Depth Bias Research Articles

Solar radiation estimation in West Africa: impact of dust conditions during the 2021 dry season

Abstract. The anticipated increase in solar energy production in West Africa requires high-quality solar irradiance estimates, which are affected by meteorological conditions and in particular the presence of desert dust aerosols. This study examines the impact of incorporating desert dust into solar irradiance and surface temperature estimations. The research focuses on a case study of a dust event in March 2021, which is characteristic of the dry season in West Africa. Significant desert aerosol emissions at the Bodélé Depression are associated with a Harmattan flow that transports the plume westwards. Simulations of this dust event were conducted using the meteorological Weather Research and Forecasting (WRF) Model alone, as well as coupling it with the CHIMERE chemistry transport model, using three different datasets for the dust aerosol initial and boundary conditions (CAMS, GOCART, and MERRA-2). Results show that considering desert dust reduces estimation errors in global horizontal irradiance (GHI) by about 75 %. The dust plume caused an average of 18 % reduction in surface solar irradiance during the event. Additionally, the simulations indicated a positive bias in aerosol optical depth (AOD) and PM10 surface concentrations. The choice of dataset for initial and boundary conditions minimally influenced GHI, surface temperature, and AOD estimates, whereas PM10 concentrations and aerosol size distribution were significantly affected. This study underscores the importance of incorporating dust aerosols into solar forecasting for better accuracy.

MetaCompare 2.0: differential ranking of ecological and human health resistome risks.

While numerous environmental factors contribute to the spread of antibiotic resistance genes (ARGs), quantifying their relative contributions remains a fundamental challenge. Similarly, it is important to differentiate acute human health risks from environmental exposure, versus broader ecological risk of ARG evolution and spread across microbial taxa. Recent studies have proposed various methods for achieving such aims. Here, we introduce MetaCompare 2.0, which improves upon original MetaCompare pipeline by differentiating indicators of human health resistome risk (potential for human pathogens of acute resistance concern to acquire ARGs) from ecological resistome risk (overall mobility of ARGs and potential for pathogen acquisition). The updated pipeline's sensitivity was demonstrated by analyzing diverse publicly-available metagenomes from wastewater, surface water, soil, sediment, human gut, and synthetic microbial communities. MetaCompare 2.0 provided distinct rankings of the metagenomes according to both human health resistome risk and ecological resistome risk, with both scores trending higher when influenced by anthropogenic impact or other stress. We evaluated the robustness of the pipeline to sequence assembly methods, sequencing depth, contig count, and metagenomic library coverage bias. The risk scores were remarkably consistent despite variations in these technological aspects. We packaged the improved pipeline into a publicly-available web service (http://metacompare.cs.vt.edu/) that provides an easy-to-use interface for computing resistome risk scores and visualizing results.

Impact of the Offshore Seismograph Network and 3‐D Seismic Velocity Structure Model on Centroid Moment Tensor Analysis for Offshore Earthquakes: Application to the Japan Trench Subduction Zone

AbstractRecently, a widespread and densely continuous‐recording ocean‐bottom seismograph network has been deployed in the Japan Trench subduction zone. Utilizing the offshore network data improves azimuthal station coverage for offshore earthquakes in the Japan Trench subduction zone. It has a potential to obtain centroid moment tensor (CMT) solutions more accurately than conventional analyses using onshore networks and a simple one‐dimensional seismic velocity structure model. In this study, we conducted CMT inversion for subduction zone earthquakes that occurred between 1 April 2017, and 31 March 2024, with a moment magnitude range of 5.2–7.0. We used seismograms obtained from both the offshore and onshore networks. We calculated Green's functions using a three‐dimensional seismic velocity structure model. Our CMT solutions with thrust‐type mechanisms mostly indicated depths and dip angles consistent with the plate interface. For earthquakes in the outer‐rise region, our CMT solutions were characterized as normal‐fault mechanisms. The joint use of the offshore and onshore networks reduced the estimation errors of the CMT solutions compared with the only use of the onshore network, although the optimal solutions were consistent. The dip angles for the thrust earthquakes determined by our analysis were more consistent with the dip angle of the plate boundary than those determined by conventional CMT analyses. Additionally, we found that the conventional CMT analysis could introduce a systematic bias in depth and magnitude determinations. This finding highlights the importance of an offshore seismograph network and a reliable seismic velocity structure model for CMT inversions.

Standardized Kalman filtering for dynamical source localization of concurrent subcortical and cortical brain activity

ObjectiveWe introduce standardized Kalman filtering (SKF) as a new spatiotemporal method for tracking brain activity. Via the Kalman filtering scheme, the computational workload is low, and by spatiotemporal standardization, we reduce the depth bias of non-standardized Kalman filtering (KF). MethodsWe describe the standardized KF methodology for spatiotemporal tracking from the Bayesian perspective. We construct a realistic simulation setup that resembles activity due to somatosensory evoked potential (SEP) to validate the proposed methodology before we run our tests using real SEP data. ResultsIn the experiments, SKF was compared with standardized low-resolution brain electromagnetic tomography (sLORETA) and the non-standardized KF. SKF localized the cortical and subcortical SEP originators appropriately and tracked P20/N20 originators for investigated signal-to-noise ratios (25, 15, and 5 dB). sLORETA distinguished those for 25 and 15 dB suppressing the subcortical originators. KF tracked only the evolution of cortical activity but mislocalized it. ConclusionsThe numerical results suggest that SKF inherits the estimation accuracy of sLORETA and traceability of KF while producing focal estimates for SEP originators. SignificanceSKF could help study time-evolving brain activities and localize landmarks with a deep contributor or when there is no prior knowledge of evolution.

Generation of state‐dependent ensemble perturbations based on time‐varying seawater density for GloSea5 initialization

AbstractIn this study, we developed a flow‐dependent oceanic initialization system for initializing the oceanic temperature and salinity in the Global Seasonal forecast system version 5 (GloSea5). Our algorithm overcomes the limitation of stationary perturbations for Ensemble Optimal Interpolation (EnOI) by spreading observed information along isopycnal lines to create three‐dimensional snapshot density states. The proposed algorithm, which we call state‐dependent ensemble‐based EnOI (SD‐EnOI), takes into account changes in the background error covariance over time without relying on ensemble model simulations. To evaluate the quality of the oceanic initial conditions (ICs) produced by SD‐EnOI, we compared them with those generated by the Global Ocean Data Assimilation and Prediction System version 1 (GODAPS1) operated by the Korea Meteorological Agency (KMA) throughout January 2017 to December 2017. Our findings show that the thermal construction of the SD‐EnOI ICs is more realistic than that of GODAPS1, particularly in the tropical Pacific region. The strong warm bias in sea surface temperature (SST) and the shallow mixed‐layer depth bias observed in the GODAPS1 ICs are not shown in SD‐EnOI. Due to the more realistic oceanic thermal structure present in the SD‐EnOI ICs, their use in retrospective forecast experiments resulted in a systematic reduction in climatological SST drift in the central‐eastern Pacific for forecasts up to four lead months compared to using GODAPS1 ICs. This demonstrates the significant impact of the initialization process on the quality of dynamical seasonal forecasts.

Coplane-constrained sparse depth sampling and local depth propagation for depth estimation

Depth estimation with sparse reference has emerged recently, and predicts depth map from a monocular image and a set of depth reference samples. Previous works randomly select reference samples by sensors, leading to severe depth bias as this sampling is independent to image semantic and neglects the unbalance of depth distribution in regions. This paper proposes a Coplane-Constrained sparse Depth (CCD) sampling to explore representative reference samples, and design a Local Depth Propagation (LDP) network for complete the sparse point cloud map. This can capture diverse depth information and diffuse the valid points to neighbors with geometry prior. Specifically, we first construct the surface normal map and detect coplane pixels by superpixel segmenting for sampling references, whose depth can be represented by that of superpixel centroid. Then, we introduce local depth propagation to obtain coarse-level depth map with geometric information, which dynamically diffuses the depth from the reference to neighbors based on local planar assumption. Further, we generate the fine-level depth map by devising a pixel-wise focal loss, which imposes the semantic and geometry calibration on pixels with low confidence in coarse-level prediction. Extensive experiments on public datasets demonstrate that our model outperforms SOTA depth estimation and completion methods.

Accurate isoform quantification by joint short- and long-read RNA-sequencing.

Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQT's potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.

Insights into Temporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations from the TXAR Catalog (2009–2016)

Abstract The Texas Seismological Network (TexNet) has enabled real-time monitoring of induced earthquakes since 2017. Before 2017, location uncertainties and temporal gaps in seismic data obscure correlations across Texas between seismicity and saltwater disposal or hydraulic fracturing. Depth biases also complicate linking anthropogenic stress changes to faults. We relocate 73 M 1.5+ earthquakes from the TXAR catalog (2009–2016) relative to the centroid of a calibrated core cluster consisting of 116 earthquakes from the TexNet catalog post-2020, in the southern Delaware basin south of the Grisham fault zone. Hypocentroidal decomposition relocation reduces spatial uncertainties of the TXAR events to <5 km and provides updated depths. The core cluster has uncertainties less than <300 m and depth constrained from near-source stations and S−P differential times. The majority of relocated TXAR events indicate the triggering of northwest-trending faults at a mean depth of 1 km below sea level, suggesting a causal connection with shallow saltwater disposal and consistency with post-2017 seismicity. Spatiotemporal patterns of pre-2017 seismicity and saltwater disposal highlight initial triggering via pore-pressure stress perturbations from nearby low-volume injections and later from southeastward pressure diffusion along permeable anisotropic conduits and fracture zones. The comparison between pre- and post-2017 seismicity indicates shallow fault reactivation through similar triggering mechanisms since 2009.

Enabling Advanced Snow Physics Within Land Surface Models Through an Interoperable Model‐Physics Coupling Framework

AbstractAccurate estimation of snow accumulation and melt is a critical part of decision‐making in snow‐dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah‐MP and Noah. The original LSMs and the coupled models (Noah‐MP‐Crocus and Noah‐Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference data sets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah‐Crocus configuration reducing the RMSE and bias of snow depth from 2% to 12% and 57% to 75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah‐MP. The Coupled Noah‐MP‐Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

How Well Do CMIP6 Models Simulate Salinity Barrier Layers in the North Indian Ocean?

Abstract Previous studies have hypothesized that climatologically thick salinity-stratified barrier layers (BLs) in the north Indian Ocean (NIO) influence the upper ocean heat budget, sea surface temperature (SST), and monsoons. Here, we investigate how state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) climate models simulate the NIO barrier layer thickness (BLT). CMIP6 models generally reproduce the BLT seasonal cycle and spatial distribution, but with shallow November–February (NDJF) biases in regions with thick observed BLT: the eastern equatorial Indian Ocean (EEIO), Bay of Bengal (BoB), and southeastern Arabian Sea (SEAS). We show that the intensity of the CMIP6 equatorial easterly wind bias controls the EEIO shallow isothermal layer depth (ILD) and BLT biases. It also controls the BoB shallow BLT bias, both through the propagation of the EEIO shallow ILD bias into the NIO coastal waveguide and because it is linked to the BoB dry and cold bias through the Bjerknes feedback, hence also controlling the mixed layer depth (MLD) deep bias there. Finally, the SEAS shallow BLT bias is due to a too-deep MLD, in response to subdued monsoonal currents around India, which do not bring enough BoB low-salinity water. The BL insulating effect mentioned in literature does not seem to dominate in CMIP6. Rather, the CMIP6 salinity-related deep MLD biases diminish the BoB cooling rate by winter upward surface heat fluxes, reducing cold SST biases. This suggests that salinity effects alleviate the easterly equatorial wind, cold, and dry BoB biases that develop through the positive Bjerknes feedback loop in CMIP6.

Assessment of thermocline depth bias in the Seychelles-Chagos Thermocline Ridge of the Southwestern Indian Ocean simulated by the CMIP6 models

The Seychelles-Chagos Thermocline Ridge (SCTR, 5°S-10°S, 50°E-80°E) is a unique open-ocean upwelling region in the southwestern Indian Ocean. Due to the negative wind stress curl between the equatorial westerlies and southeasterly trade winds, SCTR is known as a strong upwelling region with high biological productivity, providing a primary fishing zone for the surrounding countries. Given its importance in shaping the variability of the Indian Ocean climate by understanding the sea-air interaction and its dynamics, the simulation of SCTR is evaluated using outputs from the Coupled Model Intercomparison Project Phase Sixth (CMIP6). Compared to observations, 23 out of 27 CMIP6 models tend to simulate considerably deeper SCTR thermocline depth (defined as the 20°C isotherm depth (D20))– a common bias in climate models. The deep bias is related to the easterly wind bias in the equatorial to southern Indian Ocean, which is prominent in boreal summer and fall. This easterly wind bias produces a weak annual mean Ekman pumping, especially in the boreal fall. Throughout the year, the observed Ekman pumping is positive and is driven by two components: the curl term, is associated with the wind stress curl, leads to upwelling during boreal summer to fall; the beta term, is linked to planetary beta and zonal wind stress, contributes to downwelling during boreal spring to fall. However, the easterly wind bias in the CMIP6 increases both the positive curl and negative beta terms. The beta term bias offsets the curl term bias and reduces the upwelling velocity. Furthermore, the easterly wind bias is likely caused by the reduced east-west sea surface temperature (SST) difference associated with a pronounced warm bias in the western equatorial Indian Ocean, accompanied by the east-west mean sea level pressure gradient over the Indian Ocean. Furthermore, this study finds local wind-induced Ekman pumping to be a more dominant factor in thermocline depth bias than Rossby waves, despite CMIP6 models replicating Rossby wave propagation. This study highlights the importance of the beta term in the Ekman pumping simulation. Thus, reducing the boreal summer-to-fall easterly wind bias over the Indian Ocean region may improve the thermocline depth simulation over the SCTR region.

An outlier-robust Rao–Blackwellized particle filter for underwater terrain-aided navigation

Terrain-aided navigation (TAN) is one of the key techniques for autonomous navigation and positioning of AUVs. This paper develops a novel integrated navigation system that combines the inertial navigation system (INS) and TAN into one filter, in contrast to the conventional INS/TAN model. This method not only provides a position estimate for the carrier but also the INS error, which increases the filtering dimension significantly. Thus, the Rao-Blackwellized particle filter (RBPF) technique is introduced to address the “dimensional disaster” problem. A three-dimensional (3-D) model is established to estimate the horizontal position while compensating for the tidal inaccuracy by extending the tidal depth bias to the state space. Furthermore, the variable and complex underwater environment can potentially result in outlier interference in depth measurements, which significantly degrades the performance of the RBPF-based estimation algorithm. This paper thus presents a 3-D adaptive RBPF method based on the maximum correntropy criterion (MCC) to suppress the influence of measurement outliers on positioning precision. Simulations and experimental tests verify the effectiveness of the proposed algorithm. The results demonstrated that the 3-D model-based AMCCRBPF method can effectively improve robustness to outliers and provide an accurate estimate of the tidal depth bias.

Microbial and epidemiological factors in early detection of esophageal squamous cell carcinoma and precancerous lesions.

Microbial and epidemiological factors in early detection of esophageal squamous cell carcinoma and precancerous lesions.

Adaptive model for the water depth bias correction of bathymetric LiDAR point cloud data

The water depth bias of LiDAR point cloud data has to be corrected. The previous models, which used the fixed parameters for an entire water area, cannot efficiently correct the bias due to the different water environments. Therefore, this paper develops an adaptive model for the correction of the water depth bias. A coordinate system, in which the water depth is taken as the X-axis and the water depth bias is taken as the Y-axis, is defined. All of the sample points are normalized, and then projected into the defined coordinate system according to their water depths and water depth biases. Second, the scatter points are clustered into several different clusters using the developed subdivision algorithm. With the clusters, an entire water area is subdivided into several sub-regions. Finally, each sub-region is fitted using a model, which is used to correct the water depth bias. Experimental verification and comparison analysis are conducted in three different environments: an indoor tank, an outdoor pond and the Beihai sea. The experimental results demonstrate that the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) from the adaptive model are reduced by approximately 61% and 59%, respectively, relative to those from the traditional models. Therefore, it can be concluded that the proposed model can adapt to changes of the different water environments and achieve a higher accuracy for water depth bias correction than traditional methods do.

Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends

Abstract. Monitoring snow cover to infer climate change impacts is now feasible using Earth observation data together with reanalysis products derived from Earth system models and data assimilation. Temporal stability becomes essential when these products are used to monitor snow cover changes over time. While the temporal stability of satellite products can be altered when multiple sensors are combined and due to the degradation and orbital drifts in each sensor, the stability of reanalysis datasets can be compromised when new observations are assimilated into the model. This study evaluates the stability of some of the longest satellite-based and reanalysis products (ERA5, 1950–2020, ERA5-Land, 1950–2020, and the National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR), 1966–2020) by using 527 ground stations as reference data (1950–2020). Stability is assessed with the time series of the annual bias in snow depth and snow cover duration of the products at the different stations. Reanalysis datasets face a trade-off between accuracy and stability when assimilating new data to improve their estimations. The assimilation of new observations in ERA5 improved its accuracy significantly during the recent years (2005–2020) but introduced three negative step discontinuities in 1977–1980, 1991–1992, and 2003–2004. By contrast, ERA5-Land is more stable because it does not assimilate snow observations directly, but this leads to worse accuracy despite having a finer spatial resolution. The NOAA CDR showed a positive artificial trend from around 1992 to 2015 during fall and winter that could be related to changes to the availability of satellite data. The magnitude of most of these artificial trends and/or discontinuities is larger than actual snow cover trends and the stability requirements of the Global Climate Observing System (GCOS). The use of these products in seasons and regions where artificial trends and discontinuities appear should be avoided. The study also updates snow trends (1955–2015) over local sites in the Northern Hemisphere (NH), corroborating the retreat of snow cover, driven mainly by an earlier melt and recently by a later snow onset. In warmer regions such as Europe, snow cover decrease is coincident with a decreasing snow depth due to less snowfall, while in drier regions such as Russia, earlier snowmelt occurs despite increased maximum seasonal snow depth.

Contribution of the stereoscopic representation of motion-in-depth during visually guided feedback control.

Considerable studies have focused on the neural basis of visually guided tracking movement in the frontoparallel plane, whereas the neural process in real-world circumstances regarding the influence of binocular disparity and motion-in-depth (MID) perception is less understood. Although the role of stereoscopic versus monoscopic MID information has been extensively described for visual processing, its influence on top-down regulation for motor execution has not received much attention. Here, we orthogonally varied the visual representation (stereoscopic versus monoscopic) and motion direction (depth motion versus bias depth motion versus frontoparallel motion) during visually guided tracking movements, with simultaneous functional near-infrared spectroscopy recordings. Results show that the stereoscopic representation of MID could lead to more accurate movements, which was supported by specific neural activity pattern. More importantly, we extend prior evidence about the role of frontoparietal network in brain-behavior relationship, showing that occipital area, more specifically, visual area V2/V3 was also robustly involved in the association. Furthermore, by using the stereoscopic representation of MID, it is plausible to detect robust brain-behavior relationship even with small sample size at low executive task demand. Taken together, these findings highlight the importance of the stereoscopic representation of MID for investigating neural correlates of visually guided feedback control.

Comparison between Conventional Modality Versus Cone-Beam Computer Tomography on the Assessment of Vertical Furcation in Molars.

This study aimed to assess the accuracy of diagnosis of vertical furcation subclass in molars using periapical radiographs (PAs) and clinical chartings compared against cone-beam computer tomography (CBCT) as the gold standard. The protocol involved examiners with different levels of experience. This retrospective radiographic study retrieved 40 molar teeth with full periodontal chartings, PAs, and CBCT records. Fifteen examiners with different levels of experience evaluated the PAs and periodontal chartings to assess the vertical depth of furcation and, thus, the vertical subclassification. CBCT was used as the gold standard for comparison. The accuracy of vertical furcal depth measured was assessed together with the accuracy of vertical subclassification assignment. The reliability of the conventional diagnostic modality among the examiners was also evaluated. A linear mixed model adjusted for the CBCT vertical furcal depth measurement was constructed to determine if tooth position, horizontal furcation distribution, and examiner experience level affect the bias in the vertical depth of furcation measurement. The reliability of the conventional periodontal diagnostic method in measuring vertical furcal depth was found to be fair, while vertical subclass assignment was moderate. Significantly better reliability during subclass assignment was found with mandibular molars (p < 0.001) and in maxillary molars with isolated buccal class II furcation. Within the study’s limitations, conventional periodontal diagnostics based on periapical radiographs and clinical periodontal chartings appear to be in poor to fair agreement with CBCT (gold standard) when measuring the vertical depth of furcation. Examiners with the least experience were more prone to bias when estimating the vertical furcal depth.

Retrieval of Suspended Sediment Concentration from Bathymetric Bias of Airborne LiDAR.

In addition to depth measurements, airborne LiDAR bathymetry (ALB) has shown usefulness in suspended sediment concentration (SSC) inversion. However, SSC retrieval using ALB based on waveform decomposition or near-water-surface penetration by green lasers requires access to full-waveform data or infrared laser data, which are not always available for users. Thus, in this study we propose a new SSC inversion method based on the depth bias of ALB. Artificial neural networks were used to build an empirical inversion model by connecting the depth bias and SSC. The proposed method was verified using an ALB dataset collected through Optech coastal zone mapping and imaging LiDAR systems. The results showed that the mean square error of the predicted SSC based on the empirical model of ALB depth bias was less than 2.564 mg/L in the experimental area. The proposed method was compared with the waveform decomposition and regression methods. The advantages and limits of the proposed method were analyzed and summarized. The proposed method can effectively retrieve SSC and only requires ALB-derived and sonar-derived water bottom points, eliminating the dependence on the use of green full-waveforms and infrared lasers. This study provides an alternative means of conducting SSC inversion using ALB.

Investigation of Effects of Near‐Surface Complexities on Measurement of Mantle Discontinuity Using SS and Its Precursors

AbstractSeismic observations of the mantle discontinuities at depths of 410 and 660 km provide crucial information to constrain the thermal and compositional states and further shape our understanding of mantle dynamics. The topography and impedance contrast of the mantle discontinuities, at both global and regional scales, have been imaged through differential travel times and amplitude ratios between SS and its precursors. However, a relatively simple reference phase (SS) is often assumed, which may overlook potential bias caused by near‐surface complexities. To assess these effects, we developed an algorithm to simulate the waveforms of SS and its precursors efficiently based on the propagation matrix method. We found that the crustal thickening increases the differential travel times but does not change the amplitude ratios much, while the sedimentary layer significantly affects both the differential travel times and amplitude ratios. The global anomalies of differential travel times and amplitude ratios are mapped incorporating earth models from CRUST1.0 and PREM. The time shift can reach ∼9 s, which will bring a depth bias of mantle discontinuity of more than 20 km, while the amplitude ratio anomaly is up to ∼40%, which will make the impedance contrast of the mantle discontinuity overestimated. The near‐surface complexities cannot be ignored in the investigation of mantle discontinuities with SS precursors, especially when the bounce points located in the regions with sediment.

P-321 Culturomics-generated vaginal and endometrial microbiome profiles in subfertile patients

Abstract Study question In subfertile patients, do the endometrial and vaginal microbiome correspond to one another when applying high-throughput culturing techniques? Summary answer High interpatient variation was observed in the number of different isolated species. Furthermore, there was a diverse patient-specific concordance between vaginal and endometrial microbiota profiles. What is known already The microbiome of the female reproductive tract is potentially implicated in fertility and in vitro fertilization success rates. Of particular interest is Lactobacillus iners, an ineffective lactic acid producer, that is related with vaginal dysbiosis and adverse reproductive health outcomes. Previous metagenomics-based studies have highlighted that the endometrial and vaginal microbiome can be quite distinct in the same patient. Here, we investigated the applicability of a novel methodology, i.e. culturomics, in studying the vaginal-uterine microbiome. In contrast to metagenomics, a culturomics-generated microbiome profile is not influenced by depth bias or DNA contamination from extraction kits and reagents. Study design, size, duration In this pilot study, paired samples of vaginal swabs and endometrial biopsies were obtained from six subfertile women undergoing a diagnostic hysteroscopy. Vaginal swabs were taken prior to the hysteroscopy, while biopsies were harvested afterwards, with a pipelle technique. Participants/materials, setting, methods A plethora of enrichment broths and (non)selective agars were processed in an aerobic and anaerobic manner to ensure exhaustive culturomics results. Agars were incubated until 30 days post-inoculation and processed applying the Copan WASPLab® system. Bacterial colonies were identified using the Brüker MALDITOF MS Biotyper® system. Main results and the role of chance A total of 3218 colonies were detected using the MALDI Biotyper® system, enabling the identification of 79 different species. High inter-patient variability was observed in the number of detected isolated species (11–49). Also, the concordance between vaginal and endometrial microbiota differed from 4 to 48 %. Interestingly, the percentage of vagino-endometrial similarity and microbial diversity seemed to be associated with the presence of certain Lactobacillus species. We observed that the less diverse microbiome profiles (11-27 species) were dominated by eubiosis-related strains such as L. crispatus, L. jensenii, L. gasseri, L. salivarius and L. vaginalis, while the more diverse microbiome profile (49 species) was colonized by de dysbiosis-associated L. iners. Limitations, reasons for caution Only a small number of patients was included as this was a proof-of-principle study investigating the applicability of culturomics on vaginal swabs/endometrial biopsies. Wider implications of the findings Culturomics-acquired microbiome profiles could have a place in personalized fertility medicine with potential implications in therapy. The true value of our findings and their relationship with endometrial pathologies and IVF success rates needs to be confirmed in larger series. Trial registration number 143201420916