Fluorescence has long been the best method for detecting bio-molecules at high sensitivity and with the possibility of easy data analysis and is routine for gel electrophoresis and much more. However, the systems for detecting the fluorophores remain expensive and thus inaccessible to many. Most commercial systems are often only optimized for one specific application, making reuse difficult. This paper demonstrates, characterizes and evaluates an inexpensive and versatile system for the detection of fluorophores with two wavelengths using high power LEDs. The LEDs are arranged in two banks angled downward at a tray in a way that allows for bright and uniform illumination while preventing direct reflections into the camera. The emitted light is filtered through an exchangeable filter frame and can be detected using the camera of a smartphone or similar device. By using filters both in front of the LEDs and the camera, very low background and using sufficient exposure times, very high sensitivity can be achieved. The two wavelengths of excitation light and the exchangeable filters allow for optimization for the specific fluorophore used and thus highest brightness. When fluorescence was first used to detect DNA for gel electrophoresis, the most common stain was ethidium bromide. This fluorescent stain was often excited using UV light, mostly by using UV tubes arranged underneath the gel in a so called transilluminator. Due to safety concerns regarding the possible mutagenicity of ethidium bromide, newer stains were developed, these often have different spectra and make excitation possible using visible light It is a common project to build a simple gel documentation system using blue LEDs and colored acrylic as filters For this reason, even for LED illumination, filter cubes that incorporate both excitation as well as emission filters and a dichroic beam splitter have been the standard for fluorescence microscopy. A recent approach to use filter cubes for macro imaging applications has allowed for multi-channel imaging of fluorescent bacteria The ideal excitation wavelength for many of the modern gel stains as well as Acridine Orange is blue light with 470 nm LEDs matching the absorption spectrum well The imager presented here, like many fluorescence imaging systems, can be simplified to two excitation light sources with their excitation filters and a set of emission filters that block the excitation light. The excitation light source are high power LEDs that are mounted in two banks on the broad sides of the imager. LEDs with two wavelengths are used that are arranged in a 2 × 4 checkerboard pattern with excitation filters mounted directly in front of the LEDs in the same pattern. The emission filters are mounted each in a frame that uses a simple snap mechanism to exchange the filters. Unlike other systems, this imager does not need a beam splitter for the excitation light. The two banks of LEDs are angled downwards at the sample at an angle that prevents direct reflections into the camera which could cause uneven background. The sample is placed on a removable tray that is loaded into the device from the front. The base of the device is a cuboid that houses the sample tray with a pyramid on top that has the LED banks placed on the sides with the emission filters. The camera is placed on top. While most of the device is made out of 4 mm polycarbonate, the top piece that hold the filters, the filter frames themselves and the optional platform that holds the camera are 3D-printed from ABS plastic. With the use of this platform, a smartphone can be stably placed on top for long exposures. The inside of the device and the tray are painted black and the partly covered by black tape to increase the opacity and shield from outside light. The electronics consist of simple linear regulators for the LEDs with shunt resistors so that a constant current of 1.4A with two banks of 4 series LEDs in parallel is kept. Switching regulators were avoided due to the higher cost and complexity, it was later also determined that this would only improve the efficiency by about 13% in a worst case scenario. The banks of LEDs with different wavelengths are driven separately due to the need for independent control and the different forward voltages. Due to the large amount of waste heat produced, the LEDs, all linear regulators as well as their shunt resistors are mounted with thermal glue to a 4 mm thick steel plate with a cooler to dissipate the heat. The fans of the coolers are supplied with 12 V using another linear regulator. This is atypical for most DIY imagers as usually less powerful LEDs are used. However, the very bright LEDs minimize the influence of stray light entering the device and reduce exposure times and allow for visibility of most signals with the bare eye. A thermal test proved that the cooler is necessary to keep the LEDs at safe temperatures. The device uses switches to select the wavelength and to switch on the device. The fans can also be switched off using another switch, this should be limited to short periods of time as shown in the thermal test.A big difference between this device and most commercial gel imagers is that it illuminates the sample from the top while most gel imagers illuminate from the bottom through a glass plate. While this option was also evaluated, it comes with some issues. The device would have to be much thicker for an even distribution of the light. It would also result in an easy to scratch plastic sheet or a fragile and expensive glass sheet. The biggest downside though would be that such a setup is exclusively usable with gels, limiting the usability for other purposes. The device presented here can not only image gels but also TLC plates with fluorescent compounds, microtiter plates and generally any object that fits into the tray and has some fluorescent property. The disadvantage is that if gel extractions are to be performed, it can only be done by measuring the position of the band and cutting the gel outside of the imager, unlike a transilluminator that allows for cutting of the gel while visualizing the band to be cut.Compared to many other DIY imagers the biggest difference is the use of very high powered LEDs. Most other designs use blue low power LEDs, which combined with the strongly fluorescent green gel stains available, usually give a strong enough signal and don’t required cooling. However, as this device is also used to image fluorophores with sub-optimal spectra for the LEDs, like imaging GelRed, a higher intensity is advantageous and necessary. When using filters with cutoffs far away from the emission wavelength of the LEDs a very low background can be achieved, but it also results in a very weak signal. During some tests exposure times up to 30 s were required even at this high power, using lower power LEDs would therefore require unpractical exposure times. Another important design decision was the use of excitation filters for the LEDs. Unlike lasers, the light from LEDs isn’t monochromatic so without excitation filters there could be spectral overlap into the spectrum of the emission filter which can lead to a high background signal. This relationships between the excitation filters and emission filters can be seen in In general, many of the design decisions like the use of higher power LEDs, good emission filters or the use of excitation filter increased the cost of the device above that many other solutions. However, in the context of operating a normal laboratory and compared to commercial solution that this device can compete with the cost is neglectable. The exchangeable filters increase the versatility and the strong LEDs allow for high sensitivity with strong signals and thus easy visibility, justifying the cost for most applications.The added weight and power requirements however, drastically limit the usability for field work, however this usually doesn’t pose an issue. The complexity however drastically increases the assembly time and of course cost, however as stated above this decision was made to increase the sensitivity. Due to the placement of the LEDs and the resulting light pattern, this device can not be used for accurate quantification unless the object is very small or compensation is performed. While the filters are easily exchangeable, the LEDs are not. This limits the applicability of the device for other applications that need different excitation wavelengths for example for NIR fluorophores. It would thus be an interesting upgrade to develop a system with exchangeable illumination and possibly use monochromatic light sources to further reduce background and increase sensitivity. This device however balances cost and complexity as well as sensitivity, uniformity and a low background.The main use case is agarose gel visualization. With the very bright illumination even sub-optimal gel stains can be used and even very faint bands can be seen. It can replace expensive gel documentation systems or dangerous UV transilluminators in that regard.Due to the illumination from above, the sample doesn’t have to be transparent. This allows for imaging of TLC plates with fluorescent compounds, charcoal agar plates or any other sample that would usually require a separate device to image.By using two different wavelengths of LEDs a large range of fluorophores can be utilized without needing a second imager. This increases the flexibility and doesn’t limit reuse for future applications.Because of the exchangeable filters, both inexpensive colored plastic as well as high quality glass filters can be used. Not only does this increase flexibility and is required when using two excitation wavelengths, but it also lowers the barrier of entry with upgrades being possible in the future.Due to its versatility the device can be used for a variety of tasks:When assembling and testing the LED arrays, never look directly at the LEDs when operating at high power. With careful control of the voltage, the LEDs can be tested at low power. While most of the very bright light is effectively shielded to the outside, some can exit through gaps in the plastic. Avoid looking at bright objects without a filter installed.The fans can cause injury to fingers, the use of fan guards is recommended for that reason. Avoid loose items of clothing, long hair or loose items like paper towels around the fans. Depending on the heat sink used, it might have sharp edges. While the overall temperature of the steel plates stays below 50 °C under normal conditions, some components, specifically the linear regulators and the large shunt resistors can get dangerously hot.Many of the fluorophores used during these tests and as typical gel stains intercalate DNA and often are suspected mutagens and carcinogens. Always follow standard laboratory practice around dangerous chemicals. Specifically be very careful not to contaminate the camera by changing or taking off gloves after handling contaminated liquids and surfaces.Don’t turn off the fans for extended periods of time as shown in the thermal tests. For the same reason don’t obstruct the fans or the airflow. The power supply can get hot during operation and should not be surrounded by flammable or insulating material.Design files•Electronics.sch: This design file contains the schematics, including comments on cooling and grouping into PCBs.•schematics.pdf: a PDF version of the schematics•GelImager.blend: This design file contains a complete 3D model of the imager and should be used to transfer the dimensions to the polycarbonate sheet.•1InchFilterFrame.stl: An STL file of the filter holder for a one inch diameter filter such as the RG645.•36 × 40FilterFrame.stl: An STL file of the filter holder for a filter with the dimensions 36 × 40 mm, such as the amber plastic filter used.•40 × 40FilterFrame.stl: An STL file of the filter holder for a filter with the dimensions 40 × 40 mm, such as the green and red plastic filter used as well as the OG530.•PhoneHolder.stl: An STL file of the platform that can be used to set down a camera or smartphone.•TopPiece.stl: An STL file of the top piece that the filters and the platform attach toBill of materialsThe OG530 has been bought second hand and no exact equivalent could be found, a close replacement would be a OG530 with a diameter of 25.4 mm, available from Edmund, the only square filter available would be 50 × 50 mm, which would not fit.The orange and red acrylic were not specifically bought for this project and no supplier or part number could be found. If possible it is recommended to characterize the spectrum of any plastic to be tested and to compare it to the spectra of the filter used here, raw spectral data of the filters can be found in the repository.•A jigsaw or scroll saw to cut or the polycarbonate and any plastic filters•Measuring equipment and a fine waterproof marker•Coarse sandpaper•A syringe with a blunt needle and a small amount of dichloromethane or similar solvent to weld the polycarbonate•Alternative adhesives to the ones listed can be used if evaluated carefully•Double-sided adhesive tape that can resist shear forces, thick mounting tape works well for this•Black spray paint•A voltage and current controlled power supply to test the LEDs•Soldering equipment•A crimping tool for the connectors used, alternatively other connectors can be substituted•A drill press and drill bits suitable for stainless steel•Hot glue gun and glue sticks•Wire cuttersFor building the device as described below, the following additional tools and items are required:Using the blender file found in the repository, transfer the measurements to the 4 mm polycarbonate plate. The Blender plugin “MeasureIt” can be used to get precise dimensions The parts to be assembled can be seen in For the pyramid shaped top part (red), hold the pieces together with tape in the right shape. Use two component epoxy glue to secure the parts, dichloromethane can not be used as the odd angles result in only a very small surface area being in contact with each other. Allow for the glue to harden completely. Never touch the uncured epoxy glue with your bare skin as it is known to be a sensitizer, it is recommended to wear gloves.At this point spray paint both the top (red) and lower half (blue) was well as the tray (green). In case the paint isn’t opaque enough, black, non reflective tape can be used in addition. Allow for the paint to dry before proceeding.Using epoxy glue, attach the pyramid (red) to the lower half (blue), making sure the tray (green) can slide in and out freely. Allow for the epoxy glue to harden completely before proceeding, usually waiting overnight is sufficient.From the blender file the positions of the LEDs can be transferred to the metal plate. If using two wavelengths of LEDs, the LEDs should be touching in the middle. Arrange the LEDs in a checkerboard pattern, alternating green and blue LEDs so that when the two panels are mounted, the pattern are mirrored to each other, this pattern can be seen in Glue the LEDs in the right pattern to the metal plate using thermal glue, taking note of the polarity for easier wiring. Each color of LEDs will be wired with 4 LEDs in series. Solder the LEDs into strings of 4 for each color and use the twin wire on the last LEDs, passing the wire through the holes drilled before. The exact wiring can be seen in the schematics available in the repository. Be careful when soldering anything, the hot tip of the soldering iron as well as its shaft can cause serious burns. Leaded solder is toxic, there is no safe dose of lead and it can cause serious chronic health risks, always wash your hands after handling leaded solder, the author recommends to completely avoid leaded solder. Additionally the fumes produced by the flux used should not be breathed in, the use of a fume extractor is recommended.Use leftover polycarbonate to make 8 pieces with a length of 12 cm and a width of roughly 0.5 cm. Also make 12 pieces with dimensions of about 4x2cm. The exact dimensions aren’t important, however, too small of a surface area will result in poor adhesion when later attaching the banks of LEDs. Then cut the emission filters out of the panel of colored plastic. Eight squares of 30 mm of both colors are required in total. Arrange the squares matching the checkerboard pattern of the LEDs. Use epoxy to glue the squares into one large filter, dichloromethane cannot be used. Use dichloromethane to glue stacks of 3 of the 4x2cm plates to each other. When the assembled emission filter has hardened, use dichloromethane to attach two of the long pieces stacked on top of each other to each edge of the filter. Then use epoxy glue to attach the filter assembly to the LED bank. Attach the stacks of 2 × 4 cm plates to the outside of the LED arrays using epoxy glue. This assembly can be seen in For the schematics the “Electronics.sch” file should be opened using a suitable software such as KiCAD. The PCB layouts shown should only be regarded as a suggestion, depending on the capacitors used, the layout might have to be done differently. Excerpts of the schematic and examples of the PCB layout can be seen in Using a pair of wire cutters, cut the perfboard into two halves and trim off the end pieces. Build each of the circuits on their respective PCB, the exact layout doesn’t matter. It is important to mount the voltage regulators on the backside to transfer the heat to the metal plate and to add the shunt resistors using short pieces of wires for the same reason, as seen in e), the shunt resistors can be made into pairs of the1Ω and10Ω resistor by tightly twisting their leads together.Lastly wire the switches as shown in the schematic and attach them to the metal plate with a glue of choice, hot glue was chosen because it is easier to remove in case of mistakes. It is recommended to label the switches accordingly. Now wire the LED arrays with each color in parallel and crimp on the connectors, taking note of the correct polarity. Also wire the fans in parallel and crimp on a connector. Lastly after wiring the DC jack to the main switch, glue it to the back panel of the device, other locations are of course possible.Use thermal glue in combination with hot glue to attach all remaining components to the metal plate, hot glue serves to hold the components while the thermal glue hardens. First attach the heat sink to the center of the plate. Add the shunt resistors as shown in e) and the PCBs to the sides of the heat sink, adding thermal glue to the resistors and linear regulators. Finally organize the wiring, the channels in the heat sink can be used to guide the wires and cable ties and hot glue can be used to hold them in place.The device can now be set up and tested according to the procedure described below.Set down the imager on a level, solid and vibration free surface. Make sure the imager doesn’t wobble, if necessary insert shims under the corners. Vibrations can cause blurriness during long exposures. Avoid direct sun light to reduce background. The front of the device as well as the fans and heat sinks on the front and back need to be unobstructed or overheating could occur. The same applies to the power supply as it could heat up significantly depending on the settings used. Avoid loose objects that could get sucked into the fans. Plug the power supply into the DC jack at the back of the device while the device is switched off.Depending on the camera or mobile device used, install the platform using 4M3 screws. Install the required filters by simply pressing the filter into position, a sharp snapping sound should be heard, the filter should be flush with the platform if installed. To remove the filter, press firmly on the two levers on both sides.Start by pulling out the tray and putting the object to be imaged into it. If a gel should be imaged, gently lower the gel on one side first and push out any air bubbles trapped under the gel. Then install the optimal filter for the fluorophore used, this should be determined empirically but estimates can be made by comparing the absorption spectrum of the fluorophore to the spectrum of the filters or the dyes tested in this paper, e.g. ethidium bromide has the same spectrum as GelRed and thus similar filters should be tested. Then set the LEDs, determining the ideal setting similarly.For very bright fluorophores or when used in high concentration, the fluorescence is often well visible with the naked eye. For low concentrations or when the emission wavelength is in the far red, like when using the RG645 filter, it might not be visible without a camera. For documentation, set the camera down on the platform of a custom mount, for bright fluorescence, handheld imaging might be possible with short exposure times. Align the camera so that the whole object is visible. For bright fluorescence, autofocus can be used. Otherwise, set the focus to a predetermined value manually, this value can easily be found by removing any filter and using the bright light to focus on a feature with sharp edges, like the wells in case of a gel. Determine the ideal exposure time empirically, it is recommended to keep the ISO low to avoid noise, if possible, a manual mode should be used so that ISO and exposure can be set by the user. It is recommended to capture multiple images around the ideal exposure value. Avoid disturbing the camera during long exposures. In case that the fans create too many vibrations, they can be temporarily turned off, keep this to a minimum and depending on the cooler, don’t exceed several minutes to avoid overheating, see the thermal testing section.After use, the sample should be disposed of appropriately and the tray should be cleaned to avoid high background and for safety reasons. Especially with Acridine Orange this step is very important, as the author noticed a strong background in the shape of the gel after it wasn’t properly cleaned off in time. This background was resistant to strong acid and base as well as bleach, so the tray had to be repainted.Thermal testing was carried out using a Fluke 289 multimeter with data logging capabilities with the Fluke 80BK-A thermocouple. The thermocouple was placed at the front side underneath the LED driver PCB in contact with the steel plate and good thermal conductivity was insured using a drop of vacuum pump oil. Recordings were made after letting the device cool down sufficiently until it has equilibrated to room temperature. Recordings were made for a total time of 30 min with data points being captured every second. For further analysis the average temperature of each one second data interval was used, the raw measurements are available in the documentation.The recording was started at the same time the device was switched on. For the measurements with the fans on, the device was left undisturbed with the fans on for the whole 30 min. For the measurements with the fans off, the device was started with the fans off and the temperature mas monitored. As soon as the temperature reached 70 °C, the fans were turned on and the measurement continued for the remaining time.This test concluded that the cooling solution is sufficient as the temperature was kept well below 50 °C while the fans were on. It is also safe to turn off the fans for short periods of time, around 5 min for both LED banks and 25 min for only a single set of LEDs, this time can be seen as the time to reach 70 °C in When using the device in a laboratory shared with other researchers, it is inconvenient at best, impossible at worst to always turn off the lights when using the imager. Room light immunity is thus an important characteristic. It also separates this device from many inexpensive and simpler builds or using a simple transilluminator. The influence of external light depends on the brightness of the illumination and the background that is inevitable due to excitation light leaking into the camera.To test this characteristic two images, taken with the room lights on and off, were compared to each other. The tray was loaded into the device empty as this presents the worst case scenario. Images were then taken using the camera of a Samsung Galaxy S10+ with the wide angle camera at exposure times that showed a clearly visible background. This test was carried out with artificial lighting being the only light source as to avoid drift from changes in daylight intensity. Using the app “Physics Toolbox Suite” on the same device, the light intensity was measured at 85 lx with the lights on and 0 lx with the lights off, this should be regarded as an estimation only. Images were then taken using every filter with all applicable LED settings.The images were then analyzed using ImageJ. The input images were converted to 32bit grayscale and the image with the lights on was divided by the image with the lights off. Using one of the brighter images, the width of the tray was used to calibrate the scale for the images and a 9 × 11 cm ROI, corresponding to the size of the gels used, was saved in the ROI imager. This ROI was then applied to each of the processed images approximately in the center and then the images were cropped and despeckled. The cropped images were exported as a text image. In the text files the spaces were replaced by commas and a whitespace. This allowed them to be imported into a spreadsheet and statistics were carried out and the data was visualized as a boxplot.These values can be interpreted as the added background in percent of the normal background without any stray light entering the device.As can be seen in This represents a worst case scenario with exposure times that are unrealistic during regular use. Apart from direct spots of light on the tray, the device was very resistant to room light. If this presents a problem, black tape can be used to close any gaps at the expense of serviceability. For most test done, this problem was never noticeable as the device was kept in a shelf that shielded it from the direct light. It can be concluded that especially with bright fluorophores and high background from the staining, the device can be used in bright rooms with no noticeable degradation in performance.To test the performance and applicability of the device for its main purpose, two agarose gels were stained with two spectrally very different gel stains. The two gel stains selected were GelRed and Acridine Orange. GelRed is a commonly used gel stain and additionally is spectrally identical to ethidium bromide, which is another common stain. Acridine Orange on it’s own isn’t commonly used for agarose gel electrophoresis, but many commercial gel stains contain it, sometimes without any mention of Acridine Orange Agarose gels with a percentage of 1.6% were prepared in 50 ml of 1× LAB buffer Images were captured using a Samsung Galaxy S10+ smartphone with the main camera for each applicable wavelength and using all filters with five exposures. The best image for each filter and stain was selected for further analysis The gel images were cropped to only show the ladders and a montage was made using ImageJ. Documenting the results using a table, the sensitivity of each stain with each filter was graded manually as the faintest band still distinguishable from the background. As grading is subjective, all original and processed images are available in the repository.Additionally the gels were analyzed using ImageJ to plot the intensity of each lane. For this the images were loaded and an FFT filter with a maximum size of 200 pixels and a minimum size of 3 pixels was applied and each lane left to right selected and then plotted. Using these plots the results from the visual test were confirmed and corrected where errors were found, this allowed for higher sensitivity especially with distracting bright background and broadened peaks but care had to be taken not to include false positives from dust, however it should be noted that this showed a higher sensitivity than would be possible with the naked eye. The plots were made available through the repository for verification.Unsurprisingly the red filters resulted in better sensitivity with GelRed and the filters passing green light worked best with Acridine Orange  and 100 µl for the rest of the plate. In the first column (A1–G1) 20 μl of each diluted fluorophore solution as well as a water control in H1 were added and mixed by pipetting. Then serial dilutions with a volume of 100 μl, corresponding to a dilution of 1:1, were performed along the rows, from X1 to X12, for the whole plate. Then 300 μl of the glycerin solution were added to each well and mixed carefully by pipetting, filling the wells to the top. This was done as the excitation light is at a shallow angle and otherwise could not reach the solutions at the bottom of the wells as also seen in the uniformity test.After waiting for some of the bubbles to disappear, the plates were imaged. A Samsung Galaxy S10+ with the main camera and various exposure time were used. For each filter with each applicable LED setting, 5 exposures were made centered around an empirically determined optimal value. This showed that apart from the Auramine O, which was very hard to detect and due to its excitation spectrum, every other fluorophore was detectable with almost every filter, with the exception of the green plastic filter, which as already seen with the gels, leaks a lot of excitation light and is unusable for almost all applications. The two fluorophores Rhodamine B and Acridine Orange show that matching the filter and LED setting to the spectrum of the fluorophore is crucial for a high sensitivity. Both fluorophores could be detected at sub-nanogram levels, however, only with the correct filter.To calculate the intensity of each signal, the Fiji plugin ReadPlate3.0 was used to measure the intensity of the signal. For this the images were loaded into ImageJ and the rotation was adjusted as necessary, then a rectangle from A1 to H12 was drawn and the plugin was run with the settings found in the raw files in the repository. The data was imported into the spreadsheet and the relative intensity was calculated from the raw values by subtracting the blank on H1 from each of the wells in the first column containing the highest concentration of fluorophore according to this formula, where t is the exposure time, and f the aperture, not that a higher aperture corresponds to a lower signal:Then the results were visualized in a bubble plot with the fluorophores being numbered from 1 to 7 on the X-axis and the sensitivity on a logarithmic Y-axis, the bubble size represents the signal intensity above the background as calculated above. The fluorophore number 1 corresponds to Rhodamine B, 2 corresponds to Acridine Orange, 3 corresponds to Eosin Y, 4 corresponds to Erythrosine B, 5 corresponds to Eosin B, 6 corresponds to Neutral Red and 7 corresponds to Auramine O.This test can be used when evaluating new fluorophores for the device by comparing their absorption spectrum to the ones used in this experiment and helps with selecting the right filter.Thin layer chromatography of the dyes used was performed and the plate was imaged using this device. TLC is a powerful tool to estimate the purity of compounds in case of dyes, they can be detected using fluorescence. It can also be used to compare an unknown sample to a known standard, such as comparing a commercial gel stain to a pure sample of Acridine Orange to identify if a gel stain uses that fluorophore. This is a well known method and was also performed by the author to identify two commercial gel stains as Acridine Orange as noted before To perform the test, a small, about equal amount of each 50 μg/ml dye solution was spotted on a silica TLC plate  without fluorescence indicator. The plate was then developed in a 5:1 DCM/Methanol solvent mixture in a beaker that was covered to allow for the atmosphere to saturate with solvent vapor. Once the solvent front reached near the top of the plate its position was marked and the plate was allowed to dry before imaging.The plat was imaged using a Samsung Galaxy S10 + smartphone using the main camera at various exposure times and using all filters with all applicable LED settings. The complete data can be found in the repository. While the purity of the dyes used wasn’t important to the tests, this example shows that the device can be used for these types of applications as well that would not be possible using a design that illuminates the sample from the bottom. One such application would be a fluorescent western blot. Similar to a TLC plate, a white membrane is used. The signal is usually much weaker, however, as shown before in the sensitivity test, the device is capable of sub-nanogram sensitivity depending on the fluorophore and filter used.The spectra of the GelRed gel stain used as well as the commercial glass filters are available online from the manufacturers The spectra were acquired using a Helios Omega UV–Vis spectrophotometer. The exact parameters are available in the raw files. The filters that were already mounted in their frames were held into the beam path using alligator clips and the unmounted plastic was inserted into the slot of the standard cuvette holder. Spectra of the fluorophores were acquired by diluting them to a final concentration of 10 µg/ml in MilliQ water and measured in a quartz cuvette (Hellma 105.202.008-QS) with a path length of 10 mm. The data was exported as.CSV files and analyzed externally, the raw files are available in the documentation.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Technological advances seen in recent years have introduced the possibility of changing the way hospitalized patients are monitored by abolishing the traditional track-and-trigger systems and implementing continuous monitoring using wearable biosensors. However, this new monitoring paradigm raise demand for novel ways of analyzing the data streams in real time. The aim of this study was to design a stability index using kernel density estimation (KDE) fitted to observations of physiological stability incorporating the patients’ circadian rhythm. Continuous vital sign data was obtained from two observational studies with 491 postoperative patients and 200 patients with acute exacerbation of chronic obstructive pulmonary disease. We defined physiological stability as the last 24 h prior to discharge. We evaluated the model against periods of eight hours prior to events defined either as severe adverse events (SAE) or as a total score in the early warning score (EWS) protocol of ≥ 6,  ≥ 8, or ≥ 10. The results found good discriminative properties between stable physiology and EWS-events (area under the receiver operating characteristics curve (AUROC): 0.772–0.993), but lower for the SAEs (AUROC: 0.594–0.611). The time of early warning for the EWS events were 2.8–5.5 h and 2.5 h for the SAEs. The results showed that for severe deviations in the vital signs, the circadian KDE model can alert multiple hours prior to deviations being noticed by the staff. Furthermore, the model shows good generalizability to another cohort and could be a simple way of continuously assessing patient deterioration in the general ward. It is known that hospital deaths and severe complications in many cases are both predictable and preventable –3. TrackThe technological advances seen in recent years has the potential for changing the monitoring paradigm at the general ward. Whereas continuous monitoring was previously restricted to specialized sections such as the intensive care unit (ICU) and cardiology wards, wearable technologies and increased computational power now allow for monitoring vital signs continuously to be done in the general wards, while patients can still adhere to the enhanced recovery after surgery protocols of early mobilization , 9. The With more continuous clinical vital signs data available, the amount of research of analysis of this has increased in recent years. As larger samples of data become available, the use of big data approaches for analyzing health care data will aid in discovering trends and patterns, that will certainly change the way monitoring in hospitals are done today . In contThe analysis of vital signs data is heterogenous. Some studies used heuristically set thresholds either fixed –16 or adGiven the above, we aimed to investigate detection of physiological deterioration by novelty detection. Kernel Density Estimation (KDE) allows for this by constructing a density of normal observations and evaluating new observations in relation to this. Though the use of KDE on vital signs data is not novel, none of the analyzed studies seemed to incorporate the natural fluctuations in the vital signs given by the circadian rhythm into the models , 24. TheWe hypothesized that incorporation of this in the model would lead to a better discrimination margin between normal and abnormal physiology. We hypothesize that implementing a circadian KDE model based on continuous monitoring of vital signs and computing a stability index using novelty detection can aid in identifying physiological deterioration earlier than the current practice.http://ClinicalTrials.gov  after written approval from the Regional Ethics Committee . All patients had given informed consent prior to inclusion [This study was done as part of the Wireless Assessment of Respiratory and circulatory Distress (WARD) project. The data from two different observational studies with continuous monitoring of vital signs were used in this study. Both studies were registered at nclusion , 19.The nclusion . The secContinuous monitoring used the Isansys Patient Status Engine, an end-to-end platform for wireless monitoring of patients. The system consisted of the sensors: (A) Isansys Lifetouch —a single-lead ECG device that records HR (1/60 Hz), RR (1/60 Hz), from electrocardiogram (B) Nonin WristOx 3150 —a finger mounted pulse oximeter that records SpO2 (1/60 Hz) (C) Meditech BlueBP-05 —a blood pressure cuff measuring systolic and diastolic blood pressure (Sys. BP and Dia. BP) every 30 min during the daytime (07:00 to 22:00) and every hour during the nighttime (22:00 to 07:00). The recorded data were transmitted via Bluetooth to a bed side gateway from which the data were sent via Wi-Fi to a local server database at the hospital.The continuous variables recorded in the study, is not suited for a KDE in the raw format, as there is a high occurrence of smaller periods of missing data and as the devices are prone to artefacts. Therefore, we decided to create observations in a window-based way, such that features were computed over an interval of data points. Each observation would then represent a sample in the KDE model. The flow of computing features is shown in Fig. We selected the four ‘standard’ modalities in vital signs monitoring, Sys. BP, HR, RR, and SpO2. Apart from the systolic BP, the remaining modalities are continuous signals (1/60 Hz) and prone to artefacts from sensors displacement and movement. We have found this to be a common source of errors, and applied a moving median filter with a size of 4 min as proposed by Sow et al. .After filtering, we split the signals into observations in a window-based approach. Three window lengths were used with the length of 30, 60, and 120 min, and the windows were moved 10 min for each observation thus giving an overlap in the observations. Each observation was used as a sample for the model, making it possible to compute a stability index every 10 min independent of the window length. Within each window, the mean value of each of the four signals was computed as the only feature. This resulted in each observation consisting of four features. We labeled the observations to either belong to the physiologically stable class, or one of four event classes. The timestamp of the observations was set to be at the end of the window, such that the time to an event would reflect the moment the observation could be computed in a real-life setting. Observations not belonging to any of the classes, was discarded.In defining the stable physiology, we aimed to select the period of monitoring that would represent the patients which followed the optimal trajectory of recovery at their best physiological state. We defined this as the last 24 h of monitoring for patients for whom the monitoring ended due to discharge from the hospital if and only if they had not had any Severe Adverse Events (SAE) during their stay. All valid observations within this 24-h period would be labeled as stable. This period was chosen as the patients fulfilling these criteria would have a short hospitalization (as monitoring ended after 96 h), and we assumed that the last 24 h of their monitoring would represent the time of their stay where they were under the least amount of surgical stress. The definition of physiological stability is visualized in Fig. We chose to use data which we assumed would represent stable physiology as the normal class, as (1) it is believed to have a homogeneous expression of the vital signs in relation to abnormalities, which differ dependent of the mechanism behind  (2) it is overrepresented within the patients monitored in relation to abnormalities, and (3) it would allow for us to create a metric of stability centered around physiological stability.In order to evaluate the model against physiological deterioration, we defined four unstable events from the patients’ electronic health records, of which three were defined from the manually reported EWS measurements and one was based on the reported SAEs during a patient’s stay. For each event type, the eight hours of data leading up to the event was considered physiologically unstable, and observations within this period were labeled as such. This is shown in Fig. SAEs during the monitoring period were registered retrospectively from the electronic health records as part of the studies , 19. SAEFrom the EWS, three events were created based on the total sum of the scores given by the values of the measured vital signs. The measurements used for the events were obtained in the standard care according to local regulations at the hospitals . We chosThe KDE is a non-parametric method for estimating a probability density function of a random variable using kernel smoothing. The kernel density of a set of random samples is created by applying a kernel function to each random sample and summing these to create the density. Thus, regions with large number of observations will get result in a high density estimate and regions with a sparse number of observations a low estimate. Hence, we can create the KDE from a set of observations representing physiological stability and achieve a density where the evaluation any given point will reflect the likelihood of that point to come from observations of stable physiology.We defined our training set of given by 1\documen matrix. . We defiHence, the stability index was used to represent how likely any given observation was to be physiologically stable where a stability index close to zero indicates an observation of stable physiology and increasing stability index indicates an observation of increasing deviation in the physiology. By comparing the stability index to a threshold, The only free parameter in the gaussian KDE is the bandwidth matrix, n’s rule , Scott’sn’s rule , or optin’s rule . In the e et al. , such thTo incorporate the oscillations of normal vital signs values during the day and night into the models, we proposed a separate model setup based on the time of day, such that multiple KDE models were fitted using only observations from a given time interval during the day. We heuristically chose 12 intervals , as this was believed to provide enough intervals to capture the circadian oscillations, but at the same time ensure that there would be a sufficient number of observations within each. Within each model, observations were standardized to zero mean unit variance based on training observations, and the bandwidth matrix were computed. We chose the separate model setup, as there is no ordinal scale in the time of day. The flow of the circadian configuration is shown in Fig. 2By creating the model in a circadian setup, we aimed to achieve two things (1) that the natural circadian oscillations in vital signs would not lead to the observations appearing abnormal during the extreme periods of the day, e.g. normally low heart rate during the night appearing abnormal in a model fitted with day-time observations, and (2) that the distribution of the stable physiology would be as narrow as possible to avoid deviations being ‘hidden’ in the circadian oscillations, e.g. a high heart rate during the night being ‘hidden’ by a model fitted with day-time observations.The experimental setup was intended to evaluate the different configurations of the model to compare these. We evaluated the circadian KDE with the three different window sizes .In order to estimate the performance of a model configuration, we divided the dataset of the surgical patients into training and test sets, with the training set containing 80% of the observations from the stable class and the test set containing the remaining 20% along with 80% of the observations from each of the event classes. The remaining 20% of the event observations were held out. This analysis was repeated 20 times in a setup shown in Fig. For patients in the AECOPD cohort, we evaluated the model in the same paradigm, with slight modifications. For each repetition, the models were fitted using 80% of the stable observations from the postoperative cohort. The test was done on 80% of both the stable observations and the observations from each event class from the AECOPD cohort. This was likewise repeated 20 times.For each repetition we constructed the receiver operating characteristics (ROC) curve based on the classification results and computed the area under the ROC (AUROC). In addition we computed the Time of Early Warning (TEW), adopted from Colopy et al.  with modThe number of valid observations within each class, i.e. with all features present, is shown in Table For all window sizes, the percentage of complete observations in relation to the total number of observations is substantially higher for the stable class than any of the event classes. For all but two event/window size combinations, less than 1/3 of the observations computed are complete. The high amount of incomplete data is also supported by the two plots in Fig. Figure The classification results of the experiments conducted in each of the window sizes of 30 min, 60 min, and 120 min on the postoperative cohort, and 120 min for the AECOPD cohort are shown in Table The discriminative ability between the stable observations and the SAEs was substantially lower (AUROC: 0.594–0.611), but still with a high average TEW at FPR = 0.1 of 2.5–2.6 h. The results show a small increase in performance with longer window size, which is seen for the metrics in all events apart from the EWS10 event. Therefore, the AECOPD cohort was evaluated with a window size of 120 min. From the results shown in Table The boxplots in Fig. Figure We illustrated the application of the stability index model on a timeline of four patients’ vital signs from the postoperative and AECOPD cohort in Fig. In this study, we found that a simple KDE model using classic vital signs parameters could distinguish between observations originating from stable physiology and observations in the period prior to an event, though with different discriminative properties. The results further showed that the computed stability index allows for the identification of events hours prior to the event being noted by medical staff.From the experiments conducted, the SAEs were the events that were the most difficult to distinguish from the stable observations. Compared to the EWS events (AUROC: 0.772–0.993), the SAEs yielded both lower AUROC (AUROC: 0.594–0.611) and TEW (TEW at FPR = 0.1: 2.5–2.6 h). Although the SAEs by definition represent a physiological deterioration, it is difficult to assume that the presence of SAEs will have any substantial effect on the vital signs given that they encompass both respiratory, circulatory, infectious or various other categories. Whereas other studies have previously considered events as clinical emergencies , 16, ICUComparing the different window lengths, the results show a slight increase in AUROC with increasing window length (except for EWS10 events). This increase might be caused by multiple factors. Firstly, as the features are based on the mean value within the window, shorter periods of deviating vital signs, which expectedly can occur from physical activity during e.g. hygienic procedures, will have a larger impact on the features computed with a window length of 30 min than 60 and 120 min. This might lead to observations with a longer window length producing less false positives. Secondly the longer window lengths results in substantially more valid observations, which provides a larger basis for giving a classification. If the latter is true, the increase in performance with longer window lengths, might just be caused by the increased sample size and thus not indicate increased discriminative properties.In the observations computed, BlueBP-05 and WristOx2 had most incomplete observations while the Lifetouch showed good up-time comparable with similar devices . The resThe ability to distinguish normal physiology from vital sign derangements leading up to an adverse clinical event could prove vital in the field of in-hospital vital sign monitoring. Late recognition of patient deterioration is associated with worse outcomes and with the current practice of manual measurements, it is proven that clinical deterioration can go unnoticed , 35. TheThe largest limitation of the study is the many incomplete observations due to missing measurements from one or more of the devices. The proposed model was constructed in such a way that observations needed to be complete in order to compute a stability index. This led to the majority of the events not being preceded by any valid observations. In other studies, the procedure has been based on single parameters, thus limiting the issues of single devices , 17. HowIn addition, the current study did not investigate an optimal threshold for classification or its implications. Any set threshold will lead to events going undetected and false alerts being produced. Therefore, the selection of a threshold criterion encompasses a larger analysis in which post-processing and other mitigations might be necessary, which is out of scope of this study.With constantly increasing rate of which health care data are generated and the variety of this, health care is now transitioning into the big data era . ContinuThis study found that it is possible to alert medical staff 5.5 h before a severe deviation in vital signs (EWS10 events) with an AUROC of 0.993, thus demonstrating the potential beneficial implications for patient safety and quality of treatment if proven implementable in a clinical setting. For the SAE events, the predictive capability was lower (AUROC of 0.611), with a potential TEW of 2.5 h. The stability index proposed demonstrates an easy-implementable way of continuously assessing the physiological stability of hospitalized patients, which allows the earlier detection of deterioration. In addition, the model showed increasing performance with increasing severity of the EWS events. The circadian KDE model showed fairly good generalization to a different patient cohort and, once the limitations have been addressed, the model could prove a valuable tool for medical staff.


SCRAPT  is able to produce operational taxonomic units that are less fragmented than popular tools: UCLUST, CD-HIT and DNACLUST. The algorithm is implemented in the open-source package SCRAPT. The source code used to generate the results presented in this paper is available at https://github.com/hsmurali/SCRAPT.16S rRNA gene sequence clustering is an important tool in characterizing the diversity of microbial communities. As 16S rRNA gene data sets are growing in size, existing sequence clustering algorithms increasingly become an analytical bottleneck. Part of this bottleneck is due to the substantial computational cost expended on small clusters and singleton sequences. We propose an iterative sampling-based 16S rRNA gene sequence clustering approach that targets the largest clusters in the data set, allowing users to stop the clustering process when sufficient clusters are available for the specific analysis being targeted. We describe a probabilistic analysis of the iterative clustering process that supports the intuition that the clustering process identifies the larger clusters in the data set first. Using real data sets of 16S rRNA gene sequences, we show that the iterative algorithm, coupled with an adaptive sampling process and a mode-shifting strategy for identifying cluster representatives, substantially speeds up the clustering process while being effective at capturing the large clusters in the data set. The experiments also show that  Sequence clustering is commonly used for a wide spectrum of biological analysis applications ,2 and pawcdEST . We describe a probabilistic analysis of the performance of the iterative clustering process, and show that the implementation of this process in SCRAPT is able to discover the large clusters in a sample in a fraction of the time required by traditional clustering techniques. The iterative approach we describe here enables researchers to trade off run time for the size of clusters that are reliably identified, thus allowing the tuning of the clustering process to the specific analytical needs of individual projects.Note that doing so is not a trivial matter as the size of clusters is not known prior to completing the clustering process. We describe here an iterative clustering framework that relies on adaptive sampling to bias the clustering process toward the most abundant clusters in the data set. This approach is implemented in the software package CD-HIT  are considered for further analysis. The likelihood that two sequences from a taxonomic unit occur within a given sample, allowing the algorithm to ‘detect’ this taxonomic unit, depends on the abundance of the taxonomic unit in the entire data set, with larger clusters being more likely to be identified by any given subsample. As a result, the sampling process biases the clustering toward identifying the most abundant clusters in the sample. Note that sequences that do not cluster within a subsample are not necessarily singletons in the entire data set, and therefore they cannot be eliminated from further consideration and are returned into the pool of unclustered sequences to be processed in subsequent iterations.We begin by describing and analyzing a naive version of the iterative algorithm. In each iteration, we sample uniformly a small portion (e.g. 0.1%) of the unclustered sequences. The sampled sequences are then clustered with DNACLUST, at a minimum sequence similarity threshold as specified by the user. The sequence similarity defined in SCRAPT is the same as the sequence similarity defined by DNACLUST, as we use DNACLUST for clustering and ‘baiting’. Sequence similarity in DNACLUST is given by DNACLUST with its default mode where gaps are allowed at the end of a sequence but not at the beginning. During this baiting process, we also adjust the cluster representative using a mode-shifting procedure that will be described in more detail below.The representative sequences for the clusters formed from the subsample are used as ‘baits’ to recruit other sequences from the full data set to the corresponding clusters. We perform this step using the ‘recruitment’ mode of At the end of each iteration, we report the clusters found and remove the clustered sequences from the set of unclustered sequences. The algorithm proceeds either for a user-defined number of iterations or until the number of clusters with a given size identified in each iteration converges. We refer to this process as naive because the sampling rate is held constant from one iteration to another.N; the number of sequences sampled in an iteration—n; the number of sequences in cluster ck—ρk. The sampling rate α is given by ρk is identified in a round if there are at least two sequences from the cluster ck in that sample. Thus, the probability that the cluster ck is recovered in an iteration that sampled n sequences is given byHere, we provide a probabilistic analysis of the naive sampling-based algorithm described in the prior section. The following notations apply: the total number of unclustered sequences at the start of any iteration—Lemma 1: Given N sequences and a sample obtained by uniformly sampling n sequences, the bound on the probability of missing a cluster ck of size ρk is given byρk relative to the size of the database for different sampling rates.Figure m clusters Next, we extend Lemma 1 to calculate the probability that the naive sampling strategy would miss Lemma 2: Given N sequences in the database with n sequences in the sample, the probability m clusters of size m clusters for different cluster sizes. We describe the proofs of the lemmas in Sections 1.1 and 1.2 of Supplementary Data.In Figure α, the cluster density ρ, the number of sequences in the database and the probability of missing 1 and m clusters of size at least ρ, respectively. However, one thing that the lemmas do not directly convey is how the distribution of clusters changes in each iteration, a factor that determines whether enough representatives from the clusters of interest are sampled in each round. Since the distribution of cluster sizes is not known in advance, we exemplify the behavior of the algorithm using a set of simulations. In order to mimic a realistic data set, we assumed that 20% of the data set comprises singleton clusters and assumed a probability distribution for the remaining non-singleton cluster sizes. In each iteration, we sample 0.1% of the sequences from the set of unclustered sequences uniformly at random and identify clusters based on flipping a biased coin with the probability calculation shown in Lemma 1 for each cluster remaining in the database. In Figure The two lemmas described above establish the relationship between the sampling rate α. To counteract this effect, we describe an adaptive process that increases the value of α as the ‘productivity’ of each iteration decreases. The naive sampling approach serves as the lower bound for the performance of SCRAPT as it assumes a constant sampling rate and does not make any assumptions on the choices of centroids.The two lemmas described above as well as Figure A more useful measure is an estimate of the number of clusters of interest that is pending to be clustered at the end of each iteration. We show that the problem of determining this probability is computationally hard even if the sizes of all clusters are known in advance. We describe a detailed analysis in Section 1.3 in Supplementary Data (Theorem 1). In this manuscript, we describe a statistical procedure to estimate an upper bound on the size of the unobserved clusters in each iteration.In the next section, we provide two strategies that improve the naive sampling baseline in terms of efficiency and the quality of the clusters produced.n sequences drawn from unclustered sequences available at the beginning of the iteration. Since the number of singletons in the data set c is constant (a true singleton will not be clustered in any iteration), we can write |unclustered| = |non-singletons| + c and observe that |non-singletons| decreases from iteration to iteration as clusters are identified. As a result, the ‘density’ of singletons in the data set, To formalize the adaptive sampling, we assume a sample of DNACLUST and CD-HIT use the longest sequence in each cluster as the cluster representative, such sequence is not necessarily the best representative of the set of sequences in the cluster or necessarily a good approximation of the biological sequence from which the co-clustered sequences were derived (i.e. the sequence the clustering process is meant to approximate). Instead, it has been previously proposed that a better representative is the sequence that has the highest multiplicity   , as it lm clusters  × r realizations, where ε is a parameter, then we mark all clusters of a given size k as having been detected. By default, we set r = 1000 and ε = 0.05.To estimate a bound on the expected number of clusters that are identified in any given round, we need to estimate two probabilities: that of missing any given SCRAPT uses DNACLUST to cluster a subsample of the data and then uses the cluster representatives as ‘bait’ to retrieve, from the full data set, the other sequences belonging to these clusters. For each cluster obtained after baiting the full data set, we shift the representative sequence to the sequence that has the highest multiplicity in the cluster. The baiting process is repeated after shifting the representative sequence in order to construct the final clusters, potentially recruiting new sequences, as well as returning previously clustered sequences to the unclustered pool. After all clusters shift the representative sequences, SCRAPT repeats the baiting process with the newly selected cluster representatives, to account for any sequences that may need to change their cluster membership. We describe a detailed algorithm to perform mode shifting in Algorithm S1 in Supplementary Data.SCRAPT algorithm, we rely on two 16S rRNA gene sequence data sets of varying sizes. The first data set contains 16S rRNA gene sequencing reads of gut microbiome of mice affected with lupus (bioprojects #PRJNA529260 and #PRJNA638971) , sequenced as a part of the Tara Oceans project  . This daA638971)  sequenceA638971) . This da project . This daDADA2, take as input the percentage of similarity that needs to be preserved between the representative sequence to any sequence within the cluster, a clustering algorithm that produces fewer clusters for a given similarity threshold is preferred over others, and is expected to be more accurate. While just the number of clusters might not sufficiently depict the extent of fragmentation, we propose a precise metric that reflects the distribution of cluster sizes.We compare these different approaches based on the number of sequences clustered and the distribution of the recovered cluster sizes. Since all these methods, except x, we measure the total number of sequences contained in clusters of size x or greater. This mirrors approaches used to evaluate the fragmentation of genome assemblies  on the soil microbiome data set. We show a similar figure for the lupus microbiome data set in Supplementary Figure S2. As seen from Figure α. A low value of α is desirable as the run time of the clustering algorithm critically depends on the size of the subsample selected in each iteration. Mode shifting . We also provide a comparison of the mode-shifting process used in SCRAPT with the longest-sequence-first approach used by CD-HIT and DNACLUST in In Figure g Figure  and the g Figure  each parDNALCUST in stand-alone mode, in dashed lines, to provide a frame of reference. All iterative approaches, irrespective of the value of α, complete the clustering faster than the original DNACLUST—a side effect of the mode-shifting approach, demonstrating that the choice of the longest sequence as cluster representative as followed by DNACLUST is suboptimal. We also restricted the analysis to clusters of size at least 10 and we see that the iterative approach for different values of starting α quickly converges, indicating SCRAPT is able to identify all clusters of size at least 10 early on, independent of the choice of α. In Figure x-axis in Figure To better exemplify the clustering process, we show the relationship between run time and the cumulative sequences clustered in the soil microbiome data set in Figure DNACLUST, CD-HIT and UCLUST. From the figures we see that the extent of fragmentation is very low for SCRAPT and the iterative algorithm that uses a fixed sampling rate (the naive method). This is an artifact of the mode-shifting procedure and confirms our intuition that the mode is closer to the ‘true centroid’ of the cluster. However, as described previously, the naive sampling tends to converge sooner and might fail to identify all large clusters for stringent similarity thresholds as it tends to spend a lot of time detecting singletons in the later iterations. Similarly, we see that the variations of the iterative algorithm that do not perform mode shifting tend to produce low-quality clusters. With respect to other stand-alone clustering tools, we see that none of the existing methods outperform SCRAPT and the clusters produced by DNACLUST, CD-HIT and UCLUST are more fragmented. We do not show the results of running UCLUST on the soil data set because the open-source version of UCLUST failed to produce outputs on these data. We also study the performance of SCRAPT against MeShClust 3.0  and that can be attributed to DNACLUST’s memory frugality.Finally, we describe the run time benchmarks for SCRAPT is to DADA2’s output, which is currently the widely accepted standard for 16S rRNA gene sequence analysis. DADA2 takes the entire data set (without deduplication) as an input and outputs the ‘top’ denoised sample sequences along with the number of sequences within the ASV, which is used for downstream analysis tasks. To compare the quality of the clusters produced by SCRAPT, we took the centroids obtained from the clusters of SCRAPT and aligned them against the denoised sequences of DADA2. For each denoised sequence identified by DADA2, we found the largest SCRAPT centroid that had at least 99% similarity with the former. We then computed the number of sequences within the corresponding SCRAPT cluster for each ASV associated with DADA2. In Figure DADA2 and the number of sequences in the largest cluster of SCRAPT corresponding to each DADA2 cluster for the data sets considered in this study. It is evident from the plot that the sizes of the clusters discovered by both the methods are highly correlated . Further, we note that a few clusters are identified only by SCRAPT and DADA2, respectively. On the lupus microbiome data set, 5.5% and 2.24% sequences are clustered only by SCRAPT and DADA2, respectively. Similarly, on the soil microbiome data set, 8.56% and 0.05% of the sequences are clustered only by SCRAPT and DADA2, respectively. Finally, on the Tara Oceans data set, only 0.64% and 1.55% of the sequences are clustered exclusively by SCRAPT and DADA2, respectively. This indicates that the two methods agree on the choice of centroids to a large extent. We also see that SCRAPT is able to produce larger clusters with more sequences in them than DADA2. We have also computed the fragmentation measure between DADA2 and SCRAPT and have included the fragmentation curves  in Section 3.6 of Supplementary Data. Although both SCRAPT and DADA2 produce similar clusters, in our experiment for the same data set we see that the SCRAPT runs faster than DADA2. The run times for the lupus microbiome and soil data sets are shown in Figure SCRAPT took 171 min, which is 27× faster than DADA2, which took 4677 min. We also note that we ran DADA2 in its default sample-by-sample mode, while we ran SCRAPT by pooling all sequences from all samples, since DADA2 would not complete in a reasonable amount of time when applied to pooled data for the soil and Tara Oceans data sets.We assess how similar the output of SCRAPT, which we evaluate in the context of 16S rRNA gene sequence analyses—a widely used approach for assessing the structure of microbial communities. Our results show that iterative clustering achieves a substantial improvement in performance over one-shot clustering approaches such as DNACLUST even without the enhancements described in our paper. We also provide a theoretical analysis of the iterative clustering process that can be used to provide guaranteed bounds on the size of clusters that can be identified in each clustering round.In this manuscript, we have described an iterative approach for sequence clustering, implemented in the software Results also show diminishing returns as clustering proceeds, with later iterations of the clustering process identifying only relatively small clusters. This provides the opportunity for users to trade off analysis time for the completeness of the clustering, because small clusters are often due to errors or contain insufficient information to allow meaningful subsequent statistical analyses.SCRAPT is not sensitive to the choice of initial sampling rate α, as shown in Figure x-axis for the soil data set, suggesting that the initial sampling rate α could have an effect on the size of the largest clusters discovered. Such an observation can be explained by the relative noise content  in the data set that causes the algorithm to split a large cluster. However, as indicated in Figure SCRAPT still outperforms the existing methods by a large margin, and the difference caused by different initial sampling rates α in SCRAPT is negligible in this context.In general, the quality of clusters produced by We also observe from our experiments that reassigning cluster representatives to sequences with highest multiplicity yields a significant improvement in the size of clusters that are recovered, while preserving the similarity within the clusters. It is important to highlight that while other studies have shown the advantage of using sequence multiplicity to guide the clustering, many clustering approaches continue to rely on a ‘longest-sequence-first’ paradigm. In our case, the mode-shifting procedure operates in just one round (sequences are ‘baited’ once after the reassignment of the cluster representative); however, we have noticed that some clusters could be further improved by multiple rounds of mode shifting. An exploration of the context within which it would be beneficial to repeatedly ‘recenter’ clusters is left for future research.CD-HIT is comparable to SCRAPT for this data set, while in the soil microbiome data set , thus leading to inconsistent performance from sample to sample. The mode-shifting approach used by SCRAPT addresses this confounding factor, leading to a more consistent behavior across samples.The fragmentation measure for the lupus microbiome data set Figure  indicatet Figure  CD-HIT lCD-HIT, UCLUST, DADA2 and DNACLUST, rely on sequence alignment. Since our focus is on 16S rRNA gene data sets, we illustrate and compare the performance of SCRAPT with tools that use alignments. An important factor that enables the iterative approach we have described here is the ‘baiting’ procedure used by DNACLUST wherein the clustering algorithm identifies a potential cluster representative from subsample and then searches the rest of the data set for other sequences that lie within the similarity radius of the representative. That said, the iterative clustering scheme presented in this paper can be easily extended to any of the existing clustering tools, including BLAST and alignment-free tools as long as baiting and centroid-shifting functions can be defined.The state-of-the-art tools for 16S rRNA gene sequence analysis, including DADA2 claim that DADA2 is able to better capture true biological signals than OTU-based approaches. Our comparison between SCRAPT and DADA2 reveals high concordance between the output of the two tools (Figure DADA2 and SCRAPT. This further shows that DADA2 and SCRAPT produce a highly similar set of clusters and the improved efficiency of SCRAPT does not trade off the quality of clusters produced by SCRAPT. It further confirms that the mode of clusters, chosen by SCRAPT as the reassigned centroid, is a biologically relevant estimator of the cluster centroid while incurring a low computational cost.The authors of s Figure , demonstSCRAPT comes from its ability to prioritize the discovery of larger clusters in the first few rounds, making the subsequent iterations cheap. Further improvements in speed can be obtained through parallelization. Specifically, within each iteration, after ‘seeding’ the clusters, the bait, mode-shift, rebait process can be run independently and in parallel for each cluster. In order to remain consistent with how other clustering tools operate, we have not leveraged this opportunity for the results presented in this manuscript.The efficiency of SCRAPT also relies on a fixed sequence similarity threshold, our tool also suffers from this limitation. However, we believe that the mode-shifting process at least partly alleviates the impact of the potentially more fragmented clustering results. Mode shifting focuses on the ‘denser’ regions of the sequence space, meaning that fewer sequences fall out of range, and also that more of the sequences are assigned to the proper clusters. As a result, the potentially spurious clusters caused by the strict identity threshold encompass fewer sequences and are overall smaller in size. Thus, the fragmentation is unlikely to impact most downstream analyses of the data, beyond the obvious impact on richness measures. The strong concordance between cluster abundances observed when comparing SCRAPT with DADA2 supports this conjecture. We are, however, interested in further refining the clusters produced by SCRAPT. Co-abundance information, for example, can be used to expand the clustering threshold in a data-driven manner, as done in LULU (SCRAPT and other fixed-threshold tools can also be underfragmented. Approaches such as the divisive clustering used in DADA2 can be used to break apart clusters in a data-dependent way. Such future research must, however, be performed in the context of relevant biological applications to ensure that the clustering results enhance rather than confound analyses.It is well appreciated that using a fixed sequence similarity threshold in OTU clustering can cause inflation in community richness . Since t in LULU . We alsoIn this paper, we study the benefits of adopting an iterative clustering framework in the context of clustering large 16S rRNA gene data sets. However, the background and the theory we have described in the previous sections do not make any assumptions on the nature of the data or the process that generated the data. With an appropriate distance function, a centroid-shifting function and a baiting function, the iterative approach can be extended to other kinds of clustering problems in general. Conversely, assumptions about the nature of the data or the process that generated them can allow us to speed up the clustering process by biasing the sampling toward sequences belonging to large clusters. This serves as the focus of future research.SCRAPT approach. We also note that this manuscript has primarily provided a comparison of SCRAPT to the non-iterative application of DNACLUST, CD-HIT and UCLUST. We plan to conduct a more thorough comparison of SCRAPT to other widely used clustering tools, including alignment-free methods. Currently, SCRAPT relies on uniform sampling of the data. We plan to explore in the future biased sampling procedures that increase the likelihood that large clusters found in the subsamples are selected in each iteration. Such biased sampling approaches may be particularly effective in the later stages of the clustering process where, even after adaptively changing the sampling rate, the clustering process stalls. Studies (SCRAPT.As discussed, there are opportunities for extending the iterative clustering framework we have described to other clustering algorithms and sequence search approaches. We intend to evaluate how the choice of such algorithms impacts the effectiveness and efficiency of the  Studies  in the p Studies  also shohttps://github.com/hsmurali/SCRAPT and https://doi.org/10.5281/zenodo.7659103.The algorithm is implemented in the open-source package SCRAPT. The source code used to generate the results presented in this paper is available at gkad158_Supplemental_FileClick here for additional data file.


Instead of Gang XiongIn addition, there was an error in Supplementary Data Sheet 1.zip (Table 3). The Supplementary material contains sensitive information about the patient that has not been deleted, and some of the descriptions in the table have not been converted to English, which will affect the reader’s reading experience. The correct supplementary material statement appears below.Table 3 Patient baseline information and MRD statusThe authors apologize for these errors and state that these do not change the scientific conclusions of the article in any way. The original article has been updated.


D (pile diameter), the stress and deformation of piles are significantly affected.Caves affected the load transfer mechanism of bridge pile foundation, and then the safety of the bridge was threatened. This study was to investigate the effect of karst cave under bridge pile foundations on the vertical bearing characteristics of bridge pile foundations by static load test, finite element analysis and mechanical model. The settlement of the pile was measured by displacement meter, and the axial force were measured by stress gauges in the test. The load-settlement, the axial force, the unit skin friction and the ratios of side and tip resistances were compared with the result of the simulation. Then sixteen conditions were selected in finite element analysis, one of them was a conventional pile not on cave. The others were about five kinds of height, five kinds of span and six kinds roof’s thickness of the cave. The simply supported and fixed wide beam were established to calculate the allowance roof thickness. The results reveal that when the cave span is greater than 9 m × 9 m or the roof thickness is less than 2  The cave under pile causes the lack of bearing layer at the pile bottom, which poses a threat to piles5.Karst is a complex substrate, and with the stable supports required for bridge structures, the performance of bridge pile foundations in karst is vitally important. Three problem scenarios can occur when installing bridge pile foundations in terrain with karst caves: cave across pile, cave at side of pile, or cave under pile. The bearing characteristics of piles are significantly different with the different size and roof of the underlying cave9. Wong and Dong implemented the load-settlement law and calculation method of bearing capacity and safe thickness in karst areas using a static load test and theoretical deduction11. Chen and Hu studied the vertical destroy mode of partially-screwed pile and calculated its bearing capacity by test and simulation13. The influence of the thickness and size of the cave roof on the bearing capacity and the sensitivity of each factor were found by Zhang15. Liu and Fattah investigated the vertical performance of the pile with the changing size, roof thickness of and shape of the karst cave and analyzed the pile’s stability17.The bearing performance of bridge pile foundations in karst area has attracted much attention of scholars. Feng, Chen and He successfully predicted the bearing strength and stability of piles that constructed on karst cave based on the high accuracy grey model19. Jiang, Zhao, Zhang, Wang and Bai established the simply supported mechanical model to get the theoretical calculation formula of the safe thickness of cave roof and studied the destroy mode of the roof, when the roof is subjected to punching, shear and bending and tensile failure24. According to Wilson, Tschuchnigg and Rodrigo, the stability of a structure could be determined by limit equilibrium analysis in engineering27. Lee proposed the stability of the void under different undrained condition and discussed the influence of inclined load on the void29. Almost all of above researches are studied by the finite element analysis and mechanical model. Static load test is one of the most reliable methods to study the performances of piles34.Most scholars studied the failure mode and the stability of karst cave’s roof. Zhao and Xiao proposed the stabilities of the one void and multiple voids under different undrained condition and discussed the influence of inclined load on the voidIn this study, the effects of the height, span and roof thickness of the underlying karst cave on the vertical bearing characteristics of bridge pile foundations were investigated by the static load test and finite element analysis. The vertical bearing capacity were obtained under different caves. Meanwhile, the load transfer characteristics were studied under different scenarios including the axial force, unit shaft resistance and ratios of two resistances.36. It enables more efficient transfer of upper loads to the test piles. Both the displacement meter, the strain gauge and earth pressure cells were used in this test, so that the mechanics and deformation data of the pile foundation obtained by the test were relatively comprehensive. The static load test mainly relied on a 470 m long flyover bridge in Pingdingshan. The superstructure of the bridge adopted cast-in-place continuous box girder, the lower part adopted column pier, and the foundation was the bored pile. The bridge was located in a flat with a ground elevation of about 89.30 m. The upper layer of the bridge site was quaternary and the lower layer was Cambrian dolomitic limestone. The characteristics of geological conditions are shown in Table The design of the field test is completely dependent on the prototype pile, so the data of the field test are the most reliable and representative. In order to reflect the bearing characteristics of the pile foundation under the influence of the underlying cave truly and verify the accuracy of the finite element simulation model, the field static load test on the bearing characteristics of the pile foundation were carried out. The static load tests in this manuscript innovatively used of the internal benchmark beamEs is the Young's modulus; t is the thickness; μ is the poisson ratio; c is the cohesion; φ is the internal friction angle; γ is the unit weight; frk is the uniaxial ultimate compressive strength of the rock; Rt is the ultimate tensile strength of the rock.The quaternary soil covered all the rock strata and concealed karst was developed at the location of SZ5 of pier 3#. The geological conditions were determined by drilling sampling method. As Fig. l is the length of SZ5; D is the diagram of SZ5; Qu is the ultimate load of SZ5 in design; hr is the rock-socketed depth of the pile; Hc is the roof thickness of the cave; H is the height of the cave; B is the span of transverse of the cave; L is the longitudinal length span of the cave.SZ5 is a model bridge pile foundation with 19.5 m in length and 1.5 m in diameter. It is a cast-in-place pile with the ultimate load of 8120 kN in design. SZ5 is built on a karst cave with a height of 3.2 m and a span of 6.0 m × 3.0 m, and the bottom of the pile is 1.5 m to the top of the cave. The heights of the caves are measured in the axial direction of the pile. The spans of the caves are measured in two directions perpendicular to the axial direction of the pile. The roof is the rock from the pile bottom to the cave. The parameters of SZ5 is shown in Table l is length of SZ5; D is diameter of SZ5; Qu is ultimate vertical bearing capacity of SZ5 in design; Mu is the bending moment of SZ5 in design; hr is the depth of the embedded rock of SZ5; h is the thickness of the roof; hc is karst cave’s height; B is longitudinal span of cave; L is the horizontal span of cave.Loading hydraulic system consists of 4 jacks Fig. a6 with pFour displacement meters were installed on SZ5, and four on the outer beam, as shown in Figs. Stress meters were arranged along the two symmetrical main reinforcements of SZ5 for the accuracy of the data. The stress meters were welded to the steel bar as shown in Fig. The test was carried out by slow maintenance loading method. The vertical load included 11 levels. Each level increased by 2 MN. The settlement of SZ5 was recorded at 5 min, 15 min, 30 min, 45 min and 60 min in the first hour, and then recorded once every 30 min. When the settlement was less than 0.1 mm at least twice within an hour, it could be regarded as stable. Then the load was removed at 2MN per level. After removing each level load, the residual settlement was required at the 15 min, 30 min, 60 min. After the load was decreased to zero, the residual settlement was read every 15 min in the first half an hour, and then once every 30 min, which took 3 h altogether.The pile was made of concrete as a solid model, which adopt the ideal elastic constitutive relation. The elastoplastic constitutive model and Mohr–Coulomb yield criterion were selected for the rock and soil. The soil and rock were established as 2D grid by ABAQUS, and extended to 3D entity unit in order to keep the contact surface continuous. Then each entity was added with a corresponding material attribute Fig. , 2, 3. TThe parameters that are manipulated included the height, the span, and the roof thickness of karst caves that interact with piles. There are seventeen total treatments, including one with piles without karst caves (control), five different heights , five different spans , and six different thickness of caves . The pile is 20 m in length and 1.5 m in diagram. It is embedded in rock 2.0 m. When the span of cave is manipulated, the height and roof thickness of cave is 3 m and 2.25 m, respectively. When the height is changed, the span and roof thickness of cave is 6 m × 6 m and 2.25 m, respectively. The heights of the caves are measured in the axial direction of the pile. The spans of the caves are measured in two directions perpendicular to the axial direction of the pile.37 The load needed to produce this settlement is the bearing capacity of piles when the settlement does not mutate. When the settlement is more than two times of the adjacent settlement, the pile foundation has been damaged. The adjacent load is the ultimate bearing capacity of the pile.There are two methods requiring the ultimate vertical bearing force of piles from load-settlement curve according to the standard. One of which is the force corresponding to the sudden change settlement. The other method is to calculate the value of 3% of the pile diameter and the value of 40 mm, then elect the smaller one to regard as the failure settlementQ-s curves in static load test can be seen in Fig. The As shown in Fig. 38.Qi is the axial force of pile; FG, EH and AH is axial pressure, the elastic modulus and the cross-sectional area of a main reinforcement, respectively (EH is 2.8 × 104 MPa and the diameter of the main bar is 28 mm). EG and AG is the elastic modulus and the cross-sectional area of concrete, respectively (EG is 2.0 × 105 MPa); n is the reinforcement number (n is 28). Ti is the unit side friction , it is the force on the unit side area of the pile element; U is the circumference of the pile; li is the distance between adjacent stress meter , SZ5 shows typical characteristics of friction pile. The finite element analysis results are in good agreement with the static load test results.The settlement of the SZ5 in the static load test sudden increases under the load of 22 MN. It indicates that the cave roof is destroyed. Ensuring the allowable roof thickness is significant to the bridge pile foundation. The underlying cave leads to the decrease of the bearing capacity of the bridge pile foundation. It is of great significance to ensure the safety of bridge foundation to consider the size of cave and the strength of roof of cave in the design.The increasing cave height has little effect on the bridge pile foundation. The vertical bearing characteristics of the piles are greatly affected by the increasing cave span and roof thickness. When the span is greater than 9 m × 9 m or the roof is thinner than 3.0 m, the bearing capacity’s decrease of the bridge pile foundation must be considered cautiously.The axial force of the pile decreases more faster with the increasing cave span and decreasing roof thickness. It is because that more forces are transferred to surrounding soils by rubbing. At this time, the strength of cave roof is very important to ensure the stability of pile foundation.The tip resistance ratio of the pile on a cave decreases firstly, then keep almost steady with the increasing span. The side resistance ratio keeps steady, when the span exceeds 9 m × 9 m. The tip resistance ratio of the pile on a cave gradually increases with the increasing roof thickness.In this study, the effects of the underlying cave’s height, span, and roof thickness on the bearing characteristics of piles were investigated by load tests and finite element modelling. The conclusions of this study are as follows: