dataesr/scientific-paragraphs-categorization huggingface dataset:
remobilized as stationary fluid muds, and/or resuspended and laterally transported as dense suspensions  11, 12. Particle reworking and remobilization can result in loss of chronological information  [13] [14] [15]  and is highly heterogeneous spatially and temporally. Thus, the distribution and thickness of the SML are important to improve global flux estimates of dissolved and particulate constituents of key biogeochemical cycles in Earth System Models  16. Neural networks have emerged as a powerful tool to resolve complex spatial and temporal patterns that are common in large datasets in the geosciences  17, 18. Some examples of neural networks applications in the earth sciences include: hydrology, including flooding forecasts, and water quality modeling  19, 20  ; geophysics/ geomorphology, including earthquake predictions, and simulating land-use change  21, 22  ; and atmospheric sciences, by modeling cloud formation and temperatures  23, 24.
Recently, machine learning (e. g. , K-nearest neighbor, random forest and neural networks) has been applied to resolve important questions in oceanography, including the global distribution of seafloor total organic carbon, benthic properties, sediment porosity/density and sediment accumulation rates  [25] [26] [27] [28] [29] [30]. While the SML has been assessed on local and regional scales  12, 15  , global-scale datasets remain sparse. A tracer-identified surface mixeddepth global mean value of 9. 8 ± 4. 5 cm was obtained in the 1990's  31, 32  and later updated to 5. 75 ± 5. 67 cm  33. Nevertheless, there remains a significant gap in our knowledge of the distribution patterns of SMLs in the global coastal ocean. Hence, we posit that a more accurate estimate of SMLs is needed for better incorporation into global biogeochemical ocean models.
Here, we define the SML as the sediment thickness captured by 210 Pb ex profiles that has been reworked over time scales of months  34, 35. We estimated SML thicknesses in the global coastal ocean by compiling data from 742 globally distributed sediment cores. First, we evaluated the spatial patterns and drivers of sediment mixing. We then used a neural network model to estimate the thickness of SMLs in the entire global ocean shallower than 200 m, linked it to organic carbon burial in marine sediments. This global assessment of coastal SMLs improves our ability to estimate and interpret the burial capacity and remineralization of organic matter and nutrients in ocean sediments.
Furthermore, it is possible that an exponential decrease in the upper section of a Type I profile could originate from slow/deep bioturbation. This kind of reworking is indistinguishable from profiles originating from sediment accumulation and/or a combination of sediment accumulation and bioturbation. We compiled 1093 published 210 Pb ex profiles from the global continental shelf (see Methods and Supplementary Data 1). The geographic distribution of 210 Pb ex profile types of all 1093 sites (Fig. 1b, c ) revealed a dominance of Type III, followed by Type I. Studies that used gravity cores to define SML were not applied in our modeling (except for the Amazon Shelf), because gravity cores are well-known to create a bow wave artifact that disrupts nearsurface sediments  38. However, the significant greater thicknesses of the SML in the Amazon Estuary made it difficult to obtain a complete 210 Pb ex profile when using short box-cores (<1 m).
So, gravitycore data were included when analyzing SMLs in Amazon Shelf sediments. Overall, 742 cores had complete 210 Pb ex profiles and recognizable SMLs to be used in our model. The overall thickness of SMLs in the coastal ocean proved to be very heterogeneously distributed; ~47% of the SMLs were >5 cm, 36% had SMLs ranging from 5-20 cm, and only 3% had SMLs thicker than 30 cm (Fig. 2a ). Thick SMLs primarily occurred in large-river delta-front estuaries (LDEs). Deep mixing could be viewed as inconsistent with the notion that these sediments are potential "recorders" of past natural and anthropogenic changes  39. However, thick SMLs demonstrate the heterogeneity of depositional environments in LDEs, and the need for careful site selection of sampling locations for paleo-reconstruction work, which has proven to be successful in some LDEs (e.
Data source The Web of Science (Thomson Reuters, New York, NY), Google Scholar, and Bai Du Scholar were utilized to search the literature using the following key words or phrases: 210 Pb with coastal and/or estuary; and reworked muds and/or mixed layer. The data repository created by Solan et al. 71  provided an excellent base to show relationships between benthic faunal community distribution and sediment characteristics, and we also obtained additional 210 Pb ex profiles from references therein. We compiled 238 studies with 1093 sites having 210 Pb ex profiles distributed globally. Data source references are presented in the Supplementary Data 1. About 62% of the sites were located on continental shelves between 0 ~200 m water depth, and the remaining locations were in intertidal mud/sand flats, wetlands, and the deep ocean.
Environmental factors, physical dynamics and biological activities were collected for each site and presented in Supplementary Data 1, including mean annual precipitation, water depth, total suspended matter, tidal range, relative sea level rise rate, tropical cyclone frequency, bottom stress, primary productivity, river discharge, river sediment load and sediment accumulation rate, to explore for drivers of the SML in the coastal zone. Tidal ranges were extracted from a global tidal range dataset  72, 73  and water depths from the ocean bathymetry database (https:// www. ngdc. noaa. gov/mgg/global/global. html). Tropical cyclone effects and hazard risks were based on the Global Cyclone Hazard Frequency and Distribution, v1 dataset (1980-2000), and assessed on a 2. 5 min' global grid. More than 1600 storm tracks were assembled and modeled, through the period January 1st, 1980, to December 31st, 2000, for the Atlantic, Pacific, and Indian Oceans, at UNEP/GRID-Geneva PreView (https://www.
ldeo. columbia. edu/chrr/ ). Sediment types were collected from seafloor lithology in the GplatesPortal  74. We added about 7800 points in areas with poor data coverage and reproduced a new digital map of seafloor lithology (Supplementary Fig. 6 ). We simulated SML only for sites with finegrained sediment (silt and clay). In coastal wetlands, sediment accumulation rates were obtained from a recent global assessment of saltmarshes and mangroves  75. Data of mean annual precipitation were acquired from world climate data, European Climate Assessment & Dataset  76. For relative sea level rise rates, data were collected from the Permanent Service for Mean Sea Level database  77. Total suspended matter was derived from Medium Resolution Imaging Spectrometer satellite data, processed in the framework of the GlobColour project  73.
The dataset of river discharge comes from "Dai and Trenberth Global River Flow and Continental Discharge Dataset"  78  , which were interpolated according to the river discharge and distance to obtain the river discharge influence at each data point. The data of bottom currents are from TPXO global tide models  79. The sediment load of global large rivers is from the dataset by Milliman and Katherine 80. Satellite-observed monthly global climatology sea surface chlorophyll-a concentration and primary production with 4 km resolution were downloaded from Copernicus Marine Service (https://resources. marine. copernicus. eu/). Annual average chlorophyll-a concentration and primary production were originally calculated using these climatology data, which were then linearly interpolated for each location.
Acknowledgements We thank to  Wenyuan Li  for her assistance during the neural network building. We thank to  Dr. Xiaoyong Duan ,  Limin Hu  and  Guangquan Chen  for providing sediment information. Dr. Peter T. Harris  and  Miles Macmillan-Lawler  are acknowledged for providing the global morphotypes data. This work was supported by  Natural Science Foundation of China (NSFC  grant  42130410  to Z. Y. ,  41876075  to B. X. , and  U1906210  to Q. Y. ).
Based on these findings the consensus Env trimers ConM SOSIP. v7 and Con-SOSL. UFO. 664 have proceeded into the clinical testing stage. Early phase clinical trials in the United Kingdom and the Netherlands (ClinicalTrials. gov Identifiers: NCT03816137, NCT03961438, and NCT04046978) are currently underway, of which the first two (partially) overlap with the work described here in terms of immunogens, immunization schemes and adjuvants used. Gaining detailed information on the precise nature of B cell responses against these immunogens, including their immunodominant epitope(s) is valuable for predicting and evaluating the outcome of clinical studies and for accelerating iterative improvement of consensus Env immunogens. In the study described here, we isolated monoclonal antibodies (mAbs) from NHPs after three priming immunizations with the soluble ConM SOSIP.
24%). We next amplified the B cell receptor (BCR) sequences from the sorted antigen-specific B cells. In total, we obtained 148 antibody heavy chain (HC)/light chain (LC) pairs that were used to generate mAbs. Based on screening for antigen binding by enzyme-linked immunosorbent assay (ELISA), we selected 40 mAbs with high reactivity to ConM SOSIP. v7 and/or ConSOSL. UFO. 664 proteins, 18 from the first and 22 from second time point, for further characterization (Fig. 2b ). To support the analyses of antigenspecific BCR sequences, we generated individualized HC germline V-gene databases for each animal by deep sequencing preimmunization PBMCs and applying IgDiscover  31. This enabled accurate gene assignment, determination of somatic hypermutation (SHM) levels and allowed us to make comparisons to the naive repertoire for each individual animal.
2d ). Using IgDiscover analysis we identified two clonal Ab families from animals MCM04 and MCM05 (assigned lineages CM04A and CM05A, respectively), consisting of three mAbs each, including ones from both time points (Supplementary Table  1 ). The SHM in these families increased from 3. 7% (CM04A1) and 9. 4%/8. 1% (CM05A1/CM05A2) at week 25 to 6. 8% (CM04A2/CM04A3) and 13. 4% (CM05A3) at week 71 (Fig. 2d ). Combined, these data illustrate continued affinity maturation facilitated by the extended time interval and the ConSOSL. UFO. 664 booster immunizations. Overall, the antigen-specific mAbs isolated from all NHPs combined had a mean HC complementarity determining region 3 (CDRH3) length of 15. 9 amino acids (aa) (range: 8. 0-25. 0), with no significant differences between the two time points (Fig.
We report that roughly 20% of the produced mAbs are base binders. In comparison, other studies have reported the proportions of base binders to be up to 66% of isolated mAbs  25, 26. This shows that negative selection in the initial stages of mAb generation help exclude undesired mAbs. Nonetheless, it remains relevant to know the proportion of base binding mAbs as a fraction of the total Ab response. Another way to determine this is to visualize all the dominant antibody targets by EMPEM analysis. While the rationale behind the use of consensus immunogens was to minimize strain-specific responses and improve antibody breadth, a new yet artificial strain-specific immunodominant epitope was unintentionally generated because of several reasons. Firstly, when comparing the V1V2 sequence to global sequences found in the Los Alamos National Laboratory (LANL) HIV databases (www.
It remains to be seen whether the V1V2V3 region is also predominantly targeted by NAbs in humans, as data from early-stage clinical trials become available (ClinicalTrials. gov Identifiers: NCT03816137, NCT03961438, and NCT04046978). If so, the data assembled here will help to interpret these responses and accelerate the use and improvement of consensus-based proteins. Such improved consensus-based Env proteins could for example be used to boost immune responses as so called 'polishing' immunogens. These immunogens are designed to further shape the Ab response to recognize bNAb epitopes with high affinity to diverse HIV Env proteins, following priming with a germline targeting protein such as BG505 SOSIP. GT1. 1  39  , aimed to activate specific B cell germline precursors of bNAbs. Like ConM SOSIP.
Isolated mAbs were named according to animal identification number and lineage. Sequences were uploaded to GenBank. mAbs were biotinylated for competition ELISA. Biotinylation was done using the EZ-Link™ Sulfo-NHS-LC-Biotinylation Kit (Thermo Fisher Scientific) following the manufacturer's protocol. For NS-EM analysis, polyhistidine (His)-tagged Fabs were produced by Gibson cloning Ab heavy chain PCR inserts into a digested Fab expression vector. Gibson reaction products were transformed into competent XL-1 blue cells (Agilent) for mini-or midiprep DNA isolation using the Macherey-Nagel plasmid DNA isolation kit. His-tagged Fabs were recombinantly expressed and secreted as a soluble protein in HEK293F cells. The supernatant was concentrated and loaded onto a Ni-NTA affinity column, and the Fabs were eluted using an imidazole gradient.
The Leginon automated interface  48  was used to acquire the data. Data processing was performed as follows. Particles were autopicked and extracted using Appion data processing suite  49. 2D classification was performed using Relion v3. 0 51 into 100 classes, and classes containing Fab-SOSIP complexes were selected for 3D analysis. Particles from the selected classes were 3D sorted into 40 classes using Relion v3. 0, with a low-resolution model of unliganded HIV Env as a reference. The 3D maps were visualized using UCSF Chimera  50. Particles from similar-looking classes were then selected and reclassified using Relion v3. 0. The final 3D maps were visualized and segmented using UCSF Chimera  50  and Segger  52  , respectively. Representative reconstructions (all specificities for a particular animal and time point) have been deposited to the Electron Microscopy Data Bank (EMDB) (see "Data availability") and summarized in Supplementary Table  4.
The MS data was searched using the Protein Metrics 305 N-glycan library with sulfated glycans added manually. The relative amounts of each glycan at each site as well as the unoccupied proportion were determined by comparing the extracted chromatographic areas for different glycotypes with an identical peptide sequence. All charge states for a single glycopeptide were summed. The precursor mass tolerance was set at 4 and 10 ppm for fragments. A 1% false discovery rate (FDR) was applied. The relative amounts of each glycan at each site as well as the unoccupied proportion were determined by comparing the extracted ion chromatographic areas for different glycopeptides with an identical peptide sequence. Glycans were categorized according to the composition detected. HexNAc(2)Hex(10+) was defined as M9Glc, HexNAc(2)Hex(9-5) was classified as M9 to M3.
Any of these structures containing a fucose were categorized as FM (fucosylated mannose). HexNAc(3) Hex(5-6)X was classified as Hybrid with HexNAc(3)Hex(5-6)Fuc(1)X classified as Fhybrid. Complex-type glycans were classified according to the number of HexNAc subunits and the presence or absence of fucosylation. These compositions were further grouped into high mannose (oligomannose and hybrid-type), complex and unoccupied. Two analytical repeats were performed with this method Site-specific analysis of low abundance N-glycan sites using mass spectrometry To obtain data for sites that frequently present low intensity glycopeptide the glycans present on the glycopeptides were homogenized to boost the intensity of these peptides. This analysis loses fine processing information but enables the ratio of oligomannose: complex: unoccupied to be determined.
ACKNOWLEDGEMENTS This work was supported by the  European Union 's  Horizon 2020 research and innovation program EAVI2020  under grant agreement no. 681137 , as well as the  HIV Vaccine Research and Design (HIVRAD) program  ( P01 AI110657 ) (A. B. W. and R. W. S). Furthermore, this work was supported, in whole or in part, by the  Collaboration for AIDS Vaccine Discovery (CAVD)  grants  OPP1115782 ,  OPP1132237 ,  INV-002022 , funded by the  Bill and Melinda Gates Foundation. Under the grant conditions of the Foundation, a Creative Commons Attribution 4. 0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. R. W. S. is a recipient of a  Vici fellowship  from the  Netherlands Organization for Scientific Research (NWO). M.
J. v. G. is supported by an amfAR  Mathilde Krim Fellowships in Basic Biomedical Research  grant number  109514-61-RKVA  and the 2017 AMC Fellowship. Al. A. is supported by  amfAR Mathilde Krim Fellowship in Biomedical Research  no. 110182-69-RKVA. The neutralization assays performed at Duke University were supported by  NIAID-NIH  ( HHSN272201800004C ). The  Infectious Disease Models and Innovative Therapies research infrastructure (IDMIT)  is supported by the " Program Investissements d'Avenir" (PIA) , managed by the  ANR  under references  ANR-11-INBS-0008. The glycopeptide analysis was supported by the  CAVD  grant  INV-008352/OPP1153692  funded by the  Bill and Melinda Gates Foundation  (M. C. ). We are grateful to the staff of IDMIT, particularly to  C. Joubert ,  R. Ho Tsong Fang ,  B.
DATA AVAILABILITY Full antibody sequences are available upon request to the corresponding author. The ConM SOSPIP. v7/ ConSOSL. UFO. 664-specific BCR sequences are deposited to GenBank with accession numbers OM160776-OM160815. Negative-stain EM 3D reconstructions have been deposited to the Electron Microscopy Data Bank (EMDB) with accession codes EMD-14888-14908, summarized in Supplementary Table  4.
Materials and methods To obtain a database of FHR recordings that allow for HRV analysis, an experimental study involving 21 fetal sheep was set up within the University Hospital Department of Experimental Research at the Lille Faculty of Medicine from 2017 to 2019. From these 21 fetal sheep, a total of 170 measurements of FHR coupled with pH were obtained. Because of signal quality problems only 130 measurements were studied. Ethics. The anesthetic, surgical, and experimentation protocols were consistent with recommendations from the Ministry of Higher Education, Research, and Innovation, with the study being approved by the Animal Experimentation Ethics Committee of Nord-Pas de Calais, France (CEEA #2016121312148878). This manuscript is compliant with the ARRIVE guidelines for reported animal research  13.
Experimental protocol. The experimental protocol began 72 h after the surgery. Upon arrival of the ewe in the experimental room, an acclimatization period of one hour was performed in the presence of the primary investigators before start the protocol. During this phase, called baseline phase, fetal arterial catheters and an intra-amniotic catheter were connected via pressure sensors (Pressure Monitoring Kit1, Baxter, France) to a multiparametric monitor (Monitor Merlin, Hewlett Packard, Palo Alto, CA, USA). The arterial blood pressure (ABP) was measured from the blood pressure phasic signal and referenced to the intra-amniotic pressure (IAP). ECG electrodes were connected to the multiparametric monitor. Hemodynamic data were recorded continuously throughout the experiment. When the ewe was acclimated, after a 1-h baseline period, the protocol started: repetitive umbilical cord occlusions (UCOs) were performed by injecting an isotonic solution into the occluder to obtain a total occlusion for 1 min (min).
92 mg + tetracaine 4. 39 mg, MSD, France). Measurement of gasometric and hemodynamic parameters. Fetal blood gas measurements were performed on arterial blood samples taken from one of the arterial catheters during the baseline phase and after the end of the last UCO in each of the phases A, B, and C. Gasometric parameters (pH, PaO 2 , PCO 2 , base excess (BE), plasma lactate) were then measured with the iSTAT1 blood analyzer (iSTAT1 System, Abbott Point of Care Inc, Princeton, NJ, USA) using CG4 + cartridges. All the hemodynamic data were recorded on computer using the Physiotrace ® software package  18. The FHR, mean ABP (MAP), and mean IAP were read from the multiparametric scope at the same time. The MAP was corrected by subtracting the IAP (corrected MAP = observed MAP -observed mean IAP).
ECG signals were recorded by a Physiotrace data acquisition card (Physiotrace, Estaris Monitoring, Lille, France). Analysis of HRV parameters. All analyses of the ECG and the RR series (space between two R waves of the QRS complex of the fetal ECG) were performed using the FSIrelecture© software package, which enables automatic data collection via cursor movements on the FHR (see Fig. 1 -Data supplementary). These analyses were performed at the end of the baseline phase and at each of the stable periods (occurring every 20 min during the occlusion periods). The various parameter values recorded were: • Time analysis: HRV time domain analyses included the root mean square value of successive differences (RMSSD) for the RR intervals and the standard deviation of the RR intervals (SDNN), computed over 540 RR intervals which correspond to approximately 3 min, considering a basal FHR of 180 bpm).
The final predictive model was obtained by performing a logistic regression including the variables retained. We computed odds ratios (OR) and their 95% confidence intervals (CI). The predictive ability of the model was assessed by the area under the ROC (receiver operating characteristic) curve with its 95% confidence interval. We also performed a decision-tree analysis using CHAID algorithm (Sipina software). Decision tree is a non linear (stepwise forward) multivariable analysis to predict a categorical outcome (in this study the acidosis status) from a set of continuous predictors. At the first step, the continuous variables are transformed into binary variables with the threshold maximizing the chi-square statistic. Then the most predictive variable according to the chi-square statistic is selected among the whole set of predictors.
The process is repeated in each sub-population until no significant variable at 5% level for the chi-square is found. In parallel, we also developed a decision tree using the CART algorithm (package Rpart, R Software). The performances of the different models were assessed by calculating the sensitivities (Se), specificities (Sp), positive and negative predictive values (PPV and NPV). The analyses were conducted with the complete case sample. Statistical testing was conducted at the two-tailed α-level of 0. 05. Data were analyzed using the SAS software version 9. 4 (SAS Institute, Cary, NC), the R software (R Core Team, 2019, version 3. 6. 1) and the Sipina software.
Several authors studied ML systems for acidosis prediction. Typically, they started with the CTG FHR recordings, analyzed them via various ML and deep-learning systems, and compared them against visual analysis with respect to the prediction of pathological FHR or pH at birth  [29] [30] [31] [32] [33] [34] [35]. For example, Zhao et al. studied several FHR features based on the 2015 FIGO recommendations and integrated them into various ML algorithms to predict acidosis at birth  3. The algorithms included decision trees, support vector machines (SVMs), and AdaBoost  31. They analyzed 552 intrapartum FHR recordings obtained via CTG from 2009 to 2012, which were classified into two groups according to neonatal arterial pH (Czech Republic database, Brno University Hospital)  36. In the pH < 7.