwards, an annotator checked the point clouds and corrected any incorrectly identified outliers. However, this system did not work for the scans taken with vertical sensors. As a result, every vertical scan was manually labeled.2.6During the first data collection, the Blackfly was manually zoomed and cropped to a resolution of 2,048 When initially labeling the camera data for Site 1, every image was manually labeled. It was realized that due to the relatively slow speed of the trains and the much higher sampling rate of the camera, many images could be considered “pseudo-duplicates”. The train(s) moved so slowly between frames that the images were nearly identical to one another. Thus, annotators only labeled every 3-7 images. It was at the discretion of the labeler to determine when the scene had changed drastically enough to warrant a new label.2.7This site targeted seven trains using three separate parallel railroad tracks. Distance to targets of interest ranged from 48m–76m. The sensor arrangement was the same as that from Site 1, with the height of the sensors being outlined in 2.8At this collection, the driver was modified to only save every third scan. This effectively reduced the collection speed of the LiDARs down to 2.67 Hz and 3.33 Hz for the Velodyne and Ouster LiDARs respectively. However, this mitigated the bottleneck associated with saving the scans, resulting in significantly more valid scans, as seen in While labeling, the static relationship between targets and sensors was again exploited to automatically label every scan taken. As with Site 1, these scans were then manually verified and corrected if need be.2.9There were no changes with the camera data collection at this site. Thus, the camera base frame rate of 10 Hz was used. Due to the long stretch of train tracks, the camera was occasionally panned to the left and right to display an extended view of the tracks. This added variety to the data collected from this site. When labeling the images for Site 2, the same final methodology used at Site 1 was adopted. It was at the discretion of the annotator to label every 3-7 images based on their determination of when the scene had changed significantly.2.10This site is the only one to target trucks. As such, the sensors were pointed at a highway with many other vehicles passing through the FOV, but only trucks have been annotated. For the purposes of labeling, “trucks” were generally defined as any large, industrial vehicle including 18-wheelers, box trucks, dump trucks, etc. However, due to the sparsity of LiDAR data on some truck targets and inconsistencies among labeling personnel, there are a few, random instances of such vehicles not being labeled. Distance to targets of interest ranged from 48m–240m. Sensors were arranged within the sensor mount similar to that of Site 5 seen in , except Due to the volume of scans collected, the data was down sampled for the labeling process. Roughly every fifth scan was labeled. This translates to a labeled scan rate of 1.6 Hz and 2 Hz for the respective sensors, resulting in slightly less synchronized labeled data. This down sampling had no impact on the data rates for collection of raw data. A summary of the total scans collected and those labeled are outlined in 2.11As mentioned in the LiDAR Data section, scans were originally saved as ASCII PCDs. After collection at Site 2, it was found that the larger file sizes of the ASCII format were the primary contributor to the bandwidth issue resulting in the significant data loss discussed in Sites 1 and 2. To solve this issue, the LiDAR driver was modified to save point cloud data as binary PCDs instead. This significantly reduced the file size and bandwidth required, allowing every scan collected to be saved. As such, this enabled data collection at the original rates of 8 Hz and 10 Hz for the Velodyne and Ouster LiDARs respectively.All sensors had a comparable amount of incomplete scans. The larger amount of incomplete scans outlined in For this site, the static relationship could not be exploited for labeling, as this method would include points belonging to targets that are not of interest. Therefore, every fifth scan was manually labeled.2.12During collection, the camera’s image buffer would occasionally fill up every 4,000–6,000 images. This was due to the large number of images and constant operation of the collection process at a 10 Hz rate. Once the buffer filled, image capturing was momentarily paused to clear the buffer for 15–20 s, and then resumed. Therefore, there are some short gaps in data collection where LiDAR scans have no correlated camera images. To vary the data, the camera was occasionally panned left or right to capture additional viewpoints. Orientation was typically changed for 30–60 min before another change.When initially labeling the camera data, every image was labeled. This was due to the increased speed of the trucks resulting in enough scene change to warrant labeling. However, after the camera was panned left or right, the view was essentially straight up or down the road . When this occurred, the same “pseudo-duplicate” situation arose, and labeling was returned to every 3–7 images based on the annotator’s discretion.2.13This collection occurred near sunset, resulting in scans taken in low light conditions. This new variation in data lets researchers evaluate LiDAR and camera performance at night. Distance to targets of interest ranged from 50 m–160 m. The sensor arrangement within the sensor mount was similar to that of Site 5 seen in , but aga2.14At this site, no changes were made to the driver. Scans were once again saved as a binary PCD and later converted to ASCII. All LiDARs collected data at the base rates indicated in 2.15The last two trains were captured during sunset and nighttime, respectively. Due to these low light conditions, the exposure, gain, and gamma were adjusted during these trains to improve train visibility. The camera frame rate was the base rate shown in 2.16Site 5 data was collected at the same location as Site 4. This time, the sensor mounting frame was attached to the side of a mobile platform with the resulting sensor heights outlined in Once a train approached, collection began and the mobile platform continuously drove around randomly for the duration of the train. This allowed for a large variety of viewpoints to be gathered as the train was passing. Distance to targets of interest ranges from 50 m–160 m. No GPS tracking data is available for exact paths, but the general area can be seen in 2.17Due to the mobile nature of this collection, the LiDARs collected data from their entire 360Now that the sensors are no longer stationary, there was no longer a static relationship that could be exploited. Roughly 1,000 scans from each sensor were targeted for manual labeling. To create additional variety, 333 scans were taken from the start of one train, middle of another, and end of the last. Train specific scan ranges were randomly selected for each sensor.2.18Due to the nature of the mobile collection, there were times when the camera system could not see the train. This resulted in a higher percentage of negative images than other sites. The camera collected data at its base rate. As with the LiDAR, roughly 1,000 images were targeted from across the start, middle, and end of different trains randomly. The motion of the mobile platform produced enough scenic change between images to avoid the need to skip images.Maxwell Eastepp: Methodology, Software, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing. Lauren Faris: Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing. Kenneth Ricks: Conceptualization, Methodology, Writing – review & editing, Supervision, Project administration, Funding acquisition.This material is based upon work supported by the Army Contracting Command, Contract W909MY-19-C-0020. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. Army. The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


A potentially irreversible threshold in Antarctic ice shelf melting would be crossed if the ocean cavity beneath the large Filchner–Ronne Ice Shelf were to become flooded with warm water from the deep ocean. Previous studies have identified this possibility, but there is great uncertainty as to how easily it could occur. Here, we show, using a coupled ice sheet-ocean model forced by climate change scenarios, that any increase in ice shelf melting is likely to be preceded by an extended period of reduced melting. Climate change weakens the circulation beneath the ice shelf, leading to colder water and reduced melting. Warm water begins to intrude into the cavity when global mean surface temperatures rise by approximately 7 °C above pre-industrial, which is unlikely to occur this century. However, this result should not be considered evidence that the region is unconditionally stable. Unless global temperatures plateau, increased melting will eventually prevail. New simulations find that one of Antarctica’s largest ice shelves, the Filchner–Ronne, may be less vulnerable to climate change than previously thought. Melting of the ice shelf initially decreases for many decades, and only increases when global warming exceeds approximately 7 °C. Unlike ice shelves in the Amundsen Sea, which are already in contact with relatively warm water flowing from the deep ocean (≈1 °C)3, the cavity beneath FRIS is currently filled with cold water (≈−2 °C)2. This results in relatively low ice shelf basal melt rates with some regions of refreezing. Whether the FRIS cavity remains in this cold state, or transitions to a warm state akin to the Amundsen Sea, will have significant implications for its source glaciers and ultimately global sea level rise4.In order to constrain long-term projections of sea-level rise, it is crucial to understand the likely response of the Filchner–Ronne Ice Shelf (FRIS) to climate change. FRIS is one of Antarctica’s largest and most dynamically important ice shelves, which accounts for over 10% of the discharge of the entire Antarctic Ice Sheet2 is driven by density gradients between saltier, denser water on the surrounding continental shelf and fresher, lighter water within the cavity. Cold and dense High Salinity Shelf Water (HSSW) flows into the cavity from the strong sea ice formation regions of the Ronne Depression and Berkner Bank  from offshore was simulated to enter the cavity through the Filchner Trough, dramatically increasing basal melt rates. A similar circulation change has been triggered in other ocean models by manually varying the cavity salinity or atmospheric forcing12. However, it remains unclear how easily this circulation change could occur in reality, and over what timescales.More recently, Hellmer et al.13. In the years since, global climate models have reduced their atmospheric biases in the Antarctic region14, which increases confidence in their projections. Regional ice-ocean models, which can downscale these results to ice shelf cavities, have become more sophisticated15 with higher resolution, coupled ice sheet dynamics, and reduced present-day biases. Even so, future projections of ice shelf melting are scarce in the scientific literature.These two conflicting hypotheses for the future of FRIS melting—whether it will decrease or increase—have never been reconciled. Recent advances in Antarctic modelling warrant a re-examination of this topic. All previous simulations projecting WDW intrusion in the 21st century were forced by the same climate model projection, which is now a decade old6 and Hellmer et al.7, showing they are two sides of the same coin. However, the second stage requires extremely strong greenhouse forcing in our simulations, indicating that FRIS melting is unlikely to increase in the 21st century.In this study, we find a two-timescale response of the FRIS cavity to climate warming Fig. b, c. The16. Present-day simulations with this combination of models produce basal melt rates and water mass characteristics exhibiting good agreement with observations, with minimal grounding line drift . It is forced in the atmosphere and at the lateral ocean boundaries with output from the global climate model UKESM (United Kingdom Earth System Model)17: piControl holds CO2 fixed at pre-industrial levels, abrupt-4xCO2 instantaneously quadruples CO2 and then holds it fixed, and 1pctCO2 increases CO2 by 1% per year. All simulations run for 150 years. To investigate longer-term impacts, we then run 50-year extensions of each simulation, where the last decade of UKESM forcing is repeated 5 times. These idealised scenarios were chosen over realistic climate projections, which are shorter with less extreme changes in CO218, and may not induce sufficient warming to trigger a circulation change beneath FRIS.We simulate three idealised scenarios following the CMIP6 protocol (Coupled Model Intercomparison Project phase 6)2 forcing. We therefore use a single ensemble member for each UKESM scenario. UKESM was chosen from CMIP6 due to early data availability, as well as its good representation of the present-day Antarctic region14.The computational expense of ÚaMITgcm means that a large ensemble of long simulations would be impractical. However, the signal-to-noise ratio of forced change to internal climate variability is relatively small in scenarios with such extreme COBoth stages of the response are ultimately driven by freshening of the source waters which feed the FRIS cavity. Average salinity on the continental shelf in front of FRIS Fig.  decreaseThis freshening is caused by a combination of local and remote factors, which we examine using a salt budget analysis for the abrupt-4xCO2 simulation. Integrated salt fluxes over the continental shelf in front of FRIS Fig. a show thFigure ∘E). It also reflects freshening in UKESM, which then enters the ÚaMITgcm domain at the eastern boundary. The Southern Ocean freshening seen in UKESM is a common response to climate warming among CMIP models19 and is primarily caused by increased precipitation, with a smaller role for surface melting of the Antarctic Ice Sheet. Precipitation over the Antarctic Ice Sheet in UKESM is not immediately routed to the ocean, but rather is handled by a snow melting scheme. Note that the relatively low resolution of UKESM may decrease confidence in its ability to accurately simulate coastal dynamics20. Therefore, some uncertainty exists in the relative role of advection in this salt budget, which could be important for FRIS melt rates21.While surface processes dominate, Fig. The changes in surface fluxes and advection are partially offset by increased diffusion. After year 80, a new equilibrium is reached and the total salt flux anomalies oscillate around 0, indicating the freshening has plateaued A decrease in sea ice formation, but only in the later stages of the simulationIn summary, the main causes of freshening are as follows:6, which relied entirely on a reduction in local sea-ice formation.These mechanisms differ from the hypothesis of Nichollsbetween the dense source HSSW (in Ronne Depression and over Berkner Bank) and the less dense FRIS cavity; andbetween the dense outflowing ISW (in Filchner Trough) and the less dense offshore WDW.Continental shelf freshening has implications for the density gradients which drive circulation beneath FRIS. There are two density gradients to consider:A reversal of the first density gradient characterises Stage 1 of the response, when HSSW inflow slows and ice shelf melting decreases. A reversal of the second density gradient characterises Stage 2, when WDW flows into the cavity and ice shelf melting increases.Figure In the Ronne Depression Fig. a, bottomIn the Filchner Trough Fig. b, the caA reversed density gradient between the inflowing HSSW and the FRIS cavity has profound impacts on the ice shelf. Weaker circulation Fig. a means tAlthough basal melt rates are lower, the longer residence time induced by slower circulation means that more heat is ultimately extracted from the source HSSW through ice shelf melting. The resulting supercooled ISW, which flows out into Filchner Trough, becomes colder throughout Stage 1  after 69 years in 1pctCO2 and 14 years in abrupt-4xCO2, which corresponds to ≈3 °C and ≈5 °C of warming respectively. Observed global mean near-surface air temperature is now ≈1 °C above pre-industrial, which corresponds to ≈30 years of the 1pctCO2 experiment Fig. . Our simStage 2 begins when the Filchner Ice Shelf front transitions from a net exporter of supercooled ISW to a net importer of WDW above the surface freezing point (Methods). This transition occurs after 79 years in abrupt-4xCO2 and 147 years in 1pctCO2. In both cases, this corresponds to ≈7 °C of average global near-surface warming relative to pre-industrial.18. It is only the most extreme scenario (SSP5-8.5) which exceeds 7 °C of warming by the end of the century. The trajectory of SSP3-7.0 suggests it would also exceed this threshold in the 22nd century, but this is not obvious for the cooler scenarios (SSP2-4.5 and SSP1-2.6). With regards to the 3–5 °C warming required for detectability of Stage 1, only the most moderate scenario (SSP1-2.6) stays clearly below this range. In all other scenarios, we would expect Stage 1 to become detectable at some point in the second half of the 21st century.UKESM climate projections suggest that 7 °C of warming could only be reached in the 21st century under the highest emissions scenarios. Figure 22. The true timescales could therefore be even longer. Other forcing agents in the ScenarioMIP experiments, such as changes in stratospheric ozone, could also have a regional effect on the Antarctic climate23 which may confound the comparison.This comparison should be interpreted with caution, as UKESM is a single model with a higher climate sensitivity than the CMIP6 ensemble mean6, but no model projections were provided to support this hypothesis. Conversely, previous modelling studies using two closely-related ice-ocean models9 advanced directly to Stage 2 and did not appear to simulate a discernible Stage 1. We hypothesise that these studies may have been overly sensitive to WDW inflow, as they were all forced with the same climate model projection, now two generations old, with no bias corrections. The idealised study of Hazel and Stewart11 is not comparable to our results, as it applied large step-changes in atmospheric variables which triggered an immediate Stage 2 response, with little opportunity for transient changes.Our simulations are the first to identify a two-timescale response of FRIS melting to a warming climate. This result resolves previous disagreement as to whether FRIS melting would increase or decrease in response to climate change. Stage 1 was previously hypothesised based on one year of observationsThe challenge now is to bound the timescales of Stage 1 and Stage 2. Our results call for a larger model intercomparison project of ice shelf melting projections in the Weddell Sea, to quantify the model structural uncertainty in these estimates. Internal climate variability may control the precise timing of WDW pulses, so a range of CMIP models and ensemble members should be used for forcing, as well as a range of resolutions in the ocean and atmosphere. Fully coupled models, which simulate the atmosphere, ocean, and ice sheet simultaneously, should be included as they are developed. These models would simulate atmosphere-ocean feedbacks, which may be important to FRIS, but which cannot be captured by ice-ocean models such as ÚaMITgcm.25 have not existed for long enough to detect a climatological trend in FRIS melting. The few in-situ measurements which exist beneath the ice shelf show large variability related to the strength of circulation5, which masks any trend in cavity water mass properties. Continued observations would allow for better detection of trends in the region, which could then be used to constrain models. In particular, long-term monitoring should continue in the Filchner Trough26, as it could detect cooling and freshening of ISW.At the same time, observational programmes should monitor the FRIS cavity and continental shelf for signs of Stage 1 beginning. It is not possible to make judgements based on existing observations, which have been sparse until recently. Satellite estimates of basal melt ratesFinally, our results suggest that the FRIS region may be less sensitive to climate change than previously thought. In order for melt rates to increase, an extraordinary amount of global warming (7 °C in the UKESM) is required. This far exceeds the pledges of the Paris Agreement, which aims to keep warming below 2 °C. Unless these global mitigation efforts fail dramatically, there is a good chance that the FRIS sector of the Antarctic Ice Sheet could be preserved. However, if global temperatures continue to climb, decreased melting from FRIS should not be considered evidence that the region is unconditionally stable. Our study finds that if continental shelf freshening proceeds far enough, reduced melting will eventually give way to increased melting as warm water floods the cavity. A reduction in FRIS melting could paradoxically be an early warning sign for a later destabilisation of the region.28, which includes ice shelf thermodynamics and sea icethe MITgcm ocean model 29, which governs the movement and geometry of the ice shelves and ice sheetthe Úa ice flow modela custom-built coupler that handles the exchange of information between the two modelsSimulations are performed with a regional Weddell Sea configuration of the ÚaMITgcm model. This coupled framework consists of three parts:30, which uses a Weddell Gyre domain with open boundaries at 30∘E and 61∘S. Horizontal resolution is 0.25 degrees scaled by cosine of latitude, and there are 120 vertical levels with 25 m resolution throughout the FRIS cavity. Bathymetry follows Bedmap231 with updates in the Filchner Ice Shelf cavity from Rosier et al.32 and the grounded iceberg A23-A as defined by Schaffer et al.33. The FRIS ice shelf draft is given by Úa as described below; outside the Úa domain (e.g. for the Larsen and Fimbul ice shelves) the ice shelf draft follows Bedmap2. In regions with thin water columns and steeply sloping ice shelves, the bathymetry is deepened by the minimum amount to prevent the formation of spurious subglacial lakes34, a process hereafter referred to as digging.The ocean component is based on the configuration from Naughten et al.z* coordinate system as in Naughten et al.30, but instead employ MITgcm’s linear free surface scheme. We use an ice shelf drag coefficient of 0.0025 following new observational evidence35. The MITgcm code has also been updated to version 67k. All other parameterisations, including the GM-Redi parameterisation of geostrophic eddies, the KPP vertical mixing scheme, and sea ice elastic-viscous-plastic rheology, follow Naughten et al.30.To ensure numerical stability on the long timescales used in this study, we do not use the 36. Further numerical details are provided by Gudmundsson37. The domain used here consists of the FRIS catchment region38 with zero inflow conditions on the boundaries. Resolution ranges from 300 m to 50 km, with the finest resolution near grounding lines and ice shear margins. A minimum ice sheet thickness of 10 m is imposed. The ice front is constant in time, and any ice which flows past this boundary is assumed to have calved. The freshwater fluxes resulting from calving are not passed to the ocean, as iceberg freshwater flux is already accounted for in MITgcm (see UKESM Forcing below).Úa is a finite-element ice flow model based on the shallow ice stream approximation, with spatially varying resolution, automatic mesh refinement, and adaptive time-stepping3 with firn depths provided by the RACMO2.1 firn densification model39. The surface mass balance is a climatological record also obtained from RACMO2.140 and the basal mass balance is given by MITgcm, as described below. Úa is initialised with the same bathymetry as MITgcm, and ice surface topography from Bedmap231. We invert for the basal slipperiness in the Weertman sliding law and the rate factor in Glen’s Flow Law, using surface ice velocity from the MEaSUREs 2 dataset41 which covers the years 1996–2016. The inversion is based on an iterative adjoint method with Thikonov regularisation, and minimises the cost function between observations and model output of surface velocities. There is an additional term in the cost function to penalise large changes in ice thickness, which reduces drift in the subsequent simulations.This configuration assumes a meteoric ice density of 917 kg/m42 but has been rewritten and extended so it is suitable for a realistic ocean with time-varying forcing and sea ice. For each coupling segment, Úa and MITgcm run independently for 1 year, then stop, exchange data, and restart. This design is known as offline coupling, and is standard practice for most ice sheet-ocean models in the Marine Ice Sheet Ocean Model Intercomparison Project (MISOMIP)43.The coupling algorithm originated with De Rydt and GudmundssonAt each coupling step, MITgcm sends Úa the ice shelf basal melt rates, which Úa then linearly interpolates to all ice shelf nodes. No basal melting is applied in partially grounded elements, to avoid unphysical melting at connected nodes upstream of the grounding line. Úa sends MITgcm the ice shelf geometry and floating mask, linearly interpolated to the MITgcm grid. Each MITgcm cell containing at least one Úa ice shelf node is considered to be floating.42. Newly opened cells are filled with the properties  of nearest neighbours, preferencing vertical neighbours. This ensures that newly opened cells at the ice base are filled with conditions from the ice-ocean boundary layer wherever possible. Newly closed cells become part of the land mask with no changes to the properties of surrounding cells.The MITgcm restart file is then edited to suit the new geometry. As much information as possible is preserved from the previous ocean state, unlike the re-initialisation approach of De Rydt and GudmundssonThe velocity in newly opened cells is first set to zero. If a given water column is newly opened, its velocity remains at zero. Otherwise, the velocity is corrected to preserve the barotropic transport field, by adding or subtracting a small value from the entire water column. This correction is necessary to ensure numerical stability. The barotropic transport field is not preserved in the case where an entire water column closes.The pressure load anomaly of the ice shelf is recalculated at each coupling step, assuming the water displaced by the ice shelf has the temperature and salinity of the uppermost ocean layer. Digging of the MITgcm bathymetry is recalculated each coupling step as the ice shelf draft changes, and previous digging is reversed if it is no longer necessary. Úa does not see these changes in the bathymetry.46. This forcing consists of daily fields for atmospheric variables  and monthly fields for ocean and sea ice variables on the lateral boundaries . The atmospheric fields are used to calculate bulk fluxes within the MITgcm code, which then force the ocean and sea-ice models. We use the first ensemble member of UKESM (r1i1p1f2).MITgcm is forced with output from the UKESM1-0-LL submission to CMIP648. The spatial distribution of this flux follows the observations of Rignot et al.24. UKESM also considers steady iceberg calving through a statistical iceberg model49. To conserve mass, the sum of the meltwater flux and the calving flux is equal to the modelled pre-industrial surface mass balance of Antarctica . This freshwater flux implicitly enters the MITgcm domain via the lateral boundary conditions. Therefore, the meltwater from ice shelves and icebergs is accounted for both within the ÚaMITgcm domain and in the external forcing from UKESM.While this version of UKESM does not include ice shelf cavities, it prescribes a steady three-dimensional coastal freshwater flux representing ice shelf basal melting50 January climatology. SOSE conditions for temperature and salinity are applied on the continental shelf  and extrapolated into ice shelf cavities. UKESM temperature and salinity are used elsewhere in the domain, and sea ice initial conditions  come exclusively from UKESM. This merged approach to initial conditions was chosen due to a poor representation of the continental shelf in the low-resolution UKESM ocean.The initial conditions in MITgcm are a combination of UKESM output for the first month of simulation, and the Southern Ocean State Estimate (SOSE)30. In this domain, the only significant bias of UKESM’s historical simulation compared to the ERA5 reanalysis51 is the coastal winds around Antarctica. These winds are generally too weak and diffuse, which is expected given the low resolution of UKESM. Coastal winds, including katabatic winds and barrier winds, are directed by small-scale features of the ice sheet surface topography which can only be properly captured at high atmospheric resolution52. We counteract this bias using a coastal wind correction, which is applied to the UKESM output when it is passed to MITgcm. This method extends the work of Mathiot et al.52 by applying a spatially varying, time-constant correction to winds near the coastline. The winds are converted to local polar coordinates, and are scaled and rotated such that their time-averaged magnitude and angle agrees with the ERA5 reanalysis over the observational period. Further details of the correction are provided in Supplementary Note The MITgcm configuration has been shown to produce realistic results when forced with atmospheric reanalysesTo prevent sea level drift in MITgcm, the ocean velocity boundary conditions are modified each year. This is necessary due to differences in the UKESM and MITgcm freshwater budgets which would otherwise lead to a net convergence or divergence of volume within the domain. The correction adds or subtracts a small value from the normal velocities at the open boundaries, to produce a volume flux, which relaxes the area-averaged free surface to zero over a 1-year timescale. Interannual variability in the freshwater budget means that this value needs to be updated every year, as a rolling correction.53 and do not change over the simulations. Similarly, the surface mass balance used to force Úa remains constant in time.Freshwater fluxes from iceberg melt are given by the monthly climatology of Storkey et al.An initial standalone ice sheet simulation of 10 years was performed with Úa, forced with basal melt rates from the piControl average of a standalone MITgcm simulation performed previously. The purpose of this Úa simulation was to overcome the initial adjustment period, which can trigger brief but rapid changes in ice geometry. The final geometry from this simulation was used to redefine the MITgcm domain.An ocean-only spinup of 10 years was then performed, forced by years 2880–2889 of the piControl simulation in UKESM. These years were chosen so that the transient simulations would later branch from the correct point of piControl.The coupled ice sheet was activated for a further 20 years of spinup under piControl forcing (years 2890–2909).ÚaMITgcm was spun up in three stages to ensure numerical stability and consistency between the two component models:Three simulations branched from this point, each running for 150 years: the continuation of the piControl case (years 2910–3059), abrupt-4xCO2, and 1pctCO2. The final decade of each forcing scenario was then repeated 5 times for a 50-year extension.v and each year t, we calculate the mean μ and standard deviation σ of the 20-year period around t. The variable v is deemed to be outside the bounds of pre-industrial variability when the range  no longer overlaps between the two simulations. More specifically, whenμ and σ refer to the transient simulation (abrupt-4xCO2 or 1pctCO2), and μPI and σPI to piControl. Stage 1 is considered to be detectable for the first value of t where this holds for both variables, i.e. cooler temperature and lower basal mass loss.To determine the threshold of detectability for Stage 1, we compare two annually averaged timeseries , which varies with the local salinity but not with depth. This quantity is averaged over the ocean cells at the Filchner Ice Shelf front at all depths between the ice shelf base and the seafloor. If the integrated result is negative, this transect has net supercooling with respect to the surface freezing point, indicating a dominance of Ice Shelf Water. This is the case for every year of piControl. Stage 2 begins when the annually averaged value first becomes positive, indicating a new regime of warm water transport into the ice shelf cavity.To determine the threshold at which Stage 2 begins, we calculate the temperature difference from the surface freezing point  to a global sea level contribution (see their Eq. 2). To account for drift in the ice sheet model, we use the difference in VAF between the transient simulation (here abrupt-4xCO2) and the piControl simulation at the same point in time.We follow the method of Goelzer et al.Supplementary InformationPeer Review FileSupplementary Movie 1Description of additional supplementary files


The activity of the left and right central pattern generators (CPGs) is efficiently coordinated during locomotion. To achieve this coordination, the interplay between the CPG controlling one leg and that controlling another must be present. Previous findings in aquatic vertebrates and mammalians suggest that the alternate activation of the left and right CPGs is mediated by the commissural interneurons crossing the midline of the spinal cord. Especially, V0 commissural interneurons mediate crossed inhibition during the alternative activity of the left and right CPGs. Even in humans, phase-dependent modulation of the crossed afferent inhibition during gait has been reported. Based on those previous findings, crossed inhibition of the CPG in one leg side caused by the activation of the contralateral CPG is a possible mechanism underlying the coordination of the anti-phase rhythmic movement of the legs. It has been hypothesized that the activity of the flexor half center in the CPG inhibits the contralateral flexor half center, but crossed inhibition of the extensor half center is not present because of the existence of the double limb support during gait. Nevertheless, previous findings on the phase-dependent crossed inhibition during anti-phase bilateral movement of the legs are not in line with this hypothesis. For example, extensor activity caused crossed inhibition of the flexor half center during bilateral cycling of the legs. In another study, the ankle extensor was inhibited at the period switching from extension to flexion during anti-phase rhythmic movement of the ankles. In this review article, I provide a critical discussion about crossed inhibition mediating the coordination of the anti-phase bilateral rhythmic movement of the legs. Anti-phase bilateral rhythmic movement is produced during locomotion in mammals. The central pattern generators (CPGs) produce the rhythmic movement of the limbs Guertin, . PreviouThe CPGs in the spinal cord produce locomotion in mammals Brown, . Even inIn rats, locomotor activity in each side of the limb was produced even the spinal cord was separated with a midline cut (Kudo and Yamada, Even though the CPG on each side of the spinal cord is possible to produce rhythmic movement in each leg separately, activity of the left and right CPGs must be coordinated during locomotion in humans, according to an observation that humans move the legs coordinately during gait Perry, . PreviouV interneurons, and inhibitory V0D interneurons. The role of the V0 commissural interneurons on the left-right alternation of the motor output is dependent on the frequency of the locomotion cycle (Talpalar et al., V interneurons maintained an alternate activity at low-frequency cycle of locomotion, but switched to a synchronized activity at high-frequency cycle of locomotion (Talpalar et al., V commissural interneurons play a role in the left-right alternation during the high-frequency cycle of the locomotion. In contrast, ablation of the inhibitory V0D interneurons eliminated the left-right alternation at low-frequency cycle of the locomotion, but maintained it at high-frequency cycle of the locomotion (Talpalar et al., D interneurons play a role in the low-left-right coordination of the low-frequency locomotion.V0–V3 commissural interneurons represent the classes of the postmitotic spinal interneurons based on the transcription factor expression (Butt et al., D interneurons receive exaction from the ipsilateral flexor centers and inhibit the contralateral flexor half center (V commissural interneurons are involved in two possible connections. One is that the excitatory V2a interneurons receive excitation from the ipsilateral flexor center and excite V0V interneurons but inhibit contralateral flexor half center through the inhibitory interneurons (V interneurons, and then, V0V interneurons excite the contralateral flexor half center (The pathways mediating the alternate activity of the half centers during locomotion have been proposed by a computational model (Rybak et al., f center . On the rneurons . Anotherf center . Taken tAccording to the discussion above, the alternate activity of the left and right CPGs is likely mediated by crossed inhibition between the CPGs; activity of the half center on one side inhibits the contralateral half center. Crossed inhibition induced by the activity of the CPG in one leg has been examined by observing the effect of the rhythmic movement of one leg on the soleus H-reflex in the contralateral leg at rest in humans. The soleus H-reflex at rest was suppressed by the contralateral leg cycling in the flexion phase and the end of the extension phase (McIllroy et al., However, those previous findings on the unilateral rhythmic movement are not direct evidences indicating the mechanism underlying the coordination of the leg movements during the anti-phase bilateral rhythmic movement during which both left and right half centers are alternately activated. In addition, we have to note supraspinal influence on crossed inhibition during unilateral movement. Unimanual activation of one finger muscle increases interhemispheric inhibition from the active hemisphere to the resting hemisphere (Vercauteren et al., In humans, one important neural event indicating the existence of the inhibitory pathways crossing the midline of the spinal cord is crossed afferent inhibition. The soleus H-reflex in the leg at rest was suppressed by the conditioning stimulation of the tibial nerve in the contralateral leg given 3–33 ms after the test stimulus (Stubbs et al., Soleus H-reflex amplitude is suppressed by the contralateral cutaneous stimulation of the dorsum of the foot in the early stance phase of gait (Suzuki et al., Such crossed afferent inhibition is possible to be induced by passive movement of the contralateral leg. For example, passive rhythmic movement of one leg suppressed the contralateral soleus H-reflex at rest (McIllroy et al., Based on those findings, the activity of one half center is inhibited not only by the active rhythmic movement of the contralateral leg, but also by the passive rhythmic movement of that. The somatosensation is induced, but the CPG is inactive during passive movement. It has been stated that the CPG is capable of reorganizing the sensory input to reconfigure itself to evoke the appropriate pattern Frigon, . AccordiAs discussed above, crossed inhibition of the contralateral CPG is a possible mechanism underlying the anti-phase bilateral rhythmic movement. Crossed inhibition is supposed to be achieved through mutual inhibition between the extensor half centers, between the flexor half centers, or between the extensor half center and contralateral flexor half center (see “Crossed Pathways” section and D commissure interneurons proposed by Rybak et al. and Danner et al. (Rybak et al., This hypothesis by Dietz was supported by a previous finding on the split-treadmill gait (Yang et al., In human experiments, crossed inhibition of the CPG during activity of the contralateral CPG has been examined by comparing the motor output between the unilateral and bilateral rhythmic movements. For example, peak power during unilateral cycling was greater than that during the anti-phase bilateral cycling (Dunstheimer et al., V commissural interneurons (Previous findings on the phase-dependent inhibition of the rhythmically moving one leg induced by the rhythmic movement of the contralateral leg were not in line with the hypothesis proposed by Dietz. For example, during bilateral anti-phase cycling, the activity of the tibialis anterior, biceps femoris, and rectus femoris muscles in the flexion phase was less than that during the unilateral cycling of the tested leg (Ting et al., rneurons . HoweverAnother previous study had tested the soleus H-reflex of the rhythmically moving ankle with and without rhythmic movement of the contralateral ankle (Hiraoka et al., A previous study reported that the soleus H-reflex during bilateral leg cycling was not significantly different from that during the unilateral cycling of the leg contralateral to the tested side (McIllroy et al., An important finding in the previous study by Hiraoka and colleagues is that such phase-dependent inhibition was not present during the in-phase movement of the ankles. This finding suggests that crossed inhibition during the switching period of bilateral rhythmic movement is particularly present during the anti-phase bilateral movement. It has been suggested that the control process of the in-phase bilateral movement of the hands is different from that of the anti-phase movement (Shih et al., Because pre-stimulus background muscle activity level was equivalent between the conditions in this previous study by Hiraoka and colleagues, suppression of the soleus H-reflex during bilateral rhythmic movement observed is likely presynaptic origin. In humans, rhythmic leg activation during gait was greatly controlled by presynaptic inhibition (Stein and Capaday, The conflict between the finding by Hiraoka and colleagues and the finding by Ting and colleagues may be due to the difference in the measurement of the motor status. The experiment by Ting and colleagues observed the EMG and force. Thus, the finding indicates the direct inhibition of the motoneurons causing the decrease in the motor output. In contrast, the experiment by Hiraoka and colleagues observed the suppression of the H-reflex with equal EMG activity. Thus, the finding in the study by Hiraoka and colleagues indicates the inhibition of the excitatory synaptic transmission to the motoneurons. This process is not direct inhibition of the motoneurons, and thus, does not indicate the change in the motor output. Such difference may be related to the conflicting findings between the studies.In the studies on bilateral coordination in humans, as introduced in the present study, actual locomotion is not performed; pedaling movement (Ting et al., The mesencephalic locomotor region and the lateral hypothalamus project to the reticulospinal neurons, and those neurons project to the spinal cord for activating the CPGs (Jordan et al., In this article, previous studies on the phase-dependent crossed inhibition underlying the anti-phase coordination of the rhythmic movement of the legs were reviewed. V0 commissural interneurons mediating crossed inhibition of the contralateral half center likely contribute to the alternate activity of the left and right CPGs. Crossed inhibition of the contralateral leg motor system has been reported in previous studies in humans. Accordingly, alternate activity between the left and right CPGs may be coordinated by crossed inhibition of the spinal cord in humans. On the one hand, a previous finding suggested that the flexor half center is inhibited during the activity of the contralateral extensor half center. On the other hand, another recent study indicated that the extensor half center is inhibited at the period switching the activity from the extensor to flexor half center while the contralateral limb is at the period switching the activity from the flexor to extensor half center. Future studies are needed for further insight into those findings.KH wrote this article.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The degree of polymer covalent functionalization using the BCD-OH and BCD-NH2 derivatives were determined to be 4.27 and 19.19 weight %, respectively. The adsorption properties of the materials were evaluated using bisphenol A as a target molecule. The best fit for the adsorption kinetics was Lagergren’s model  together with Weber’s intraparticle diffusion model in the case of ST-HEMA-GMA-BCD-NH2. The isothermal adsorption evaluation indicated that both systems follow a Langmuir type behavior and afforded a Qmax value of 148.37 mg g−1 and 37.09 mg g−1 for ST-HEMA-GMA-BCD-NH2 and ST-HEMA-GMA-BCD-OH, respectively. The BCD-modified polymers display a degradation temperature of over 400 °C which can be attributed to the existence of hydrogen bonds and BCD thermal degradation pathway in the presence of the polymers.This study presents the synthesis and characterization of polymer derivatives of beta-cyclodextrin (BCD), obtained by chemical grafting onto spherical polymer particles (200 nm) presenting oxirane functional groups at their surface. The polymer spheres were synthesized by emulsion polymerization of styrene (ST) and hydroxyethyl methacrylate (HEMA), followed by the grafting on the surface of glycidyl methacrylate (GMA) by seeded emulsion polymerization. The BCD-polymer derivatives were obtained using two BCD derivatives with hydroxylic (BCD-OH) and amino groups (BCD-NH Bisphenol A (BPA) is one of the most abundant chemical synthetic additives produced for the manufacturing of epoxy resins, polysulfones, unsaturated polyesters, polyacrylate resins polycarbonate plastics, and rubber ,2. HumanDifferent methods for BPA removal were directed to biological treatment, membrane adsorption processes, or advanced oxidation . The bioThe drawbacks of these methods are not only related to the potential of generating harmful by-products but also to the difficulty of controlling the necessary parameters for efficient removal of BPA  and laborious/expensive synthesis methods for adsorption membranes . d-glucose units linked together by α-1,4-glucosidic bonds with hydrophilic surface and hydrophobic internal hollow + or [M+H]+ were not identified but the mass 1215 corresponds to a complex adduct [2M+Na]2+.The ST-HEMA-GMA emulsion was dried in an oven at 70 °C on a glass plate. A solid powder was obtained after the water evaporation. 0.5 g ST-HEMA-GMA powder was dispersed in 10 mL DMSO and heated to 80 °C. After 30 min, 0.6 g BCD-OH was added to the mixture together with 0.01 mL pyridine, as catalysis. The reaction was kept to the temperature for 12 h and precipitated into hot water. The obtained white powder was filtered and dried in the oven.2 was added to the mixture together with 0.01 mL pyridine, as catalysis. The reaction was kept to the temperature for 12 h and precipitated into hot water. The obtained white powder was filtered and dried in the oven.0.5 g ST-HEMA-GMA powder was dispersed in 10 mL DMSO and heated to 80 °C. After 30 min, 0.7 g BCD-NHThe morphology of the polymer nanoparticles was evaluated using transmission electron microscopy (TEM) , at 300 kV acceleration voltage, at a 1 Å resolution. −1 in 4000–600 cm−1 region. The samples were analyzed using the attenuated total reflection (ATR) technique.FTIR spectra were recorded on a Bruker VERTEX 70 spectrometer using 32 scans with a resolution of 4 cm−1. The BCD content was quantified by determining the reducing sugars of the polymer using concentrated H2SO4 acidolysis and phenol colorimetric analysis [The UV-Vis spectra were recorded using a V-550 Able Jasco spectrophotometer, using a bandwidth of 1 nm, and a scanning speed of 1000 nm minanalysis . The pro−1; samples mass: ∼3 mg; temperature range: room temperature −700 °C; heating rate: 10 °C min−1 in an alumina crucible.The thermogravimetric analyses (TGA) were performed using a Netzsch TG 209 F3 Tarsus equipment considering the next parameters: nitrogen atmosphere flow rate 20 mL min1H NMR spectra were recorded at 500 MHz using the solvent peaks as internal references and 13C NMR spectra were recorded at 125 MHz. All NMR experiments were conducted according to the literature, and they can be found in the NMR experiments were carried out on Bruker Avance III 500, Matrix-assisted laser desorption ionization time of flight (MALDI-TOF) mass spectrometry experiments were carried out on Shimadzu AXIMA Performance, operated in high-resolution reflectron mode using α-Cyano-4-hydroxycinnamic acid as matrix.The fluorescence spectra have been registered using a FP-6500 Able Jasco spectrofluorometer.2, 2). Therefore, the synthesis strategy involved the emulsion polymerization of ST-HEMA system [2 (after BCD-NH2 grafting to the polymeric particle).The first stage of our study consisted in the synthesis of polymeric particles presenting the capacity for the chemical attachment of BCD derivatives ). Thus, using a calibration curve for fluorescence intensity depending on the bisphenol A concentration (0 (mg L−1) and ce (mg L−1) are the initial and final solution concentrations of bisphenol A and m is the mass of BCD modified polymer particles (mg).The organic pollutants present in water, such as bisphenol A, can be removed relatively easily and at a low cost by adsorption processes that can exploit the presence of BCD cage structure immobilized on polymeric supports . In thisntration , the amoe values obtained were 9.51 and 12.57 mg/g for ST-HEMA-GMA-BCD-OH and ST-HEMA-GMA-BCD-NH2, respectively. The higher Qe value obtained for the polymers modified using the BCD-NH2 can be related to the higher BCD grafting degree. It is easily observed that the large difference in grafting efficiency is in contrast with the relatively small difference between the adsorption capacity at equilibrium. This can be explained by the formation of hydrogen bonds between the hydroxyl groups of BCD-OH and bisphenol A [2 are evident. Consequently, a kinetic analysis of the adsorption process was performed for ST-HEMA-GMA-BCD-NH2 ), Ho’s pseudo-second-order model (Equation (3)), and Weber’s intra diffusion model (Equation (4)):e (mg g−1) and Qt (mg g−1) are the amounts of bisphenol A adsorbed per unit mass of ST-HEMA-GMA-BCD-NH2 at equilibrium and t (min), respectively. k1 is the pseudo-first-order adsorption rate constant (min−1), k2 is the pseudo-second-order adsorption rate constant (g (mg min)−1), and Kp is the intraparticle diffusion constant (mg g−1 min−0.5).e-Qt) versus t, t/Qt versus t, and Qt versus t, respectively  together with Weber’s intraparticles diffusion model for the step that controls the mass transfer  indicates the amount of adsorbate at equilibrium; Qmax (mg g−1) indicates the maximum amount of adsorbate at equilibrium; ce (mg L−1) is the equilibrium concentration of the adsorbate in the solution; KL (L mg−1) and KF (L mg−1) are the Langmuir and Freundlich constants, respectively; and n is the heterogeneity factor.The study of the adsorption isotherms offers information on the interaction between the adsorbate and the adsorbent and allows the determination of the adsorption capacity of the adsorbent, which is an important parameter for system evaluation. The most intensively used isotherm adsorption model are Langmuir (Equation (5)) and Freundlich (Equation (6)).2 and ST-HEMA-GMA-BCD-OH are represented in , Equation (6), and the parameters calculated are presented in The isothermal adsorption at the equilibrium of bisphenol A by ST-HEMA-GMA-BCD-NHented in . From th2 and ST-HEMA-GMA-BCD-OH can be regarded as a monolayer adsorption process. This means that the adsorption sites located on the surface of the polymer particles were homogeneously distributed and an equivalent adsorption force is displayed. Additionally, it could be evaluated from the Langmuir equation that the Qmax for bisphenol A was 148.37 mg g−1 and 37.09 mg g−1 for ST-HEMA-GMA-BCD-NH2 and ST-HEMA-GMA-BCD-OH, respectively. (max) can be correlated to the amount of BCD grafted to the polymer particles [2 afforded a larger substitution degree which is reflected by the enhanced Qmax value.The comparison of the Langmuir and Freundlich isotherm parameters as listed in ctively.  For a crarticles . Thus, dmax for the adsorption of bisphenol A using different adsorbents  to the surface of the polymer particles was qualitatively confirmed by FT-IR spectroscopy and quantitatively by acidolysis and phenol colorimetric analysis. The degree of polymer functionalization using the BCD-OH and BCD-NH2 derivatives were 4.27 and 19.19 weight %. A monoamine derivative of BCD was synthesized, characterized by NMR and mass spectroscopy. The BCD derivative was chemically grafted to ST-HEMA-GMA particles obtained by seeded emulsion polymerization. The GMA was used during the seeded polymerization due to its high reactivity towards -OH and NHe value and for R2), together with Weber’s intraparticles diffusion model for the step that controls the mass transfer in the case of ST-HEMA-GMA-BCD-NH2. The isothermal adsorption evaluation indicated that both systems follow a Langmuir type behavior and afforded a Qmax value of 148.37 mg g−1 and 37.09 mg g−1 for ST-HEMA-GMA-BCD-NH2 and ST-HEMA-GMA-BCD-OH, respectively. Due to the higher reactivity of BCD-NH2, a larger substitution degree of the polymer particles was obtained, which is reflected by the enhanced Qmax value.The adsorption properties of the materials were evaluated using bisphenol A as target molecule. The results from the mathematical models for the adsorption kinetics process indicated that the best fit was Lagergren’s model (both for QThe thermogravimetric analysis of the materials indicated that the functionalization of the polymer particles afforded an increase of the thermal resistance. Thus, the BCD modified polymers present a degradation temperature of over 400 °C, which can be attributed to the hydrogen bonds and BCD thermal degradation pathway in the presence of the polymers.


The petroleum industry is a major source of energy and economic development but can pollute the environment and negatively impact animal and human health. Heavy metals are environmental pollutants and can reach the human body in direct and indirect ways. We investigated the levels of heavy metals in the soil and in the milk, blood, muscle, liver, and kidney of Arabian camels living near a petroleum industry site in the eastern region of Saudi Arabia. Our results revealed increased lead levels, cadmium, nickel, and vanadium in the soil and different tissues of camels. In addition, liver and kidney dysfunction, tissue damage, oxidative stress, inflammation, and apoptosis were reported. These findings may be of public interest and call attention to the assessment of the impact of the petroleum industry on the environment and the health of nearby communities.Camelus dromedarius) in the eastern region of Saudi Arabia, pointing to HMs accumulation, tissue injury, redox imbalance, inflammation, and apoptosis. Soil and camel samples  were collected from a site near an oil industry field and another two sites to analyze HMs. Pb, Cd, Ni, and V were increased in the soil and in the camel’s milk, blood, muscle, liver, and kidney at the polluted site. Serum aminotransferases, urea, and creatinine were elevated, and histopathological alterations were observed in the liver and kidney of camels at the oil industry site. Hepatic and renal lipid peroxidation, pro-inflammatory cytokines, Bax, and caspase-3 were increased, whereas cellular antioxidants and Bcl-2 declined in camels at the oil extraction site. In conclusion, the oil industry caused soil and tissue accumulation of HMs, liver and kidney injury, oxidative stress, and apoptosis in camels living close to the oil extraction site. These findings pinpoint the negative impact of the oil industry on the environment, animal, and human health.The petroleum industry can impact the environment and human health. Heavy metals (HMs), including lead (Pb), cadmium (Cd), nickel (Ni), and vanadium (V), are toxic pollutants found in petroleum that can cause several severe diseases. This study investigated the impact of the oil industry on the Arabian camel ( The petroleum industry potentially impacts water, soil, air, and community health . Exposur2+ and Ni2+ to form metalloporphyrins [Lead (Pb) and cadmium (Cd) are hazardous HM pollutants produced through various human activities, including the oil industry . The tworphyrins . V is rerphyrins . Besidesrphyrins . Given irphyrins . Schlesirphyrins . Althougrphyrins . Accordirphyrins . In addirphyrins . Human erphyrins .The presence of HMs in the drilling waste discharges poses a risk of contaminating the environment. Accumulation of HMs in the soil adversely affects its properties and could restrict the biodegradation of organic contaminants. In addition, the continuous leaching of HMs can contaminate subsurface water resources and poses a major risk to human health . ProximiCamelus dromedarius) in the eastern region of Saudi Arabia. In addition, we evaluated the histological changes, oxidative stress biomarkers, inflammation, and apoptosis in the liver and kidney of camels.Given the hazardous impact of the oil industry, this study investigated the concentration of some HMs in different tissues of camels , Urayarah  and Rodaht Khoraim  (w/v) in cold phosphate-buffered saline (PBS). The homogenate was centrifuged, and the clear supernatant was separated to determine malondialdehyde (MDA), a marker of lipid peroxidation (LPO), and the antioxidants (reduced glutathione (GSH), superoxide dismutase (SOD), and catalase (CAT)).Thirty soil samples were collected from three different regions in the eastern region of KSA to assay HMs contamination. In addition to the soil samples, blood, milk, muscle, liver, and kidney samples were collected from 30 ce site) . Site 3 Pb, Cd, Ni, and V were determined in the collected soil, milk, blood, and tissue samples using ELAN 9000 ICP-MS . In this assay, a 200 mg sample was mixed with 2 mL nitric acid in a clean digestion beaker and heated at 140 °C for 40 min. The digest was filtered, and ultrapure water was added to bring the filtrate volume to 10 mL. A blank digest was prepared in the same way. For calibration and quality control, standard references  were used. The linear rank of the methodology was assured by analyzing different standards for each element, and all standards were used in duplicate to determine the precision of the analysis. CP-MS detects the elements at a parts-per-trillion level. Ultrapure water was used to prepare blanks and calibration standards, and three replicate determinations were performed for each sample.Levels of ALT, AST, creatinine, and urea were assayed in blood samples collected from camels at the three studied sites with Spinreact  reagent kits according to the provided instructions.To determine MDA levels in the liver and kidney, a 200 µL sample was mixed with 400 µL thiobarbituric acid  and 1.2 mL O-phosphoric acid (1%). The mixture was heated for 45 min at 95 °C, 0.8 mL n-butanol was added, followed by vortexing for 1 min and centrifugation for 10 min at 2000 rpm. The upper layer was separated, and the absorbance was measured at 535 nm . GSH conTNF-α and IL-1β were assayed in the liver and kidney samples using specific ELISA kits  as per the manufacturer’s protocol.The fixed liver and kidney samples were processed for paraffin embedding, and 5μm sections were cut using a microtome. The sections were processed for staining with hematoxylin and eosin (H&E) as previously described  and exam−ΔΔCt method [The liver and kidney samples were crushed into a fine powder with a mortar and a pestle under liquid nitrogen. Total RNA was isolated from the powdered samples using an RNA Purification Kit  according to the manufacturer’s instructions. The extracted RNA was quantified using a nano-drop, and samples with A260/A280 ≥ 1.8 were selected. One µg RNA was reverse transcribed into cDNA using a high-capacity cDNA reverse transcription kit . The synthesized cDNA was amplified using Maxima SYBR Green/ROX qPCR master mix  in 10 µL reaction volume and the primers listed in t method .p value < 0.05 was considered statistically significant.The data were presented as the mean ± standard deviation (SD). The normality of the data was checked with the Shapiro-Wilk test. The statistical comparisons were performed using two-way ANOVA followed by Tukey’s post-hoc test on GraphPad Prism 8. A p < 0.001; p < 0.001) and site 2 (p < 0.01) (p < 0.001) in Ni  B. Site 31) in Ni C and V (1) in Ni D concent1) in Ni .p < 0.001; p < 0.01) and site 2  as depicted in p < 0.001; p < 0.001) in camels at the polluted site, as represented in p < 0.001), milk (p < 0.05) and muscle (p < 0.001) Ni levels as compared to samples collected from the reference site (p < 0.001) increase in the blood, milk, and muscle samples from camels at site 3  when compared with samples from animals at site 1  as compared to the reference site samples. Hepatic Cd (p < 0.001). Like the liver, all HMs showed significant increases in the kidney samples collected from camels at site 3 compared with the corresponding reference site samples  elevation in the aminotransferases, creatinine, and urea in the serum of camels at site 3 when compared with the other two sites.To assess the probable negative impact of the accumulated HMs on the liver and kidney function in camels, serum ALT A, AST FB, creatiLiver and kidney samples were collected from camels living at the 3 sites, and sections were prepared, stained, and examined. While animals at site 1 A,B showep < 0.001) and kidney (p < 0.001) of camels at site 3, whereas GSH, SOD, and CAT were declined. In addition, we determined the mRNA abundance of PTGS1 (p < 0.001).To evaluate redox imbalance in the liver and kidney of camels at site 3 as compared to the camels at sites 1 and 2, we determined the levels of MDA A and GSHof PTGS1 A, and PTof PTGS1 B, and bop < 0.001). In support of the gene expression data, the protein levels of TNF-α (p < 0.001) and kidney (p < 0.001) of camels at site 3 as compared to the other sites.mRNA abundance of TNF-α A and IL-of TNF-α C and IL-of TNF-α D were elp < 0.001) and significant upregulation of Bax (p < 0.001) and caspase-3 (p < 0.001) mRNA levels in both liver and kidney of camels at site 3.Apoptosis was evaluated in the liver and kidney of camels through the determination of the mRNA abundance of Bcl-2 A, Bax FB, and caEnvironmental contamination with HMs released from oil extraction, mining, stone quarrying, and other industries occurs commonly in Saudi Arabia and can negatively impact animal and human health ,42,43. TPb, Cd, Ni, and V were increased in the blood, milk, meat, liver, and kidney of camels near the oil extraction site, demonstrating the negative effect of this industry on both camels and human beings. Exposure of the human body to HMs could occur by consuming contaminated water, plants, meat, and dairy products. HMs can easily be transmitted to animals via consuming plants growing in contaminated soil. Here, HMs were increased in the blood, milk, and meat of the camel samples collected from the site of the oil industry. Therefore, the consumption of camel milk and meat could be a route of transmitting these HMs to the body of people living near the oil industry site, particularly children who consume a large amount of milk. In this context, Norouzirad et al. have reported an increase in Pb and Cd levels in cow milk collected from areas near petroleum extraction industries in Southwest Iran .Accumulation of Pb, Cd, Ni, and V in the liver and kidney of camels at the contaminated site was associated with functional and histological alterations. Assessment of the liver and kidney function markers revealed a remarkable elevation in serum ALT, AST, creatinine, and urea, demonstrating liver and kidney injury. In addition, dilated central vein, lipid infiltration, and fibrous deposition were observed in the liver, and abnormal glomeruli and interstitial hemorrhage were observed in the kidneys of camels at the contaminated site. The altered liver and kidney function and the tissue injury could be directly connected to the accumulation of HMs in these organs. Pb and Cd are hazardous HM pollutants produced through various human activities, including the oil industry . Pb is aOxidative stress is a major player in mediating the toxic effects of HMs. The current investigation revealed a significant increase in hepatic and renal MDA along with declined GSH, SOD, and CAT in camels at the polluted site, demonstrating an oxidative stress status. In addition, PTGS1 and PTGS2, a part of the MDA synthesis pathway, were upregulated in the liver and kidney of camels at the oil extraction site. The negative impact of Pb on the liver and kidney has been demonstrated to occur via oxidative stress in experimental rodent models ,53,54. Tc and subsequent activation of caspase-3, which elicits apoptotic cells death [Besides oxidative stress, inflammatory response and apoptotic cell death contributed to liver and kidney injury in camels at the petroleum industry site. Here, TNF-α and IL-1β, both mRNA and protein, were remarkably upregulated in the liver and kidney of camels at the polluted site. In addition, Bax and caspase-3 were upregulated, whereas Bcl-2 was downregulated at the oil industry site. These findings show the development of hepatic and renal inflammatory response and apoptosis along with oxidative stress and accumulation of Pb, Cd, Ni, and V. The developed inflammation and apoptosis are a direct consequence of HMs accumulation and increased ROS generation. Excessive ROS can activate NF-κB, a transcription factor that elicits the transcription and release of pro-inflammatory mediators, including TNF-α and IL-1β. ROS and pro-inflammatory cytokines activate Bax and mitochondrial dysfunction, resulting in the release of cytochrome ls death .c and promotes the interaction between Fas and FasL, leading to the formation of the death-inducing signaling complex, which contains FADD and procaspase-8 and -10. Within the cells, caspase-8 and -10 cleave and activate caspase-3, -6, and -7, leading to apoptosis [The role of Pb, Cd, V, and Ni in provoking cell death via apoptosis has been well-acknowledged. For instance, Cd-induced apoptosis is associated with decreased mitochondrial membrane potential and Bcl-2/Bax ratio, and activation of caspase-3, in addition to Fas/FasL-mediated mitochondrial apoptotic pathway in neurons ,66. The poptosis . The lacThis study introduced new information on the hazardous effect of the petroleum industry in the eastern region of Saudi Arabia on dromedary camels. Pb, Cd, Ni, and V were accumulated in the soil and milk, blood, muscle, liver, and kidney of camels at the oil extraction site, demonstrating the negative environmental impact of this industry. Camels exhibited liver and kidney injuries associated with oxidative stress, upregulated inflammatory and apoptosis markers, and depletion of antioxidants. These findings pointed to the hazardous effects of the petroleum industry on nearby communities, and might be of value for evaluating its negative impact upon them.