The Sahara region has experienced periodic wet periods over the Quaternary and beyond. These North African Humid Periods (NAHPs) are astronomically paced by precession which controls the intensity of the African monsoon system. However, most climate models cannot reconcile the magnitude of these events and so the driving mechanisms remain poorly constrained. Here, we utilise a recently developed version of the HadCM3B coupled climate model that simulates 20 NAHPs over the past 800 kyr which have good agreement with NAHPs identified in proxy data. Our results show that precession determines NAHP pacing, but we identify that their amplitude is strongly linked to eccentricity via its control over ice sheet extent. During glacial periods, enhanced ice-albedo driven cooling suppresses NAHP amplitude at precession minima, when humid conditions would otherwise be expected. This highlights the importance of both precession and eccentricity, and the role of high latitude processes in determining the timing and amplitude of the NAHPs. This may have implications for the out of Africa dispersal of plants and animals throughout the Quaternary. A climate model identifies that periodic wet phases in the Sahara, termed North African Humid Periods, were driven by Earths orbital variations and were suppressed during glacial periods due to the influence of extensive ice sheets. The alternation of arid and humid phases and consequent dramatic environmental changes in this vast region forms a key biogeographic factor influencing species’ distributions and evolution19. It has also been suggested that changes in the precipitation and vegetation cover of the Sahara have consequences beyond Africa, influencing the climate system from the tropics to the Arctic11. Currently, 230 of these North African humid periods (NAHPs) have been identified in proxy records over the past 8 myr8. They were ultimately driven by the precession cycle, which governs the seasonal insolation contrast and is modulated by eccentricity, with periods of increased boreal summer insolation  intensifying the African monsoon system24. Internal biogeophysical feedbacks then amplified the external orbital forcing and further enhanced precipitation28.There is widespread palaeoclimatological evidence indicating that the Sahara has experienced wetter periods in the past, with proliferation of vegetation, rivers and lakes into what is now desert23, some records show gaps in NAHPs at intervals of precession minima termed skipped beats12. These coincide with glacial periods, suggesting that the monsoon response could be modulated by eccentricity and/or obliquity. These glacial skipped beats have previously been linked to the modulation of precession by eccentricity8. However the glacial boundary conditions, namely CO2 and the extent and shape of the ice sheets which are additionally forced by eccentricity, may also indirectly influence the African monsoon response to insolation change30. Problematically, most climate models fail to reproduce the precipitation change expected during the NAHPs, including the most studied NAHP in the Holocene33. Furthermore, studies investigating the impact of glacial boundary conditions on the NAHPs are not conclusive. Some indicate that the NAHPs are not sensitive to the boundary conditions and are driven predominantly by orbital forcing34. Others conclude that NAHP suppression during the skipped beats was driven primarily by reduced glacial CO2 concentrations6, or conversely was due to extensive ice sheets during glacials15. Another study concludes that both forcings were responsible for the skipped beats21. Currently therefore the mechanisms that drive the NAHPs remain poorly constrained.Although precession is evident in numerous monsoon proxies2 concentrations do not significantly impact NAHP timing or amplitude. In contrast, we show that extensive ice sheets during glacials suppress NAHPs, this drives the skipped beats and elucidates a high latitude control on the NAHPs. Eccentricity therefore indirectly forces the NAHPs via its control over northern hemisphere glaciation. While the model simulates humid periods that have good agreement with regional proxy data across much of North Africa, precipitation levels remain below proxy-based expectations in the Northeast Sahara and the Arabian Peninsula, which suggests that our understanding of North African precipitation variability is possibly still incomplete.Here, we address these outstanding research questions using a recently developed version of a coupled general circulation model  that enables us to simulate the dynamics of NAHP events over the past 800 kyr. This allows us to clarify the forcings and mechanisms that drive the NAHPs and the skipped beats. Our results show that the modelled NAHPs are paced by precession due to their influence on the West African Monsoon (WAM) and regional jet streams. Glacial CO38. This disparity between models and observations has persisted for decades across numerous climate models, and has been linked to misrepresentation of vegetation, ocean and dust feedbacks40. How these could be reconfigured in models remains unclear42. Instead, recent work has modified convection and land-surface parameterisations44. This palaeo-conditioning was shown to markedly improve the simulation of the Holocene NAHP44, however, it has not been tested beyond this time period.Climate models largely fail to reproduce the intensification of NAHP precipitation implied by observations45 and incorporate the updated atmospheric parameters and vegetation parameterisations identified by Hopcroft, et al.44 in order to palaeo-condition the model and constrain NAHP timing and mechanisms over the past 800 kyr (methods). The model is herein termed HadCM3BB-v1.0. We performed 219 snapshot simulations with results splined to a continuous timeseries on a 100-yr timestep (methods). Although there are limitations to the snapshot approach 48 to 640 mm/yr in the Western Sahara 14. This compares to a modelled 494 mm/yr and 584 mm/yr for the same regions for 6–11 kyr BP.Figure 8  and the upper Nile sources  may have been enough to support river networks and lakes downstream. These, in addition to other remote sources such as groundwater, may have supported vegetation growth across the region, rather than enhanced local precipitation sources58. However, the Arabian Peninsula poses a more marked conundrum. Modelled precipitation is limited to the south-west (580 mm/yr), which deteriorates to <100 mm/yr within the interior. We identify that this is an outstanding caveat in the model, and further research is required in order to identify the reasons for the model to underperform in this region, and how this should be rectified. It may be linked to an inaccurate response of the East African or Indian Monsoon systems to insolation change, or to the response of tropical plumes which affect Northern Arabia and may have been important during the NAHPs62. Additionally, Mediterranean storm tracks, which are an important winter moisture source along the Mediterranean coastline63, may be poorly resolved in the model. Improved representation of these may enhance winter precipitation as shown in other modelling studies21, albeit their impact was isolated to the coastlines. Further progress with palaeo-conditioning of the model, and utilising high-resolution or regional models which better resolve these processes66, may reconcile these biases and is a focus of future work.The modelled NAHP amplitude in the North-eastern Sahara (Egypt and N.E. Libya), and the Arabian Peninsula is likely too small Fig. , with wi67).Our results confirm that precession is the dominant pacemaker of the NAHPs. This is verified by wavelet analysis Fig. , which s70. The summer monsoon, indicated by westerly (positive) zonal wind, extends to 18°N during precession maxima 22 concluded that although glacial ice sheets and CO2 concentrations contributed in suppressing NAHP precipitation during glacials, it was predominantly a response to weaker precession forcing . Kutzbach, et al.6 used the CCSM3 model and concluded that glacial boundary conditions reduce summer NAHP precipitation, but the overall retreat of monsoon rain was relatively small. This was attributed to lower CO2 concentrations, which cools tropical temperatures and reduces equatorial rains. Conversely, larger ice sheets increased winter precipitation due to a southward shift in the winter jet.Past modelling studies that have investigated greenhouse gas and ice sheet forcing on monsoons and the NAHPs have proved inconclusive. A study15 emphasised the importance of ice sheet forcing. They concluded that ice sheet retreat at the last and penultimate glacials was required to initiate the Holocene15 and Eemian9 NAHPs. Timm, et al.15 concluded that this retreat altered the background atmospheric state causing enhanced convective activity, this amplified the atmospheric response to orbital forcing. Menviel, et al.9 concluded that glacial ice sheets caused a more southerly ITCZ and weaker WAM which inhibited AHP amplitude, although this was of secondary importance to the strength of the Atlantic meridional overturning circulation (AMOC). Both studies concluded that CO2 concentration had no impact on AHP timing or amplitude. Another sensitivity study using LOVECLIM21 also showed that NAHP supression during the last glacial was primarily due to ice sheet forcing, however they showed that CO2 also influenced NAHP amplitude , although the reasons for this were not explored. These studies therefore imply an ice sheet determined orbital signal in Saharan precipitation, however they all used a low resolution EMIC, covered a relatively short temporal timescale, and did not explore the direct mechanisms in detail21.In contrast, studies using the LOVECLIM EMIC77 and therefore eccentricity indirectly forces the NAHPs via its influence on the ice sheets.In order to clarify the impact of the boundary conditions, we performed additional sensitivity experiments to evaluate the role of greenhouse gas and ice sheet forcing Fig. ; methods10 concluded that obliquity was a dominant control on NAHPs between 350 to 450 kyr BP. Our modelled results similarly indicate that obliquity has a significant influence on Saharan precipitation during this period .The Hadley Centre Coupled Model 3 Bristol (HadCM3B) is a coupled climate model that consists of a 3d dynamical atmospheric component with a resolution of 2.5° × 3.75°, 19 vertical levels, and a 30 minute timestep91 which simulates water and energy fluxes and physiological processes such as photosynthesis, transpiration and respiration which is determined by stomatal conductance and consequently CO2 concentration. The fractional coverage of nine surface types are incorporated by MOSES 2.1 and simulated by the dynamic global vegetation model (DGVM) TRIFFID. Of the nine surface types, five are plant functional types (PFTs); deciduous and needleleaf trees, C3 and C4 grasses, shrubs, with the residual assigned to bare soil. Vegetation evolves throughout a model simulation depending on temperature, moisture, CO2 and competition with other PFTs. HadCM3B does not include an interactive carbon/methane cycle or ice model, so these boundary conditions have been imposed as discussed below.The model incorporates the Met Office Surface Exchange Scheme (MOSES) version 2.144. The most important change was to modify the vertical profile of convective entrainment and detrainment, decreasing the entrainment rate near the surface and increasing the entrainment rate higher up in the atmosphere globally. This resulted in greater mixing between convective plumes and the environment in the upper troposphere relative to the lower troposphere increasing the precipitation response from African Easterly Wave events. Hopcroft, et al.44 showed that this permitted the model to more accurately simulate mid-Holocene greening of the Sahara. At the same time, it did not adversely influence present-day climate simulations. The moisture stress function of vegetation was also altered to be linear in soil moisture potential rather than soil moisture, and then optimised to reproduce vegetation cover-climate relationships for the present day and the mid-Holocene43.The update to the atmospheric and vegetation parameters follows the work of Hopcroft, et al. 92 targeted on seven observational targets. The performance of the model was quantified in a probabilistic sense that accounts for structural error in the model—that is the conditioning was specifically designed not to expect a perfect model—since this could easily lead to the right answer for the wrong reasons. Five of the tuning targets are for the present day: annual mean temperature, tropopause water vapour, central and north Africa annual mean precipitation and present-day tropical vegetation cover. The remaining two targets are derived from the mid-Holocene: pollen-inferred North African 6 kyr BP precipitation and vegetation cover. The paleo-conditioned model was then validated43 using a leaf-wax precipitation record14 and other records93.The model was tuned using a Bayesian statistical methodology93, including a more gradual and later desertification in the East and South of the Sahara and a centennial-scale transition over North Western Sahara. The methodology differs to previous NAHP modelling studies94 as they prescribed the greening of the Sahara, i.e., implicitly assumed that the Sahara is 100% vegetated, whereas here we are dynamically simulating the connection between forcings  and the land-surface/atmosphere system.Together these updates allow a dynamic simulation of the Holocene greening of the Sahara that shows many similarities with the reconstructions of this time periodAfrican Humid Periods (and dry phases) that occurred before the Holocene were not used to condition (tune) the model in any way. This study is therefore the first attempt to evaluate the palaeo-conditioned model for periods beyond the mid-Holocene against the spectrum of humid phases back to 800 kyr BP. We have incorporated the changes described above into HadCM3B, the updated model configuration is termed HadCM3BB-v1.0.95 and greenhouse gas concentrations  taken from the Vostok Ice core97.The simulations have been forced with well-constrained orbital parameters98 This provides ice extent and thickness which are used within the model to calculate continental elevation (depending on ice thickness and isostatic rebound), bathymetry, ice sheet extent, and consequently the land–sea mask. There remain uncertainties regarding Ice sheet reconstruction beyond the LGM, in part due to poor preservation following the last deglaciation. Although the ice area might be approximated based on sea-level data, uncertainties remain associated with ice sheet structure during growth and decay phases. As stated in the text, this may cause uncertainty regarding the suppression of some the NAHP events and be the reason for inconsistencies between the model and reconstructions.The extent and elevation of the Antarctic, Greenland, North American and Fennoscandian ice sheets has been imposed utilising the reconstruction of de Boer et al.The boundary conditions have been incorporated into 219 snapshot experiments covering 0 kyr BP  to 800 kyr BP. These are set at 1 kyr intervals for 0–24 kyr BP and 4 kyr intervals to for 24–800 kyr BP. Each simulation has been run for 500 years with analysis conducted on the final 50 years of each simulation unless stated otherwise. This spin-up permits the atmosphere and surface ocean to reach a state of near equilibrium. This approach allows the simulations to be run simultaneously and is therefore very efficient.99 which utilises spline under tension. This is the same methodology to that outlined in Armstrong, et al.46. The timeseries data was then bilinearly interpolated to 1° resolution for surface variables. There has been no bias correction applied to the data in this study.The snapshot climatologies were then splined to a 100-yr timeseries for all variables used in the study, including the land–sea mask and ice fraction (subsequently rounded to 0 or 100% coverage in any grid cell). Splining has been done using the ftcurv function of the NCAR command language (NCL)2—280 ppm, CH4—760 ppbv, and N2O—270 ppbv) and ice sheet extent and elevation, this is termed Orb_Only. The third set of snapshot simulations incorporates both orbital and GHG forcing, with constant pre-industrial ice sheets, termed Orb_GHG.In order to identify the contributing role of three forcing factors on the NAHPs; orbital variations, GHGs and ice sheets, we performed two additional sets of snapshot experiments (2 × 219 simulations) using HadCM3BB-v1.0. The original set of snapshot simulations incorporate all three forcings (as discussed above) and is termed NAHP_All. In order to identify just the orbital forcing, the simulations were re-run with varying orbital parameters, but with constant pre-industrial GHGs (COThe snapshot climatologies were similarly splined to a 100-yr timeseries and bilinearly interpolated to 1° resolution for surface variables.45) and incorporated the updates described above (HadAM3BB-v1.0). We have run the model controlling for SSTs, ice sheet elevation, and ice sheet albedo.In order to resolve the topographic vs thermodynamic contribution of the ice sheets to the suppression of the NAHPs during precession minima, we have used the atmosphere-only version of the model . This incorporates the boundary conditions and SST input from the 176 kyr NAHP_All snapshot experiment. The climatology is therefore very similar to the 176 kyr BP HadCM3BB-v1.0 NAHP_All experiment section . In ordeSupplementary InformationPeer Review File


Due to the rapid motility of the selected sperm, sperm parameters cannot be accurately determined by the manual method. So, the application of a computer‐assisted sperm analysis system with a high frame rate  in sperm selection is investigated.A total of 177 semen samples were collected for sperm selection with density gradient centrifugation. Then, the manual method and HFR‐CASA method will be used to observe and analyze the sperm concentration, motility, and percentage of progressively motile sperm (PR) of the selected sperm samples. The quality control of sperm concentration was performed with microballoons. Two selected sperm samples were analyzed 10 times repeatedly to evaluate the accuracy of the HFR‐CASA.p < 0.001), the manual method significantly underestimated sperm concentration (p = 0.002) but overestimated sperm motility and PR (p < 0.001). When sperm concentration was below 50 × 106/mL, the manual method significantly overestimated sperm concentration (p < 0.05). However, when sperm concentration was more than 100 × 106/mL, the manual method significantly underestimated sperm concentration (p < 0.001). Regardless of sperm concentration, the manual method consistently overestimated sperm motility and PR (p < 0.001). When sperm concentration was more than 20 × 106/mL, there was no correlation in sperm PR between them (p > 0.05). When sperm concentration was below 50 × 106/mL, the correct rate of captured sperm by the HFR‐CASA was more than 98%.The results of microballoons analyzed with the HFR‐CASA were in control. The coefficients of variation of sperm concentration, motility, and PR from two selected sperm samples were all below 10%. The results of 177 selected sperm samples analyzed by the manual method and HFR‐CASA showed that although there were significant positive correlations in sperm concentration, motility, and PR between them (The HFR‐CASA method is more accurate than the manual method in analyzing the selected sperm samples. After abstinence for 2–7 days, semen samples were collected from each patient by masturbation and put into an aseptic semen collection cup. After routine examination, the remaining semen samples were used in this study. This study was conducted in the Center for Reproductive Medicine of Zhongda Hospital Affiliated to Southeast University, which was approved by the Reproductive Medicine Ethics Committee of Zhongda Hospital affiliated to Southeast University (Reproduction No. 2015‐1), and all patients signed the informed consent. To demonstrate that the analysis of selected sperm should use an HFR‐CASA instead of the manual method, we performed the study.2.26/mL) were provided by Hua Yue Medical Technology Co., Ltd. SpermGrad lower layer (90%), the upper layer (45%), and SpermRinse (Vitrolife) were used for sperm selection.The HFR‐CASA system  with 85 Hz of frame rate and quality control materials for sperm concentration  are commonly used as quality control materials to evaluate the accuracy and stability of sperm concentration.2.4Two fresh semen samples from different patients were randomly collected, and sperm selection was performed with density gradient centrifugation (DGC). The selected sperm suspensions were detected 10 times repeatedly by the HFR‐CASA system, and the coefficient of variation (CV) was calculated.2.5g under room temperature, the upper liquids were carefully aspirated away using a pipette, and sperm sediments were transferred into a new centrifuge tube with the help of 3 mL of the SpermRinse solution. The mixture was blown up and down slowly and then centrifuged for 10 min at 200g. Next, the upper liquids were carefully aspirated away using a pipette, and 0.5 mL of the SpermRinse solution was added. The sperm sediments were blown gently, and the selected sperm suspension was obtained.After routine examination, the remaining semen samples were kept at room temperature (25°C). The SpermGrad lower layer (90%), upper layer (45%), and SpermRinse solutions were taken out from a refrigerator and recovered to room temperature for further use. First, 1 mL of SpermGrad lower layer (90%) solution was added into a 15‐mL centrifuge tube. Then 1 mL of SpermGrad upper layer (45%) solution was gently added to the surface of the SpermGrad lower layer (90%) solution. Next, normally liquefied semen was slowly added to the surface of the SpermGrad lower layer (45%) solution, and a clear interface between semen and gradient solutions could be seen. After 20 min of centrifugation at 400The obtained selected sperm suspension was divided into two portions. One portion was sent to the embryo laboratory. An embryologist with at least 5 years of working experience evaluated sperm concentration, motility, and the percentage of progressively motile sperm (PR) by the manual method. The other portion was sent to the reproductive testing laboratory, where a technician analyzed sperm concentration, motility, and PR by the HFR‐CASA system. All the detection results were sent to a third technician to analyze the data. All three technicians were blinded to each other's results.2.6Five microliters of selected sperm suspension were loaded into a Makler counting chamber, and sperm concentration, motility, and PR were evaluated visually under an ordinary optical microscope.2.7The HFR‐CASA system was equipped with a Disposable Double Chamber Slide , which consisted of one glass slide and two coverslips. Each glass slide had two counting chambers. Before adding a sample, the slide and coverslip were put on a constant temperature heating platform (37°C). First, one counting chamber was incompletely covered by one coverslip. Then, 5 μL of selected sperm suspension was loaded into the junction between the counting chamber and coverslip. Next, the coverslip was slowly pushed in parallel with the glass slide until the coverslip thoroughly covered the counting chamber. Last, the counting chamber was put on the stage of the HFR‐CASA system, which was placed in a thermostated container (37°C). In analyzing selected sperm suspension, 15 fields of view were selected from each sample randomly, and each field of view was scanned at 85 frames per second. The first 50 frames of pictures for each sample field were saved. Finally, the results of sperm concentration, motility, and PR were outputted automatically by the HFR‐CASA system. The total analysis time of each sample was about 1 min. The trajectory diagram of the selected sperm is shown in Figure 2.8The sperm concentration of a sample was determined based on sperm in the first frame of each field counted by the HFR‐CASA system. So, the manual validation of correct rates for captured sperm by the HFR‐CASA system was also conducted as sperm in the first frame of each field. By observing the pictures of the first, second, and third frames consecutively Figure , sperm a2.9t test was used; if the data conform to a nonnormal distribution, Wilcoson‐signed rank test was used. For the correlations of sperm concentration, motility, and PR for selected sperm suspensions between the manual method and HFR‐CASA system, when the data were consistent with normal distribution, Pearson correlation analysis was conducted. Otherwise, Spearman correlation analysis was done. They all performed the two‐sided test. p ≤ 0.05 was considered to be statistically significant.The data were analyzed by statistical software SPSS 20.0 (SPSS Inc.). The accuracy (repeatability) assessment data of the HFR‐CASA system were expressed as mean ± standard deviation (SD), and their CVs were calculated. The data of sperm concentration, motility, and PR for selected sperm suspensions were first performed by one‐sample nonparametric tests (Kolmogorov–Smirnov test) to determine whether they were normal distribution. The data conforming to normal distribution were expressed as mean ± SD, and those conforming to nonnormal distribution were expressed as median . For the comparisons and hierarchical comparisons of sperm concentration, motility, and PR for selected sperm suspensions between the manual method and HFR‐CASA system, if the data conform to a normal distribution, paired 33.16/mL of nominal value were detected three times by the HFR‐CASA system repeatedly. The results were 40.5, 39.9, and 40.1 × 106/mL, all of which were in the range of the nominal value. The results of microballoons before each subsequent detection were also in the nominal value range.The quality control materials of microballoons with (42 ± 5) × 103.2The CVs of sperm concentration, motility, and PR from two samples of selected sperm suspension analyzed 10 times by the HFR‐CASA system were below 10% . Compared with the HFR‐CASA system, the manual method significantly underestimated sperm concentration (p = 0.002) but overestimated sperm motility and PR . However, when sperm concentration was more than 100 × 106/mL, the manual method significantly underestimated sperm concentration (p < 0.001). When sperm concentration was 50.1–99.9 × 106/mL, there was no significant difference in sperm concentration between the two methods (p > 0.05). Regardless of sperm concentration, the manual method always overestimated sperm motility and PR (p < 0.001). There was a significant correlation in sperm PR between the two methods only when sperm concentration was ≤20 × 106/mL. When sperm concentration was more than 20 × 106/mL, there was no correlation in sperm PR between the two methods (p > 0.05). Regardless of sperm concentration, sperm concentration detected by the manual method was significantly and positively correlated with that by the HFR‐CASA system (p < 0.01). When sperm concentration was <100 × 106/mL, there was a significant correlation in sperm motility between the two methods. However, when sperm concentration was ≥100 × 106/mL, there was no correlation in sperm motility between them (p = 0.496).High sperm concentration might affect the analysis results of sperm motility. So, we compared the differences and correlations of sperm concentration, motility, and PR between the manual method and the HFR‐CASA system hierarchically according to sperm concentrations detected by the HFR‐CASA system, and the results were shown in Tables 3.5p > 0.05, Table 6/mL, the correct rate of captured sperm by the HFR‐CASA system was more than 98%. With the increase in sperm concentration, the correct rate of captured sperm by the HFR‐CASA system declined, but they were within (100 ± 6)%.The results of manual validation for six samples of selected sperm suspension at different sperm concentrations showed no significant difference in the numbers of sperm counted by the manual validation and HFR‐CASA system . When sperm concentration was below 50 × 106/mL, the correct rates of captured sperm by the HFR‐CASA system were more than 98%. As sperm concentration increased, the correct rates of captured sperm by the HFR‐CASA system declined but were within (100 ± 6)%, indicating that the correct rates of captured sperm by the HFR‐CASA system were more than 94% even in analyzing the samples with high sperm concentration (>100 × 106/mL), and that the HFR‐CASA system could accurately analyze the samples with high sperm concentration without dilution.We first evaluated the accuracy and repeatability of the HFR‐CASA system. Quality control materials of microballoons were used to ensure the detection accuracy of sperm concentration. The quality control results of repeated detection three times and the subsequent detection were in the nominal value range, indicating that the HFR‐CASA system could ensure the analysis accuracy of sperm concentration. The CVs of sperm concentration, motility, and PR from two samples of selected sperm suspension detected by the HFR‐CASA system were below 10%, indicating that the HFR‐CASA system had good repeatability for the analysis of sperm concentration and motility. The manual validation for the correct rates of captured sperm in six samples of selected sperm suspension at different sperm concentrations showed no significant difference in the number of captured sperm between the HFR‐CASA system and manual validation (p < 0.001), the manual method significantly overestimated sperm motility and PR (p < 0.001) but underestimated sperm concentration (p = 0.002), indicating that the manual method was unsuitable for evaluating the samples with high‐motility sperm. The results of sperm concentration, motility, and PR detected by the two methods were further analyzed hierarchically, and it was found that there was a significant correlation in sperm PR between the two methods only when sperm concentration was ≤20 × 106/mL. When sperm concentration was more than 20 × 106/mL, there was no correlation in sperm PR between the two methods (p > 0.05). When sperm concentration was below 50 × 106/mL, the manual method significantly overestimated sperm concentration (p < 0.05). However, when sperm concentration was more than 100 × 106/mL, the manual method significantly underestimated sperm concentration (p < 0.001). Regardless of sperm concentration, the manual method always overestimated sperm motility and PR (p < 0.001). All of these indicated that the manual method was unsuitable for analyzing sperm concentration, motility, and PR of the samples with high‐motility sperm regardless of sperm concentration and that an HFR‐CASA system might be the only choice at present.The analysis results of 177 samples of selected sperm suspension showed that although sperm concentration, motility, and PR were all significantly and positively correlated between the two methods . Since the number of samples with sperm concentration >100 × 106/mL was relatively small and such samples were considered normal, it was of little significance to study these samples. Therefore, we could think that the HFR‐CASA system was not only suitable for the analysis of selected sperm samples but also for routine semen analysis.Although it was reported that the use of a CASA system for routine semen analysis was not suggested in samples with sperm concentration <15 × 10The frame rate may affect sperm motion parameters. It was reported that completely different results could be obtained only by changing the frame rate.It was reported that a CASA system with a frame rate of 15 Hz could be a reliable diagnostic tool for routine semen analysis, which could provide clinically acceptable results based on the 5th edition of the World Health Organization (WHO) guidelines.6/mL, the collision between sperm may affect the movement orbits of sperm, leading to the fluctuation in the results of sperm concentration. So, before the samples with high‐motility sperm are analyzed in the course of daily laboratory work, sperm concentrations should be diluted to be below 50 × 106/mL. The WHO manual (sixth) recommends that semen samples with high sperm concentration should be diluted with seminal plasma from the same patient, since dilution with normal saline may affect sperm motility.Nevertheless, three considerations should be paid attention to when using the HFR‐CASA system to analyze the samples with high‐motility sperm. First, the counting chambers should be used correctly. The standard sperm counting chambers include reusable counting chambers, such as Makler and disposable counting chambers, such as Leja.5The HFR‐CASA system used in this study may accurately analyze sperm concentration, motility, and PR of selected sperm suspensions, but the manual method overestimates sperm motility, and PR. Moreover, the manual method significantly overestimated sperm concentration for the samples with low sperm concentration but underestimated sperm concentration for the samples with high sperm concentration. Therefore, the samples with selected sperm suspension can be analyzed by an HFR‐CASA system. The accurate analysis of selected sperm suspensions by an HFR‐CASA system may lay the foundations for clarifying the relationships of the total number of selected PR sperm with the selection of IUI, IVF‐ET, or ICSI and pregnancy outcomes of IUI, IVF‐ET, and ICSI. Moreover, an HFR‐CASA system may be used to compare the effect of sperm selection of different sperm selection methods, such as DGC, microfluidic technology, swim‐up method, glass fiber filtration, serum albumin filtration, and swimming precipitationConception and design: Jin‐Chun Lu. Administrative support: Yuan‐Jiao Liang. Provision of study materials or patients: Jin‐Chun Lu and Yuan‐Jiao Liang. Collection and assembly of data: Shan‐Shan Tang, Yuan‐Hua Xu, Jing Wang, Ren‐Yun Hong and Yan‐Mei Ge. Data analysis and interpretation: Jin‐Chun Lu and Shan‐Shan Tang. Manuscript writing: All authors. Final approval of manuscript: All authors. All authors have read and approved the final version of the manuscript, full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.The authors declare no conflict of interest.The lead author Jin‐Chun Lu affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned  have been explained.Supplementary video 1: Motion video of selected sperm. Selected sperm were obtained by density gradient centrifugation. Five microliters of selected sperm suspensions were loaded into the counting chamber. Then, the counting chamber was put on the stage of a computer‐assisted sperm analysis system with a high frame rate , which was placed in a thermostated container (37℃). Subsequently, the HFR‐CASA system automatically focused and recorded sperm motion videos. The sperm movement speed shown in this video is relatively slow, and the manual method can still count the number of sperm.Click here for additional data file.Supplementary video 2: Motion video of selected sperm. Selected sperm were obtained by density gradient centrifugation. Five microliters of selected sperm suspensions were loaded into the counting chamber. Then, the counting chamber was put on the stage of a computer‐assisted sperm analysis system with a high frame rate , which was placed in a thermostated container (37℃). Subsequently, the HFR‐CASA system automatically focused and recorded sperm motion videos. The sperm movement speed shown in this video is very fast, and it is impossible to accurately count the number of sperm using the manual method.Click here for additional data file.


Root rot is one of the main diseases affecting Aconitum carmichaelii Debx. during cultivation, seriously limiting yields of this herb. Currently, there is no effective control measure for aconite root rot. The antifungal activities of antagonistic strains against aconite root rot pathogens  were investigated in this study. Three antagonistic strains, JKT7, JKT28 and JKT39, were screened and identified as Trichoderma asperellum, Trichoderma hamatum and Trichoderma virens, respectively. Dual culture tests showed that the inhibition rates of the three Trichoderma strains on the pathogens were all approximately 70%. The volatile metabolites had inhibitory effects on the mycelial growth of pathogens, while the nonvolatile metabolites in the culture filtrates did not show significant inhibitory effects. The volatile components analyzed by GC‒MS were mainly ketones, esters, and alcohols. These results indicate that these strains of Trichoderma and their secondary metabolites have antimicrobial activities against the pathogens of aconite root rot. This study could provide a scientific basis for the biocontrol of aconite root rot. Its root system is a well-known medicinal material used in China, India and other Asian countries. There are many diseases affecting A. carmichaelii Debx. during cultivation, including root rot, white rot (sclerotia), leaf spot, downy mildew, etc. Among them, root rot most significantly limits the yield of A. carmichaelii Debx.1 Chen et al.2 found that Fusarium proliferatum, F. solani, and F. oxysporum are the main pathogens causing root rot of A. carmichaelii Debx. Currently, the control of root rot mainly utilizes chemically synthesized pesticides.3 However, the excessive use of pesticides can easily cause environmental pollution and loss of soil fertility.5 Thus, the quality and safety of these medicinal materials are affected.6 In addition, pesticide residues can destroy beneficial microorganisms in the soil7 and increase the resistance of pathogens. An increasing number of studies have shown that biological control of plant diseases has more advantages than chemical control and is more efficient and environmentally friendly.8–11Trichoderma have the characteristics of rapid growth7, diverse biological control mechanisms and rich metabolites. Studies have shown that various Trichoderma spp. fungi have significant effects on the control of plant diseases. The T. harzianum strains TvDPs, TDPs and T1s, isolated from the soil of Phoenix dactylifera by Perveen and Bokhari12, reduce the occurrence of root rot caused by F. oxysporum. The biological control mechanisms of Trichoderma include competition with the pathogen for nutrients and living space, production of metabolites with antifungal activities, and induction of resistance in plants, etc.13–16 Many secondary metabolites with important biological functions have been found in different species of Trichoderma17, including different classes of antifungal compounds: (a) volatile antibiotics, e.g., 6-pentyl-α-pyrone (6PP) and most of the isocyanide derivates; (b) water-soluble compounds, e.g., heptelidic acid or koningic acid; and (c) peptaibiotics and peptaibols.18 Their interactions with plants have been studied in depth, and some secondary metabolites have been found to participate in the regulation of plant growth and activate plant defense responses.19–21 Several studies have shown that Trichoderma can induce plants to produce terpenoids, phenols and other compounds in the process of interacting with host plant roots to improve plant resistance to diseases.22–26As a group of important biological control fungi, Trichoderma strains that have strong antagonistic effects on pathogens of aconite root rot were screened from the rhizosphere soil of healthy A. carmichaelii Debx. plants. The biocontrol potential of these strains against aconite root rot was determined through dual culture tests, assays of the inhibition of pathogenic mycelial growth and spore germination by secondary metabolites, and GC‒MS of volatile metabolites.In this study, 2.2.1.Fusarium strains were isolated from A. carmichaelii Debx. plants with root rot obtained from the GAP base in Puzhao Village, Jiangyou, China. The strains were identified as F. proliferatum, F. solani, and F. oxysporum based on their morphological characteristics and 18S rDNA gene sequencing, and they were stored on potato dextrose agar slant culture medium at 4°C.Three 6 spores/mL) of the pathogen was prepared after being shaken slightly on a shaker for 30 min and filtered with sterile filter paper to remove mycelia.The pathogens were grown on potato dextrose agar (PDA) medium in Petri dishes at 28°C for 3 days. Spores of pathogens were washed thoroughly with 20 mL of sterile water. The spore suspension .The soil sample was collected from the rhizosphere soil of healthy 27 was used to separate the fungi from the soil. The soil was weighed (2.5 g) into a 50 mL Erlenmeyer flask with 25 mL of sterile water. The mother liquor was obtained after shaking for 30 min at 120 rpm and standing for 30 min. One milliliter of mother liquor was taken and diluted sequentially into six gradients from 10−1 to 10−6. From each dilution, 100 μL of the solution was taken and spread evenly on a PDA plate. Sterile water was applied as a blank control. The plates were incubated at 28°C for 3–5 days. Fungal colonies were picked and streaked onto PDA plates.27 Pure subcultures were obtained from growing colonies on PDA and stored at 4°C until use.The serial dilution spread plate techniqueA 4-mm mycelial disk of pathogen was cut from the edges of actively growing colonies and placed in the center of a PDA plate. Four different strains to be tested were inoculated at 2 cm from the mycelial disk of the pathogen. Each strain was set up with three replicates and cultured at 28°C for 5 days. Antagonistic strains were screened according to the size of the zone of inhibition.2.3.Taq PCR Master Mix . The amplification program comprised initial denaturation at 95°C for 3 min, followed by 30 cycles of denaturing at 95°C for 30 s, annealing at 54°C for 45 s, extension at 72°C for 1 min by 30 cycles, and a final extension for 10 min at 72°C. The amplified fragments were purified and sequenced by an external service provider . The sequences were used to perform a BLAST search against the National Center for Biotechnology Information (NCBI) sequence database (GenBank) to compare the sequences with the most similarity to identify each fungus. A phylogenetic tree was reconstructed, and the evolutionary history was inferred using the neighbor-joining method.28 Tree topologies were evaluated by performing bootstrap analysis of 1,000 datasets29 with MEGA 7.0.30The genomic DNA of the antagonistic strains was extracted using the E.Z.N.A. Fungal DNA Mini Kit . DNA integrity was analyzed by electrophoresis on 1% agarose gels. The primers for ITS sequencing of the fungi were ITS1 (5′-TCCGTAGGTGAACCTGCGG-3′) and ITS4 (5′-TCCTCCGCTTATTGATATGC-3′). Polymerase chain reaction (PCR) was performed in a 25-μL reaction mixture solution containing 1 μL of DNA, 1 μL each of the downstream and upstream primers, 9.5 μL of double-distilled water, and 12.5 μL 2×2.4.31 was used to determine the inhibitory activities of the antagonistic strains against the root rot pathogens. The three pathogens mentioned above and the selected antagonistic fungi were activated. A 4-mm mycelial disk was cut from the edge of each actively growing colony. One mycelial disk of pathogen was placed on one side of a PDA plate, and another mycelial disk of antagonistic fungus was placed on the opposite side of the same PDA plate. The distance between the two mycelial disks was 5 cm. PDA plates inoculated with each pathogen only were used as controls. Inoculated plates were incubated at 28°C for 7 days, and each treatment was repeated three times. The radial growth of the colony of the pathogen relative to the direction of the antagonistic fungus was observed daily, and the colony radius of the pathogen was measured.The dual culture methodThe percentage of growth inhibition in the radial growth of the pathogen was calculated using the following formula:Percentage of growth inhibition = (C-T)/C × 100,where C is the colony radius of the pathogen in the control group, and T is the colony radius of the pathogen in the treatment group.2.5.32 a 4-mm mycelial disk of pathogen and a 4-mm mycelial disk of antagonistic fungus were cut from actively growing cultures and placed on the center of different PDA plates. The lid of the plate with the antagonistic fungus was replaced by the bottom containing PDA inoculated with the pathogen. The two plates were taped together with parafilm. Pathogen grown on medium without antagonistic fungus was used as a control. The plates were incubated at 28°C for 7 days, and each treatment was repeated three times. The radial growth of the pathogen was observed, and the colony diameter of the pathogen was measured daily.According to the method of Chen et al.,The percent inhibition in the radial growth of volatile metabolites of antagonistic fungi on pathogens was calculated using the following formula:Percentage of growth inhibition = (C-T)/C × 100,where C is the colony diameter of the pathogen in the control group, and T is the colony diameter of the pathogen in the treatment group.2.6.33 Two plugs (4 mm each) of each antagonistic species, cut from the actively growing margins of 3-day-old cultures, were used to inoculate 250 mL Erlenmeyer flasks containing 100 mL of sterile potato dextrose broth (PDB). The liquid cultures were incubated for 7 days at 28°C under stirring (180 rpm). The cultures were centrifuged at 10,000 rpm for 20 min and then filtered through sterilized filter paper and a 0.22 µm nylon membrane to obtain cell-free culture filtrates.The activities of nonvolatile metabolites were studied by the culture filtrate method.Fifty milliliters of culture filtrate of the antagonistic fungus was added to 200 mL of sterilized PDA medium cooled to 50°C and well mixed. Then, the mixture was poured evenly into Petri dishes and solidified. The control set was prepared by using sterile water mixed in the same ratio in PDA medium. A 4-mm agar block of actively growing colonies from 3-day-old cultures of pathogens was cut and inoculated at the center of the above-prepared Petri plates. The inoculated Petri plates were incubated at 28°C for 7 days, and radial growth was measured daily. The percentage inhibition in the radial growth of the colony was calculated by the same formula as described in 2.5.2.7.6 spores/mL) of the pathogen . An Agilent VF-624 ms column (60 m × 0.25 mm × 1.4 μm) was used with hydrogen as the carrier gas, and the flow rate was 1 mL/min. A 1 µL sample was injected in splitless mode, and the vaporizer temperature was 220°C. The oven was programmed to start at 35°C (held for 4 min) and to ramp up to 95°C at 6°C/min (held for 3 min), and then, it was increased to 230°C at 15°C/min (held for 15 min). The ion source temperature was set at 230°C. The ionizing electron energy was 70 eV, and the scan range was 35–350 u. Compounds were identified using the National Institute of Standards and Technology database version 17.0. The results were expressed as a percentage of the volatile organic compounds by dividing the area of each peak by the total area of the chromatogram peaks.3.3.1.A. carmichaelii Debx. were selected.After preliminary screening, three strains  with good antagonistic effects on the three main pathogens of root rot of rDNA of samples JKT7, JKT28 and JKT39 were 599 bp, 584 bp and 621 bp, respectively, and they were submitted to the NCBI database for comparison. Their corresponding accession numbers are MT256288, MT256289 and MT256290. The homologies between JKT7 and Trichoderma asperellum , JKT28 and Trichoderma hamatum , and JKT39 and Trichoderma virens  were all above 99%. A phylogenetic tree was constructed utilizing the neighbor-joining (NJ) method. The results are shown in T. asperellum  were clustered together at 94% of the bootstrap value; JKT28 and T. hamatum  were clustered together at 96% of the bootstrap value; and JKT39 and T. virens  were clustered together at 100% of the bootstrap value. Therefore, JKT7, JKT28 and JKT39 were identified as T. asperellum, T. hamatum and T. virens, respectively.The sequencing results showed that the ITS regions of the 3.2.Trichoderma have significant inhibitory effects on root rot pathogens of A. carmichaelii Debx., and their inhibition rates were all approximately 70%, as shown in T. asperellum showed obvious inhibition of the hyphal growth of pathogens, while the inhibition by T. hamatum was the weakest. In the dual culture assay, the growth rate of Trichoderma was significantly faster than that of the pathogens. On the second day of inoculation, contact between the hyphae of the two fungi could be observed, and the inhibitory effect began to appear. During incubation, Trichoderma gradually occupied the living space and limited the growth of the pathogen. After 4 days of cultivation, the fast-growing mycelia of Trichoderma grew directly on the surface of the pathogen colony, making it difficult for pathogenic mycelia to grow until they gradually atrophied, and finally, the mycelia of Trichoderma filled the plate. Due to inhibition by Trichoderma, the radial growth of the pathogen in the treatment group was significantly different from that in the control group. As shown in The results from the dual culture assay showed that the three strains of 3.3.T. virens on F. solani was shown on the 7th day of culture, with a rate of 62.09%. The antagonistic fungus that had a significant inhibitory effect on F. oxysporum was T. asperellum, with an inhibition rate of 37.00%. Compared with T. hamatum, T. asperellum and T. virens showed better inhibitory activities against F. proliferatum .As shown in 3.4.Trichoderma strains had weak inhibitory effects on the mycelial growth of the pathogens, and all the percentages of growth inhibition were below 20%. As shown in T. asperellum on F. oxysporum increased quickly and peaked on day 4 at 16.10% and then decreased gradually. In contrast, the inhibitory effect of T. asperellum on F. solani showed a v-shaped trend and was the weakest on day 4 at only 5.55%, while the inhibitory effect was basically maintained at approximately 19.50% on day 6 and day 7. The inhibitory effect of T. asperellum on F. proliferatum was the highest (19.21%) on day 6. Compared with T. asperellum, T. hamatum and T. virens had weaker inhibitory effects on the pathogens, and both had better inhibitory activities against F. solani; however, the inhibition rates were only 8–14%. After day 4, the inhibitory activity of T. virens on F. proliferatum was no longer manifested. Although the colony diameter of the pathogen did not change significantly, the hyphae of the pathogen were degraded, indicating that T. virens had a certain antagonistic effect on F. proliferatum.As shown in 3.5.Trichoderma were prepared to examine their effects on the germination of pathogenic spores. Trichoderma strains exhibited inhibitory effects on spore germination during the first 2 hours of cocultivation. In the subsequent observations, there was almost no inhibitory activity. According to the antagonistic activities of the culture filtrates of Trichoderma on mycelial growth and spore germination of pathogens, the nonvolatile metabolites had weak inhibitory activities on pathogens, indicating that the antagonistic components were mainly volatile metabolites.The culture filtrates of 3.6.The components of the culture filtrates extracted by ethyl acetate are shown in 4.A. carmichaelii Debx. in the GAP production base , local herbal farmers currently have no effective biological agents to control aconite root rot. The incidence of the disease can be reduced only by the timely removal of the diseased plants. To provide effective biocontrol for aconite root rot, three fungal strains, T. asperellum, T. hamatum and T. virens, showed significant antagonistic effects on pathogens of aconite root rot through a dual culture assay. After 3 days of dual culture, the inhibition rates of T. asperellum against F. solani, F. oxysporum and F. proliferatum were 37.51%, 43.18% and 37.14%, respectively. The inhibition rates of all three Trichoderma species against aconite root rot pathogens after 7 days of incubation increased to approximately 70%, which were slightly higher than those reported in the literature.36–39 The different antagonistic effects may be due to different strains of Trichoderma and pathogens.According to the field investigation of Trichoderma fungi on the growth of pathogen mycelia were studied. The antagonistic effect was the highest on the 4th and 5th days of incubation, and the growth rates were reduced by the inhibitory effects in the following 48 hours. On the 7th day of culture, the inhibition rates of the three Trichoderma strains ranged from 12.73% to 62.09%. In the study of Zhang et al.,40 the antagonism rates of volatile metabolites of eleven Trichoderma strains against F. oxysporum ranged from 20.83% to 35.29%, among which the volatile substances produced by T. harzianum 223-2M1 had the best inhibitory effect on F. oxysporum. However, in this study, the inhibition rates of the three Trichoderma strains on F. oxysporum ranged from 12.73% to 37.00%. This may have been due to the different types and contents of volatile components produced by Trichoderma strains that grow in different environments, resulting in different antagonistic effects.The antagonistic effects of the volatile metabolites from the three Trichoderma strains showed weak antagonistic effects against the mycelial growth of pathogens, with all rates below 20%. The inhibitory activities of the culture filtrates on the spore germination of pathogens were mainly manifested in the first 2 hours of cocultivation, and no inhibition was observed after 2 hours. Therefore, it was speculated that the nonvolatile components in the culture filtrates were not the main effective components that inhibited the growth of the pathogens.The culture filtrates of three T. asperellum and included four different alcohols as well as esters, silanes, alkanes, ketones and amines. The relative contents of esters and alcohols were higher than those of other components, with values of 9.67% and 2.04%, respectively. A total of twelve volatile components were detected in the culture filtrates of T. hamatum, which were ketones, esters, alcohols, alkanes and silanes. Among them, the relative content of esters was the highest, accounting for 13.03%. In the culture filtrates of T. virens, a total of fifteen volatile components were detected, which were six different ketones, followed by alkanes, alcohols, ethers and alkenes. The relative contents of alcohols and ketones were higher than those of other components, at 5.02% and 2.19%, respectively. The types and contents of volatile metabolites from different Trichoderma species were different. The compound octadecane analyzed by GC‒MS may have antifungal activities against F. oxysporum and F. solani, as well as the compound heptadecane against F. oxysporum.42 Hence, the antifungal activities of the three Trichoderma strains in this study may be related to the presence of these compounds.The volatile metabolites of antagonistic fungi were extracted and analyzed by GC‒MS. A total of thirteen volatile components were detected in the culture filtrates of Trichoderma strains screened in this research showed significant inhibitory effects on the growth of aconite root rot pathogens in the dual culture assay. Their volatile metabolites showed better inhibitory effects than the nonvolatile metabolites, which was different from the results of other scholars on the antagonistic effects of Trichoderma. For example, the antagonistic effects of Trichoderma T38D on soybean root rot pathogens were studied by Zhao et al.43 They found that the inhibitory effects of the metabolites produced by T38D that were difficult to volatilize were much stronger than those of the easily volatilized metabolites, which might be related to the different species of Trichoderma and the different hosts of pathogens.The three 5.Trichoderma strains screened in this study, T. asperellum, T. hamatum and T. virens, could inhibit the growth of aconite root rot pathogens. Their volatile metabolites showed better inhibition of pathogen growth than the nonvolatile metabolites.The three Trichoderma strains as biocontrol agents against aconite root rot, and their secondary bioactive metabolites might be utilized as alternatives to synthetic fungicides to manage medicinal plant diseases.These results highlight the potential advantage of these three


One of the most striking aspects of the primary structure in the hydrophobic domains of the tropoelastin molecule is the occurrence of the VAPGVG repeating sequence. Since the N-terminal tripeptide VAP of VAPGVG showed a potent ACE inhibitory activity, the ACE inhibitory activity of various derivatives of VAP was examined in vitro. The results showed that VAP derivative peptides VLP, VGP, VSP, GAP, LSP, and TRP exhibited potent ACE inhibitory activities, while the non-derivative peptide APG showed only weak activity. In in silico studies, the docking score S value showed that VAP derivative peptides VLP, VGP, VSP, LSP, and TRP had stronger docking interactions than APG. Molecular docking in the ACE active pocket showed that TRP, the most potent ACE inhibitory peptide among the VAP derivatives, had a larger number of interactions with ACE residues in comparison with APG and that the TRP molecule appeared to spread widely in the ACE pocket, while the APG molecule appeared to spread closely. Differences in molecular spread may be a reason why TRP exhibits more potent ACE inhibitory activity than APG. The results suggest that the number and strength of interactions between the peptide and ACE are important for the ACE- inhibitory potency of the peptide. Hypertension is a leading cause of 18 million cardiovascular deaths annually ,3. InhibElastin is the core protein of elastic fiber that provides elasticity and resilience to elastic tissues such as vascular walls, skin, ligaments, and lungs in a variety of vertebrates. Elastin also plays a role in modulating various aspects of cellular function, such as cell migration . The ACE50 of 214, 6.1, 162, 255, 258, 310, and 705 μM, respectively, were found. In this study, a further search for detecting the more potent ACE inhibitory peptides in hydrophobic domains of porcine tropoelastin was carried out in vitro and in silico. In the in vitro study, the potent ACE inhibitory peptides, VLP, VGP, VSP, GAP, LSP, and TRP, and a fairly weak ACE inhibitory peptide, APG, were found. In the in silico study, docking score S values were calculated to evaluate the interaction of ACE inhibitory peptides to ACE, and molecular docking simulation was carried out to clarify the difference between ACE-TRP and ACE-APG interactions.In our previous study , we sougThe purities and molecular weights of newly synthesized peptides were confirmed by high-performance liquid chromatography (HPLC) and mass spectrometry, respectively, and these physicochemical properties are summarized in 50 value was 906 μM, the ACE inhibitory activities of VAP at the N-terminal side and GVG at the C-terminal side of VAPGVG were examined and shown to have IC50 values of 1.0 and 369 μM, respectively. From the fact that VAP exhibited potent ACE inhibitory activity, it was implied that the N-terminal tripeptide sequence of VAPGVG is involved in ACE inhibition. Hence, VAP was regarded as a parent peptide, and the ACE inhibitory activities of five peptides, VA, AP, GAP, VSP, and TRP, which are derivatives of VAP, and two peptides, PG and APG, which are not derivatives of VAP, were examined, and the results are shown in 50 value of 326 or 230 μM, respectively. The dipeptide PG and tripeptide APG, which are not derivatives of VAP, exhibited fairly weak ACE inhibitory activities, with IC50 values of 1814 and 1194 μM, respectively. The tripeptide VLP, VGP, or VSP, which is an Ala residue of VAP, is substituted by a Leu, Gly, or Ser residue, respectively; the tripeptide GAP, which is a Val residue of VAP, is substituted by a Gly residue; and the tripeptide LSP or TRP, which are Val and Ala residues of VAP, are substituted by Leu and Ser residues or Thr and Arg residues, respectively; and all of these exhibited potent ACE inhibitory activities, with IC50 values of 3.9, 26.3, 10.4, 35.0, 1.7, and 1.3 μM, respectively. However, the tripeptide GLG, which is the Val residue of the weak ACE inhibitory peptide GVG, is substituted by a Leu residue and did not show potent ACE inhibitory activity, the same as GVG.The most prominent aspect in hydrophobic domains of the tropoelastin molecule is the occurrence of the repeating VAPGVG sequence. As the ACE inhibitory activity of VAPGVG was fairly weak and its ICThese results proved that the ACE inhibitory activities of peptides that are derivatives of VAP showed higher ACE inhibitory activities than peptides that are not derivatives of VAP. Furthermore, it was demonstrated that the peptides VAP, TRP, LSP, and VLP containing a Pro residue at the C terminus possess potent ACE inhibitory activities, suggesting that the C-terminal Pro residue of VAP is important for ACE inhibition but the Val or Ala residue of VAP is not.® 2018, and a value  of the lowest energy level at the docking state of each peptide and ACE is expressed in 50 values of 1.0, 1.3, 1.7, and 3.9 μM, respectively, exhibited low docking score S values of -10.3512, −11.1199, −10.2541, and −10.9502 kcal/mol, which are less than −10 kcal/mol, respectively. However, the weak ACE inhibitory peptides such as VA, AP, PG, APG, GVG, and GLG, with high IC50 values of 326, 230, 1814, 1194, 369, and 485 μM, respectively, gave high docking score S values of −9.1838, −8.5065, −8.9506, −9.3226, −9.0309, and −9.4905 kcal/mol, which are more than −10 kcal/mol, respectively.Docking score S for the docking of ACE inhibitory peptide to ACE (PDB ID code: 1O8A) was calculated using MOE 50 (logIC50) and the docking score S for ACE inhibitory peptides is plotted in 50 values of ACE inhibitory peptides, the lower the docking score S values between ACE and its inhibitory peptides, and that the ACE inhibitory activity of peptides with both smaller logIC50 values and lower docking score S values were potent. These results suggest that the extent of ACE-inhibiting peptide potency is related to the extent of the tightness of ACE–peptide binding. Furthermore, it is of note that the logIC50 values of ACE inhibitory peptides obtained in vitro appear to correlate with the docking score S values of ACE inhibitory peptides obtained in silico.The relationship between the common logarithm of ICp value for ACE inhibitory peptides, and the number and starting positions of these peptides present in porcine tropoelastin obtained using online tools, are summarized in p value predicts how well each peptide binds to ACE (PDB ID code: 1O8A), and a statistically significant p value of less than 0.05 predicts that its binding is favorable -N-methylmethanaminium hexafluorophosphate N-oxide) and HOBt (1-Hydroxybenzotriazole) in DMF solution.Peptides found in the hydrophobic domains of porcine tropoelastin  were synthesized by a conventional solid-phase method using the Fmoc(9-fluorenylmethyloxycarbonyl) strategy ,35,36,37The progress of the coupling reaction and the deprotection of Fmoc was evaluated using a chloranil test kit  . HBTU, H50 value: the concentration of inhibition required to inhibit 50% of the ACE activity, and each IC50 value was measured three times.The ACE inhibitory activity of newly synthesized peptides was measured under the following condition by using Nakano et al.’s method . ACE  [® 2018  software using the AMBER10: EHT force field, which optimized hydrogen atom addition. The parameters for Protonate 3D were the same as for ACE inhibitory activity evaluation: temperature 310 K, pH 8.3, and salt 0.3 M. This software was additionally used to score the resulting docking poses of peptides in the ACE active pocket via the London dG and GBVI/WSA dG functions in the software. The peptides used in the simulation were the 10 tripeptides VAP, APG, GAP, VSP, TRP, LSP, VGP, VLP, GVG, and GLG, and the three dipeptides VA, AP, and PG. The following parameters were used: Placement; Triangle Matcher; rescoring 1: London dG; Refinement: Rigid Receptor; and rescoring 2: GBVI/WSA dG [® 2018 [Molecular docking simulation was performed using the crystal structure of an ACE model imported from the Protein Data Bank  ,21,39. BI/WSA dG ,40,41. T [® 2018 , and the [® 2018 ,20,37,43p values of ACE inhibitory peptides found in porcine tropoelastin were obtained using the online tools ToxinPred [https://www.uniprot.org/uniprotkb/A0A097ZMY9/entry, accessed on 24 March 2022) [https://webs.iiitd.edu.in/raghava/toxinpred/, accessed on 24 March 2022) was used to predict the toxicity of ACE inhibitory peptides. Protein Calculator v3.4  was used to calculate the net charge at pH 8.3 and pI for these peptides. PepSite2  was used to predict the statistical significance of ACE–peptide interaction using the ACE model (PDB ID code: 1O8A).The toxicity, net charge at pH 8.3 and isoelectric point (pI), and PepSite2 oxinPred , PROTEINoxinPred , and PepoxinPred , respectch 2022) . ToxinPr50 value of 1.3 μM. In silico studies suggest that the docking score S value of the most potent inhibitory peptide, TRP, was the lowest among the derivatives of VAP, predicting a strong docking interaction between TRP and ACE. Moreover, molecular docking in the ACE active pocket demonstrated that TRP formed a large number of interactions with ACE residues in the states of four lower docking score S levels, implying that TRP appears to spread widely in the ACE active pocket. It is of note that the ACE inhibitory potency of TRP obtained in vitro appears to correlate with the docking mode of TRP to ACE obtained in silico. The fact that peptides are non-toxic is an important issue for utilizing peptides as natural functional food ingredients with ACE inhibitory function. Although no biochemical toxicity studies of peptides have been conducted, all peptides are predicted to be non-toxic based on data obtained from online tools.In this study, the ACE inhibitory activity of peptides found in the hydrophobic domains of porcine tropoelastin was investigated in vitro and in silico. In vitro study showed that, among the six peptides, VLP, VGP, VSP, GAP, LSP, and TRP, which are derivatives of the potent ACE inhibitory peptide VAP at the N-terminal side of VAPGVG, an occurrence in hydrophobic domains of tropoelastin, TRP exhibited the most potent ACE inhibitory activity, with an IC


To investigate the ideal trajectory for the S2AI screw and to clinically validate its safety feasibility.The 3D model was reconstructed from CT data of the pelvis of 30 selected adults, and the 3D coordinate system was established with the first sacral superior endplate as the horizontal plane. A set of cutting planes was made at 3 mm intervals in the coronal plane, and the cross-sectional internal tangent circles were divided in the target area. Using the linear fitting function, the axis of 90 mm length was calculated by the least squares method for each inner tangent circle center. The diameter of the axis is gradually increased until the first contact with the cortex, and the cylindrical model is the ideal screw trajectory. The intersection of the axis and the dorsal cortex is the screw placement point, which is located by Horizon Distance (HD) and Vertical Distance (VD); the diameter of the screw trajectory (d) is the diameter of the cylindrical model; the direction of the screw trajectory is determined by Sagittal Angle (SA) and Transverse Angle (TA). The screw trajectory orientation is determined by Sagittal Angle (SA) and Transverse Angle (TA). Based on the ideal screw trajectory, the 3D printed surgical guide and freehand techniques were used to verify its safety feasibility, respectively.The screw placement points , screw placement directions , and screw dimensions for the ideal screw trajectory of the S2AI were combined for analysis. . The S2AI screw superiority rate [96.6% (56/58)] and reasonable rate [100%] were higher in the guide group than in the freehand group , but the differences were not statistically significant (P > 0.05). Although screws invaded the cortex in both groups, there were no associated adverse events in either group.The S2AI screw-based ideal trajectory placement is a safe, feasible and accurate method of screw placement. Spinal pelvic fixation is widely used in adult spinal deformities and degenerative scoliosis –3. Due tThe placement trajectory of the S2AI screw enters through the posterior aspect of the sacrum, penetrates the sacroiliac joint to the iliac bone, and ends above the acetabulum. Study showed that the S2AI screw punctured the screw trajectory and could damage the sciatic nerve, obturator nerve, superior gluteal artery, internal iliac artery, and lumbosacral plexus, resulting in medically induced injuries. Therefore, the safety and accuracy of the S2AI screw trajectory are crucial .Currently, S2AI screw placement methods are divided into freehand techniques and adjunctive techniques. Assistive techniques include 3D printed surgical guides, intraoperative navigation, and robotics to assist in screw placement –20. AlthWe retrospectively collected pelvic computed tomography (CT) data from 30 adult cases, all scanned by a 64-row spiral CT  with a layer thickness of 0.625 mm. The sample included 15 cases of each sex, with an age range of 57.9 ± 17.4 years (range: 20–65 years). Inclusion criteria were: (1) age ≥ 18 years; (2) complete pelvic CT scan data; (3) normal structure of the iliac bone, sacrum, and their attachments. Exclusion criteria included: (1) pelvic developmental deformities; (2) pelvic fractures, infections, or tumors; (3) previous history of pelvic and lumbosacral surgery.The CT data of the 30 patients were imported into Mimics 19.0 software in DICOM format for 3D reconstruction, generating 30 3D models of the pelvis in Stereolithograph (STL) format. The STL format data were imported into Geomagic Studio 12.0 software, where the 3D models of the pelvis underwent filling, repair, and other image optimization processes.With the first supra-sacral endplate plane as the horizontal plane and the pelvis symmetrical to the left and right, a three-dimensional coordinate system was established. A series of cutting planes with a spacing of 3 mm was made parallel to the coronal plane, starting from the dorsal sacral cortex and ending at the anterior superior iliac spine cortex, effectively slicing the whole pelvis into several planes. The cross-sectional profile of the second sacral vertebra, sacroiliac joint, and iliac bone was selected as the target area, and the maximum internal tangent circle was fitted to each cross-section separately, with the center of the circle marked Fig. .An irregular bony channel was obtained by arranging the maximum internal tangent circles of the cross-section obtained from the ipsilateral sacral vertebra, sacroiliac joint, and iliac bone in the coronal plane, respectively, which is the safety domain of the S2AI screw trajectory. The safety domain was treated as a geometry, and the line obtained by least squares calculation of the center of the scattered inner tangent circle was defined as the axis of the safety domain using the line fitting function of Geomagic Studio 12.0 software  and Vertical Distance (VD); the length of the screw trajectory (L) is 90 mm, and the diameter of the screw trajectory (d) is the diameter of the cylindrical model; the direction of the screw trajectory is determined by Sagittal Angle (SA) and Transverse Angle (TA). The orientation of the screw trajectory is determined by the Sagittal Angle (SA) and Transverse Angle (TA) Fig. .We will verify the feasibility of the ideal screw trajectory in two aspects. First, the design of individualized surgical guides to assist in the placement of S2AI screws based on the ideal screw trajectory; second, the freehand screw placement based on the ideal screw trajectory parameters.The data of 64 patients with S2AI screws placed based on the ideal screw trajectory from June 2017 to May 2021 at our hospital were selected. There were 31 male and 34 female cases, age 59.0 ± 12.7 years (range 40–82 years). They were divided into the guide group and freehand group according to the screw placement method. Postoperative CT scans were classified according to the grading method proposed by Oh et al. as Grade 0, i.e., screws did not penetrate the cortex; Grade 1, i.e., screws penetrated the cortex at a distance of less than 3 mm; Grade 2, i.e., screws penetrated between 3 and 6 mm; Grade 3, i.e., the breakthrough distance was greater than 6 mm. The excellent rate was , and the good rate was .The study was approved by the ethics committee of the First People’s Hospital of Yunnan Province, and the patients gave informed consent and signed the surgical consent form before surgery. All surgical operations were performed by the same spinal surgery team.The pelvis model was reconstructed in 3D using pelvic CT data. The ideal trajectory for the S2AI screw was designed according to the method described above. The navigation tube was determined based on this trajectory; the guide plate attachment zone was determined based on the screw placement point, and the reverse template matched with the guide plate attachment zone. The navigation tube was aligned with the reverse template to generate a virtual screw placement guide. Finally, a 3D printer was used to print out the object. Pre-operative sterilization was performed.The posterior median approach was used to reveal the upper fixed vertebra to the second sacral vertebra. The S2AI screws were placed with the aid of a guide plate or by hand.For guide plate-assisted screw placement, the soft tissue in the area of the guide plate attachment was fully stripped, and the guide plate was placed firmly against the corresponding bone surface. A spherical probe was used to check the integrity of the screw trajectory, and the screw was placed after tapping.The screw was placed freehand, and the point of entry was determined with reference to the S1 posterior sacral foramen and the median sacral spine. The direction of the screw trajectory was determined according to the sagittal and cross-sectional angles of the ideal screw trajectory, and the path was opened using an open cone. When the sacroiliac joint was reached, the joint was breached by tapping with a bone hammer. A spherical probe was used to probe the four walls and depth of the bone tract. Then, the S2AI screws were tapped and placed in the direction of the screw trajectory.After screw placement, other operations such as decompression of the spinal canal and correction of scoliosis were performed according to the patient’s condition, and one to two drains were placed after installation of the connecting rods, and the surgical opening was closed with sutures layer by layer.SPSS 22.0 software was used for data analysis. The measurement data conforming to normal distribution were expressed as mean ± standard deviation. Paired χ2 samples t-test was used.The screw placement points , screw placement directions , and screw trajectory dimensions  of the ideal trajectory of S2AI screw were combined and analyzed.128 S2AI screws with a diameter of 7.0–8.0 mm and a length of 70–90 mm were placed in 64 patients (29 in the guide group and 35 in the freehand group), and postoperative radiographs and CT scans were performed.In the guide plate group, 58 S2AI screws were placed: 56 screws of grade 0, 2 screws of grade 1, 0 screws of grade 2, and 0 screws of grade 3. The excellent rate was 96.6%, and the reasonable rate was 100%. Postoperatively, the corresponding screw trajectory parameters  were not statistically significantly different from the ideal screw trajectory parameters, except for SA (P < 0.05) , TA (44.7°±3.3°), HD (5.0 ± 1.0) mm, and VD (18.4 ± 2.4) mm] were statistically different from the ideal screw trajectory parameters (P > 0.05). At the same time, HD was not statistically significantly different . The excellent and good rates were higher in the guide plate group than in the freehand group, but the differences were not statistically significant  and a good rate of 100%. In the freehand group, 70 screws were placed, with an excellent rate of 90.0% (63/70) and a good rate of 97.1% (68/70). The results indicate that either guide plate assisted screw placement or freehand screw placement is safe and feasible based on rational access. Both the excellent and good rates were higher in the guide plate group than in the freehand group, although the difference between them was not statistically significant (P > 0.05), but still clinically significant. When comparing the postoperative screw trajectory parameters with the ideal screw trajectory parameters, there was no statistically significant difference between the guide plate group except for SA. In contrast, the differences between parameters in the freehand group were statistically significant, except for HD. This demonstrates the greater precision of guide plate assisted screw placement. The good rate of 97.1% for freehand screw placement, despite some differences with the ideal screw trajectory parameters, indicates that freehand screw placement based on the ideal screw trajectory has a better tolerance for errors and reflects the safety feasibility of the ideal screw trajectory.In this study, the ideal trajectory of S2AI screw was designed and achieved satisfactory results, but there are still limitations. Limitations of the screw trajectory design include: (1) The number of cases selected for the design of the S2AI screw trajectory was relatively small, which may have biased the results to some extent. (2) We set the screw trajectory length to 90 mm, which may be hidden dangers when inserting screws exceeding 90 mm, although this is rare in clinical practice. O’Brien  found noIn conclusion, the present study is based on a three-dimensional anatomical structure, and the ideal screw trajectory for the S2AI screw was fitted using least squares calculations in an irregular bony channel with safe feasibility in clinical applications. It provides a reference basis for freehand screw placement.The S2AI screw-based ideal trajectory placement is a safe, feasible and accurate method of screw placement.