Global navigation satellite systems (GNSS) allow estimating total electron content (TEC). However, it is still a problem to calculate absolute ionosphere parameters from GNSS data: negative TEC values could appear, and most of existing algorithms does not enable to estimate TEC spatial gradients and TEC time derivatives. We developed an algorithm to recover the absolute non-negative vertical and slant TEC, its derivatives and its gradients, as well as the GNSS equipment differential code biases (DCBs) by using the Taylor series expansion and bounded-variable least-squares. We termed this algorithm TuRBOTEC. Bounded-variable least-squares fitting ensures non-negative values of both slant TEC and vertical TEC. The second order Taylor series expansion could provide a relevant TEC spatial gradients and TEC time derivatives. The technique validation was performed by using independent experimental data over 2014 and the IRI-2012 and IRI-plas models. As a TEC source we used Madrigal maps, CODE (the Center for Orbit Determination in Europe) global ionosphere maps (GIM), the IONOLAB software, and the SEEMALA-TEC software developed by Dr. Seemala. For the Asian mid-latitudes TuRBOTEC results agree with the GIM and IONOLAB data (root-mean-square was < 3 TECU), but they disagree with the SEEMALA-TEC and Madrigal data (root-mean-square was >10 TECU). About 9% of vertical TECs from the TuRBOTEC estimates exceed (by more than 1 TECU) those from the same algorithm but without constraints. The analysis of TEC spatial gradients showed that as far as 10–15° on latitude, TEC estimation error exceeds 10 TECU. Longitudinal gradients produce smaller error for the same distance. Experimental GLObal Navigation Satellite System (GLONASS) DCB from TuRBOTEC and CODE peaked 15 TECU difference, while GPS DCB agrees. Slant TEC series indicate that the TuRBOTEC data for GLONASS are physically more plausible. Currently, the global navigation satellite systems (GNSS) enable the study of the ionosphere at any spot on the globe. Such studies are based on dual-frequency phase and pseudorange measurements of the total electron content (TEC) . Since tPseudorange measurements are thought to be absolute, however, they are noisy. For this reason, one often uses pseudorange and phase measurements jointly to eliminate the phase ambiguity. Herewith, there is a bias related to a different time of the signal passage through the channels of a satellite and a receiver. In literature, this error is referred to as differential code biases (DCBs). This error is known to vary systematically, and it may have irregular variations . MylnikoAn important goal is to obtain absolute characteristics reflecting ionospheric conditions. A good way is to have an electron density profile that an incoherent scatter radar or an ionosonde can provide ,20,21,22Using the GNSS data can also provide estimates for the electron density profile. This information may be obtained by using 4D spatio-temporal ionospheric tomography suggested by Mitchell and Spencer . GNSS-toWhen using the data from sparse networks of stations, a more promising way is to build TEC maps. The International GNSS Service (IGS) Working Group on Ionosphere  providesCurrently, there are several methods to estimate the vertical TEC. These methods are based on various TEC models ,30. One Different techniques for TEC estimation do not solve the problem of negative TEC values, especially negative slant TEC on some line of sights. Start and Parker  and Watehttp://www.ionolab.org/) [We use the GNSS data from the IGS network  as the Tab.org/)  and softab.org/) , as wellab.org/)  and CODEab.org/) . It is fThe IONOLAB software is based on a single-station solution . The sofSeemala developed a software for TEC calculations (referred to as SEEMALA-TEC)  based onMadrigal TEC is also based on projection of slant TEC to vertical TEC, but for different cells (1° × 1°) on the map . They usGlobal ionosphere maps are used for a long time to monitor and model the ionosphere . We usedMI is the model (expected) value of the recorded slant TEC, Is is the slant TEC along the line of sight without a differential code bias, VI is the vertical TEC estimate at a point corresponds with the measurement (ionospheric pierce point), S is the function that converts the slant TEC into the vertical one (mapping function), and DCBI is an error related to the DCB of the satellite and the receiver.To obtain absolute TEC, one estimates the preset measurement model parameters by using experimental data. As a rule, one uses the following model of TEC:S in Equation (1) is a constant. The corresponding error can be regarded as a TEC offset [For geostationary Earth orbit (GEO) satellites it is difficult to estimate DCB (or similar error in single frequency data) from (1), because C offset . HoweverC offset .V=IV I, where 0ϕ, 0l, and 0t are the station latitude, longitude, and the time, for which the calculation is performed. This implies that all the TEC measurements were supposed to be performed over the station. However, such an approach does not allow one to obtain spatial gradients. Such an approximation is correct in a limited number of cases when the spatial gradients and the time derivative can be neglected. In the most studies, a spatial-temporal expansion of the VI function is used. The spherical harmonic expansion is often used. Komjathy et al. [VI Taylor series expansion in (1) only on space. In our research, we expand the vertical TEC function VI  into the Taylor series on space and time:ϕ, Δl are the difference between latitude, longitude of the ionospheric pierce point and the point of station , Δt is the difference between time of measurement and 0t.Ma and Maruyama  used de y et al.  increasey et al.  used they et al. , the Tayy et al.  mentione(1)PI and phase φI measurements. For the analysis, we use the data with elevations greater than 10°.Calculating TEC based on the pseudorange (2)Dividing the data into continuous samples.(3)Detecting and eliminating outliers and cycle slips in the TEC data  Figure .(4)Eliminating the phase measurement ambiguity . At this state we obtain experimental slant TEC ExpI.(5)Estimating DCBs by a simple measurement model and determining the model parameters based on minimizing the model data root-mean-square deviation.We developed the following algorithm to estimate the vertical TEC, the TEC gradients, the time derivative, and DCBs. The algorithm involves:m = 0, n = 0, k = 0 in Equation (2)), indeed, it is necessary to substantiate the selection of the expansion order in greater detail. We performed an analysis by using the IRI-2012 model. The TEC slant values, corresponding to real observed elevations and azimuths, as well as the vertical TEC, the time derivative, and the spatial gradients were modeled by latitude and by longitude. Then, we recovered the values of vertical TEC (IV) from slant measurements by using expansions to the specified order of Taylor-expansion.Although in some research the zero-order expansion of (2) is used ). Next, TEC was recovered by using the first-order (leaving terms with n + m + k ≤ 1 in (2)) and second-order (leaving terms with n + m + k ≤ 2 in (2)) expansions.At the first step, this recovery was performed based on the zero-order Taylor expansion (2)  is the latitude (longitude) difference between the ionospheric point coordinate ϕ(l) and that of the station 0ϕ(0l), Δt is the difference between the measurement time t and the time 0t for which the calculation is performed. Further, ϕ = ∂IV/∂ϕ, Gl = ∂IV/∂lG, q_ϕ = ∂2IV/∂ϕ2G, and q_l = ∂2IV/∂l2G are the linear and quadratic spatial TEC gradients, and t = ∂IV/∂tG and q_t = ∂2IV/∂t2G are the first and second time derivatives. Equation (3) represents the second-order Taylor series expsion of (1). We neglect the mixed derivatives.Meanwhile, using the second order provides a sufficient accuracy to be able to neglect higher orders. Therefore, we use the expansion α is introduced to more correctly account for the ionospheric layer height [E =R 6371 km is the Earth radius, max =h 450 km is the ionospheric point height, and α is the correcting coefficient.In the literature different mapping functions are used. Selecting the most correct one is a challenge. In most papers, one uses the one-layer approximation of the ionosphere as a thin layer. This approach was suggested by Klobuchar . Hernándr height :(4)Sji=[SI, on the elevation when converting S = S∙IVI for different values α. Simulation used the IRI-2012. By its physical implication, the coefficient α adjusts a sharp non-physical TEC growth at low elevations, less than 30°.α should be latitude-dependent. Also, the α parameter should affect the ionosphere disturbance level that may dramatically vary the ionization global distribution [α values for several points on the earth surface. It is worth noting that due to the ionosphere peculiarities, the coefficient ribution . We estiU = ∑kU (5) for the set of selected instant kt, for which we estimate the parameters. For computation, we represent (5) in the form (7):ExpI is the experimental phase TEC measurements with the eliminated phase ambiguity obtained after Stage 4 of the algorithm, Θ is the Heaviside step function, ith instant of measurement for the jth satellite, kt, for which calculating is performed, and Δt = 1 h is the maximal time difference, for which the data are still used for analysis (kt-estimate). The 1/S factor in Equation (6) causes the measurements at high elevations to produce the greatest contribution. For each time instant k, we have 7∙J + N variables  in Equation (5), where J is the number of instants over the investigated interval, for which calculation is performed, and N is the number of the satellites observed.We obtain the set of equations by minimizing the functional C. So we introduce the next boundaries:C is a non-negative value of minimal TEC, which can be observed in principle. We chose C = 0.5 TECU.After that, minimization should be applied, which is based on the least square technique. A typical problem for TEC estimation is emerging negative or zero values. For GIM data, this leads to zero values in some GIM cells. Actually, the solution for such problems was suggested a long time ago. We need to restrict the estimated values ,37. Firs(1)The algorithm first computes the usual least-squares solution. This solution is returned as optimal, if it lies within the bounds. If not, the algorithm finds all variables within the bounds (free set) and beyond (active set).(2)At each iteration the algorithm chooses a new variable  to move from the active set to the free set.(3)b in (7) is changed by active set. Least-squares solution for new equation system contains variables beyond the bounds, the gradient correction is applied to all the free set (see [New equation system for free set is created where set (see  for deta(4)The iterations continue until all the variables are in the free set.We used the Python library scipy.optimize.lsq_linear based on algorithm suggested by Start and Parker . The main iterations for a problem with n variables. As a result we obtain non-negative (positive) vertical TEC and slant TEC at all the lines of sight. To obtain robust DCB estimates IDCB, we calculate the parameters simultaneously for different time instants over 24 h, thus solving a consistent set of Equations (7). The temporal resolution for kt may vary from 2 h to 5 min.This algorithm ensures an accurate solution eventually, but may require about N is the number of satellites in the constellation. We applied condition (9) for GPS and GLONASS  separately, following Schaer [To analyze the satellite DCB separately, we apply the often-used zero-mean condition :(9)∑i=1Ng Schaer .To simulate the algorithm operation, we used the IRI-2012 model with a set of the International Union of Radio Science (URSI) coefficients, recommended by the URSI and the IRIcorr topside ionosphere profile option . To checθ > 60°; 5 + 1·S10(θ + 30) TECU at θ < 60°. Also, we introduced outliers for the pseudorange and losses of phase lock for the phase, which could occur with a probability. Thus, at the output, we obtained a series of the phase TEC and the pseudorange TEC corresponding to a certain time instant, elevation, and azimuth. Further, we used these values and calculated ionospheric parameters based on the above algorithm. Finally, the parameters obtained as a result of the algorithm operation were compared with the modeled vertical TEC, time derivatives, and gradients. The obtained DCBs were compared with those specified as the simulation input.Simulation was performed for a selected real station. For each recorded satellite at each time instant, we calculated the electron density and TEC along the line of sight. Further, we introduced the DCB-related error, as well as random noise to the phase TEC and the pseudorange TEC. This value was 0.01 TECU for the phase TEC. For the pseudorange TEC, the noise value was assigned depending on the elevation: five TECU at We validated data based on ionosphere modeling and TEC products from alternative software mentioned in α=0.96 for THU2. We did not find significant influence of the plasmosphere on TuRBOTEC estimations, except high latitudes, where we should to use another α in (4).The differences between TuRBOTEC simulation and the IRI-plas vertical TEC are shown in green. The results show that TuRBOTEC provides even better performance for modeling based on IRI-plas at mid-latitudes, and comparable performance at low latitudes. Higher difference at high latitudes can be due to using α obtained through the IRI-2012 modeling, so we used The greatest deviation occurs in the equatorial region. This is related to a substantially inhomogeneous ionosphere structure in this region, particularly, during daylight hours. The error is less at high latitudes. Although, as compared with the mid-latitude region, the error is higher. This is related to the absence of satellite observations at high elevations in high latitude regions, and to the dominance of the southward contribution to the total measurement statistics.maxKp = 2.3, maximal Kp for the day) and for the 17 March 2015 magnetic storm .One can see well that the TEC curves reproduce the diurnal variation similarly, but they can quantitatively differ. Such systematic differences are well-known and were repeatedly addressed in literature . Like diVΔI difference distribution between the vertical TEC data in the Irkutsk region.One can see that, at individual instants, synchronous variations with TEC are noted in the CODE and TuRBOTEC data. For example, for the 17 March 2015 strong magnetic storm, one can see a slight increase in the vertical TEC around 18 UT. In general, the estimates for the vertical TEC appear plausible for all the considered cases. Deviations in the data from other laboratories are within the variance interval of different laboratories among themselves. To analyze systematic variances, we built a histogram for the The vertical TEC from the previous version of the suggested technique  was experimentally checked in ,59. ThosAbsolute gradients differ from <0.1 TECU/deg. for longitude gradients in the high-latitude regions to 2.5 TECU/deg. for latitude gradients in the equatorial regions during the equatorial anomaly evolution.S function that converts the slant TEC into the vertical TEC. In the equatorial anomaly region, such an approximation works worst of all, and, whereas the vertical TEC estimate is quite reasonable  involves able see , the spaWhen estimating the vertical TEC at a growing distance from a station, this leads to an error. In S mapping function has an essential latitude dependence. Therefore, at a significant deviation, the vertical TEC value converted from the slant TEC is not precisely determined. Hence, the gradients are not precisely determined, either. It is worth noting that, in this case, this error would grow almost linearly. From One can see that the latitude errors grow more than when moving at a similar distance by longitude. This may be determined by that the maxKp = 2.3 (left panel), and for 17 March 2015, maxKp = 7.7 (right panel). Like for the absolute vertical TEC data  in the same way. Thus, for Equation (7), we exchange N variables (where N is a number of satellites) by N’ those (where N’ is a number of continuous series). This improves the solution (see red line in We can solve the emerged problem by excluding DCB from Equation (1):We have developed an algorithm to recover the absolute TEC, its gradients, its time derivative, and DCBs. The procedure is based on the space-and-time Taylor expansion and bounded-variable least squares. We termed it TayloR-series and Bounded-variable-least-squares based iOnosphere TEC (TuRBOTEC). We simulated the algorithm operation by using the IRI-2012 and the IRI-plas models. The absolute TEC values recovered through the developed algorithm were established to agree  with the IRI-2012 model-set values. The mean standard deviation for the mid-latitude IRKJ station is 0.09 TECU, for the equatorial NTUS is 0.35 TECU, for the high-latitude THU2 is 0.17 TECU. We did not find significant influence of the plasmosphere on TuRBOTEC estimations, except for high latitudes, where we should use another α for the mapping function. About 9% of experimental vertical TECs from the TuRBOTEC estimates exceed (by more than one TECU) those from the same algorithm but without constraints.S mapping function has an essential latitude dependence. Therefore, at a significant distance, the vertical TEC value converted from the slant TEC is not precisely determined. Hence, the gradients are not precisely determined, either. One should note that, in this case, this error would grow almost linearly, but this is not absolutely so. The real (including the model ones) latitude gradients have a considerably spatially non-uniform character at a distance from a station, and, at distances more than 10°–15°, the TEC estimates based on gradients, start to surpass 10 TECU.The recovered values of the TEC spatial gradients and of the TEC time derivative agree qualitatively with the model-set values. Also, we studied the accuracy of TEC estimates by means of the latitudinal and longitudinal gradients, for the ionosphere at a distance from a station. The latitude errors were established to grow more dramatically, than those of longitude. This could happen, because the The DCB values obtained through the developed algorithm for GPS satellites agree with the GIM and CODE data, but, for the GLONASS DCB values, the deviation from the CODE data is up to 17 TECU. At the same time, the recovered DCBs (at the IRI-2012 simulation) agree well with the initial data. At large errors in determining DCBs after correcting the slant TEC series, one may observe negative unphysical TEC values.The developed software may be used to calculate the vertical TEC from the local networks or to locally update ionosphere models . The ver


Nitrogen (N) and Phosphorus (P) are essential nutritional elements for life processes in water bodies. However, in excessive quantities, they may represent a significant source of aquatic pollution. Eutrophication has become a widespread issue rising from a chemical nutrient imbalance and is largely attributed to anthropogenic activities. In view of this phenomenon, we present a new geo-dataset to estimate and map the concentrations of N and P in their various chemical forms at a spatial resolution of 30 arc-second (∼1 km) for the conterminous US. The models were built using Random Forest (RF), a machine learning algorithm that regressed the seasonally measured N and P concentrations collected at 62,495 stations across the US streams for the period of 1994–2018 onto a set of 47 in-house built environmental variables that are available at a near-global extent. The seasonal models were validated through internal and external validation procedures and the predictive powers measured by Pearson Coefficients reached approximately 0.66 on average. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11948916 However, today anthropogenic production of N and P to support fertilisation and industrial releases4 has dramatically increased the N and P presence in water bodies. This has led to the widespread eutrophication of both inland and coastal waters5.Nitrogen (N) and phosphorus (P) are key nutritional elements for many important life processes such as protein and DNA synthesis, primary production, cellular growth and reproduction. Both have a natural global cycle that includes conversion between different inorganic and organic forms, solid and dissolved (and gaseous for nitrogen) phases that maintained their pre-industrial concentrations within certain natural bounds. During the preindustrial era, the concentrations and fluxes of N and P in rivers were generally small, much less than present day levels, and were mainly sourced from erosion and the leakage of dissolved N and P in their organic/inorganic forms6. However, our current ability to map N and P concentrations across regions or the globe is still limited. Early attempts focused on concentrations and fluxes from major rivers7 and were implemented through bottom-up approaches, which estimated N and P content based on our knowledge of land-use and population influences on river nutrients11. Other local and regional studies have also featured different combinations of bottom-up, process based, and statistical models, which link N concentrations in inland water to environmental variables15.Over the past decades, significant progress has been made towards our understanding of the dynamics of natural and anthropogenic inputs of N and P to inland waters. Furthermore, the recognition of human impact on the N and P cycle has driven much research into the scope for better management of these nutrients16. This set of stream variables at the near-global scale provides a new base for stream-relevant biotic and abiotic modelling, such as variability in biodiversity, nutrient distributions, or water flows. Based on this platform, we present a new method for mapping the concentrations of N and P in various chemical forms across continental waters based on a machine learning approach. The resulting N and P maps can be used to study nutrient loading and processing in inland waters. For instance, fertiliser run-off presents a high load of chemical nutrients in recipient freshwater bodies, and can be charted by the aforementioned method18. The N and P maps possess information about the location of nutrient-enriched streams, which can guide engineered de-nitrification processes20. In addition to resource recovery, a mitigation strategy can be employed through the improved management of nutrient-rich wastes. In this approach as well, the derived N/P ratio map can prove a valuable source of information on where N vs P limitation might be located regionally. Furthermore, this unique N and P modelling can be used in conjunction with process-based methods to enhance the understanding of metabolism and recycle of N and P in riverine systems.Freshwater environmental variables  that account for the basin and upstream environment have recently been computed21 (in form of GeoTIFF raster layers) derived by connecting freshwater environmental variables with in situ measurements and map the distribution of various N and P compounds in water bodies across the conterminous US for the period of 1994–2018 recorded in the Water Quality Portal (WQP)22. Random Forest (RF)23, a well-established machine learning algorithm was employed in this study for its exceptional capability of handling complex and heterogeneous data. We demonstrate in detail below how RF has excelled to date at capturing local geographical variations of stream predictors, and produces superior predictability for N and P distributions in the US. The mapped resolution of the predicted N and P concentrations is at a 30 arc-second (∼1 km) gridded stream network24 for four seasons. Moreover, the quality and appeal of the proposed geo-dataset21 lies in the rigorous scripting and modelling procedures that was applied to treat sparse spatio-temporal observations. Additionally, the computation was performed by employing multi-core processing in a super computer which requires advanced geocomputation programming skills. The described geo-dataset21 is ready for use as input data in various environmental models and analyses. The newly developed geo-dataset21 and the methodological framework are suitable for large-scale environmental analyses such as N and P emissions in small and large rivers at a global scale. To our knowledge, this is the first time that N and P concentrations have been estimated at such high spatial resolution for the territory large as the contiguous US.In this paper, we present a gridded geo-datasetThe Methods section is divided into two subsections that includes: (i) Data pre-processing, that describes cleaning the gauge stations source data , spatial/seasonal variability and stream layers (referred hereafter as predictors); (ii) Modelling framework, that concerns data splitting and model training/validation/prediction.22, which is so far the largest standardised water quality database25. From WQP22, we retrieved the measured concentration data for N and P nutrients in their various chemical forms for the period from 1994 to 2018 with data spanning US stream networks. Each single observation is associated with its sampling geolocation (latitude and longitude) and a USGS Parameter Code (PC) to indicate its chemical identity. We selected five nutrients  of interest as the response variables , the U.S. Environmental Protection Agency (EPA) and the National Water Quality Monitoring Council developed the Water Quality Portal (WQP)22 were provided by multiple organisations26. Employing such multi-sourced data for the “secondary use”, i.e. beyond the original intention proposed by the original data collection agencies26, can result in a number of challenges. For instance, intermittent sampling activities and data gaps in time series complicated the temporal analyses for long-term trend. Data records can be misinformative owing to instrument failure, missing measurement that are labelled as “0” values and incorrect use of physical units26. Such errors might produce extreme values beyond the natural value range and trend , and also large number of “0” values). We removed extreme values by data trimming using certain thresholds.The chemical nutrients recorded in WQPμ is the mean and σ is the standard deviation and E is the expected value.The distribution of the raw observation data at day-level resolution for all nutrients  were highly left-skewed, as quantified by the third standardised moment Eq. 1\documen27  less than two, determined by iterative trials. The data after cleaning are reported in Table 28 rejected the null hypothesis that a temporal trend exists in the time series. Additionally, we plotted the data distributions across the continuous US for each year in Supplementary Fig. We performed spatial and temporal analyses to better inform the design of modelling strategies. Within the current data set, we identified only eight stations with eight or more years of data continuity for a single chemical species  and sum (precipitation) across each sub-catchment. Here, soil data refers to the soil within the depth of 2.5 cm (0–5 cm thickness)29. This yielded a series of predictors such as the upstream average forest cover, upstream sum of precipitation that mimics surface run-off and the average upstream temperature16, available at www.earthenv.org/streams.To build the predictive models, we used a total of 47 predictors belonging to four categories: topography16. The unit for each stream variable is derived from an original, spatially continuous environmental variable across the land surface area. Thus, temperature is expressed in degrees Celsius, precipitation in millimetres, and land cover as a percentage of each class (e.g. Urban/built-up class in percentage). We refer to16 for further details regarding the calculation of the freshwater-specific predictors.All predictors except for climate were static, as opposed to being time-updated. Monthly climate data was averaged to a seasonal level as described in Table r.stream.snap function in GRASS GIS33 with 3 km as the maximum distance tolerance. After snapping, we computed the seasonal mean for each chemical species by considering all the points that fell in the same snapped location. This led to a unique one-to-one association between a geographical identification and an averaged concentration value for each season and each chemical species.Due to the possible spatial discrepancy between the HydroSHEDS stream network and the gauge station locations, the latitude and longitude locations of the gauge stations do not consistently fall directly on the stream grids. Hence, we snapped the geolocations (latitude and longitude) of the stations to the HydroSHEDS stream network using the v.kernel available in GRASS GIS33) for each species and season. The pixel values of the resultant density surface were used as weighting factors to split the data into training and testing subsets that possess identical spatial distributions.We split the full dataset into two sub-datasets, training and testing respectively. To consider the heterogeneity of the spatial distribution of the gauge stations, we employed the spatial density estimation technique in the data splitting step by building a density surface using Gaussian kernels with a bandwidth of 50 km (using xi represents the observation and i) at various proportions of the training-testing subsets  with 50 times independent samplings for each trial. The trial repetition intended to sample different combinations of training and testing so as to reduce the bias of the sample estimate. To this end, we labelled the MRSE as In order to optimise the split ratio between the training and testing subsets, we explored the Mean Root Square Error (MRSE = te and its low variability (defined as the standard deviation of MRSEte) at the proportion 0.5, we decided to use it as the optimal cut to build the final models.As shown in the Supplementary Fig. randomForestSRC35 to train the models. RF regression is an ensemble learning strategy that elevates the collective predictive performance of a large group of weaker learners (regression trees). Two key elements contributing to the superiority of the RF algorithm are bootstrapping aggregation (bagging) and random selection of variables. Bagging (bootstrap sampling from the training sub-dataset) aims at reducing data noise through averaging. Data that is not included in the bag is called an out-of-bag (OOB) sample. Random drawing of variables improves variance reduction by reducing the intercorrelation between trees. OOB samples can be used to validate the model performance  and evaluate the variable importance. The variable importance is of great value in identifying the most influential variables that direct predictive outcomes and thus offer adaptive or intervention strategies in response to the modelled phenomena. One important feature of the RF algorithm is its relative resilience towards data noise due to the two mechanisms mentioned above. This technical advantage of RF directly benefits the analysis of environmental data. The attractiveness of the randomForestSRC package was that it allows considering the sample distribution density in the bagging step. In the model development, we paid close attention to the model stability. We noticed that the superparameter as the number of trees had a strong impact on the model errors as shown in Supplementary Fig. We employed the RF regression algorithm implemented in the R-package The predicting performance on the training and testing sets provided complementary information for the model validation. Training primarily exhibits model robustness, i.e. stability and balance of model predictability in the presence of data shuffling. Testing measures the model performance on the unseen data and addresses the model fitness. In this context we used the Pearson correlation coefficient as the statistical metric to quantify the predictive performance of the models.xi represents the observation and i) to numerically quantify model uncertainty, since the it offers a more discernible measure of prediction accuracy. Thus, we denote:To supplement the Pearson correlation coefficient and provide an in-depth assessment of model accuracy, we calculated the Root Mean Square Error (RMSE = (i) th and above the 80th percentiles, obtaining RMSE can also be used to obtain a comparison of accuracy across high and low-density gauge station distribution. To this end, we calculated a partial Lastly, after establishment of the predictive models, we investigated the contributions of each variable to the predicted outcomes by means of the “variable importance”, an output from RF.The final validated RF models were applied to predict each of the 30-arc-second stream grid cell within the conterminous US, for all the nutrients . The predictive outcomes were then reversely transformed back to recover their original physical values (in ppm).21. The nutrient concentrations, mapped across the conterminous USA, are available in a compressed GeoTiff file format in the WGS84 coordinate reference system (EPSG:4326 code). All layers are stored as floating points (Float32 data type) to ensure sufficient precision for future use and analysis for varied purposes.We provide TN, TDN, NO3, TP, and TDP concentrations (ppm) for four seasons  for the gridded stream network at a spatial grain of 30 arc-second (∼1 km). All layers are available for download at PANGAEA repositoryThe predicted nutrient maps follow the layer name convention:nutrient abbreviation_resolution_season.formatTN_1KM_winter.tif: layer showing the Total Nitrogen for the winter season at 30 arc-second spatial resolution.TP_1KM_summer.tif: layer showing the Total Phosphorus for the summer season at 30 arc-second spatial resolution.Below are two examples of the layer names for the two main nutrients product TN and TP36.For the purpose of visual interpretation of the results, we plotted the TN and TP bivariate maps as shown in Fig. The Pearson correlations between predicted and observed values for TN and TP are in the range of 0.56–0.81 across the testing sets as shown in Fig. In Fig. 37), which highlighted the significance of human influence and suggested the need for further completing the variable list (iii) the original highly skeweness of the observation data and the associated box-cox transformation implemented.From the residual maps we also noticed that the model sometimes underestimates the higher values. Three possible causes may have contributed to this result: (i) untrustful observations (ii) anthropogenic actions that are not fully included in the current environmental variable layers  based on MERIT-DEM44 by adopting the procedure described in45. The MERIT-DEM derived stream network is also under development46. These former described layers will be useful in combination with other global maps of irrigated areas47, livestock48, agricultural fertiliser use49, soil types/properties50 to compute N and P concentrations more accurately on a global scale. We encourage potential users of the described geo-dataset to contact the authors for future product updates.Overall, the newly-developed layers provide the basis for a variety of high-resolution, nutrient-related analyses across the inland waters in the conterminous US. A global-scale N and P assessment with new stream predictors at higher resolution (3-arc-second) is under development by our group. The focus is on creating new geomorphometry variables (Geomorpho90mSUPPLEMENTARY INFORMATION


Cognition is claimed to be extended by a wide array of items, ranging from notebooks to social institutions. Although the connection between individuals and these items is usually referred to as “coupling,” the difference between notebooks and social institutions is so vast that the meaning of “coupling” is bound to be different in each of these cases. In this paper I argue that the radical difference between “artifact-extended cognition” and “socially extended cognition” is not sufficiently highlighted in the literature. I argue that there are two different senses of “cognitive extension” at play, that I shall label, respectively, “implementation extension” and “impact extension.” Whereas implementation extension is a causal-functional notion, impact-extension hinges on social normativity that is connected with organization and action coordination. I will argue that the two kinds of cognitive extension are different enough to warrant separate labels. Because the most salient form of social extension of cognition involves the reciprocal co-constitution of cognitive capacities, I will propose to set it apart from other types of extended cognition by using the label “symbiotic cognition.” In the literature on extended, integrated and distributed cognition, human cognitive systems are said to be coupled with and enhanced by a large number of rather diverse items, ranging from simple notebooks and abacuses , via comimplementation base of these processes, socially extended cognition alters the nature and hence extends the impact of cognitive engagements with the world by embedding them in social practices of coordinated behavior. When we interpret socially extended cognition as an instance of impact-extension and not as implementation-extension, the problem of cognitive bloat disappears.The paper is set-up as follows. In the next section I will introduce the notion of extended cognition and highlight the difference between artifact-extended cognition and socially extended cognition. In the section “The Problem of Cognitive Bloat,” I will briefly discuss the problem of cognitive bloat such as this has first been proposed as an argument against the early varieties of cognitive extension. I will argue that if socially extended cognition is indeed modeled on artifact-extended cognition, it falls prey to this problem in such a blatant way that it is clear that we must understand socially extended cognition differently. In the section “Implementation-Extension and Impact-Extension,” I will propose a characterization of the difference between artifact-extended cognition and socially extended cognition. I will argue that cognition can be considered to be extended in different ways. Whereas artifact-extended cognition extends cognitive processes by extending the In the section “Causality, Coordination, and Reciprocal Cognitive Dependency,” I will defend and elaborate on the distinction between “implementation-extension” and “impact-extension” by arguing that, crucially, the chain of items causally linked to a person whose cognition is socially extended involves other human beings—other cognitive systems. On the one hand, this introduces social normativity into the extended system, which is absent in artifact-extended cognition. On the other it introduces the idea of reciprocal cognitive dependency between people. I will propose the label “symbiotic cognition” for networks of mutually dependent cognitive systems. In the section “Cognitive Symbiosis, Weak and Strong,” I will define the notion of symbiotic cognition. I will allow for the possibility of socially extended cognition that is not symbiotic cognition, and will distinguish between weak forms of symbiotic cognition, that do not require social institutions, and strong forms that do. In the section “Symbiotic Cognition, Cognitive Integration and Distributed Cognition,” I will compare the idea of symbiotic cognition with integrated cognition , 2013 anThe idea that human cognitive systems are in fact extended by items outside our brains and bodies has been developed and defended by many philosophers for over two decades now. Disregarding precursors, the idea that started the debate on extended cognition—then labeled “active externalism” —was baseBrain-chauvinists think there is a relevant difference. On their view, Otto does not remember the MoMa address. Rather, he believes the address is in the notebook, perceives the contents of the notebook and forms a new belief about the address. On this reconstruction all the mental work is done in Otto’s head, not outside it. The response to this “Otto two-step” , 46 is e1 The wider variety of items that our cognition is said to be coupled with, which is the main topic of this paper, stems mainly from the second wave of extended mind theories. These are not based on the parity principle, but on the complementarity principle  is in some real sense an expression of several minds externalized and extended into the world, instantiating in external memory an agreed-upon decision, adding to a system of rights and laws that transcend the particularities of any individual’s mind. Contracts are institutions that embody conceptual schemas that, in turn, contribute to and shape our cognitive processes , p. 6.practice. It is not just scribbles and sounds, but also the way we use these in social interactions.The point I wish to make in this section is that somewhere along the way in ascending from notebooks as possible cognitive extensions to socio-cultural institutions, a crucial distinction is ignored. The connection between individuals and the items their cognition is extended with is described as more or less similar—it is described as “coupling.” What coupling entails must depend on what we couple with. Hence, in order to maintain similarity throughout the ascent from notebooks to institutions, items that are said to extend our cognition are very often  described as physical objects. Language, for example, is described as a set of physical symbols. But apart from involving a set of physical symbols, language is also a social This certainly goes for social institutions too. Legal systems involve courtrooms, togas, and in some countries wigs. But they also involve rules, conventions and practices. A contract is an externalized memory not just because of its physical properties but mainly because of the way these pieces of paper (or bunches of bits) function in legal practice. Gallagher acknowledges that cognition can also be extended by institutions that are less formal and reinforced, such as practices involving cultural conventions:In solving a problem like keeping my cattle in my pasture, my bodily manipulations of a set of wooden poles and wire are not necessarily part of the cognitive process; but my engagement with the particular local custom/practice of solving this problem with a fence (and even a specific kind of fence) is a cognitive part of the problem solving. In such cases, cultural practices, local know-how in the form of established practices, etc., in either formal or informal ways, enter into and shape the thinking process. Without such cultural practices, rules, norms, etc. our thinking – our cognitive processes – would be different , p. 10.Interestingly, the difference between physical objects and practices is not seen as an obstacle for claiming that coupling is basically similar when we move from notebooks to institutions:Just as a notebook or a hand-held piece of technology may be viewed as affording a way to enhance or extend our mental possibilities, so our encounters with others, especially in the context of various institutional procedures and social practices may offer structures that support and extend our cognitive abilities , p.4.Let us call cognition that is extended by physical objects “artifact-extended cognition.” The  question I would like to pose is whether Gallagher, Clark and Menary are correct (on some interpretations of their views) in assuming that socially extended cognition is really continuous with artifact-extended cognition. Are coupling with artifacts and coupling with practices really similar enough to warrant the use of the same label—extended cognition—in both instances?In order to make a beginning with driving a wedge between artifact-extended cognition and socially extended cognition, it is useful to look at what is known as the problem of cognitive bloat . This isFrom the perspective of artifact-extended cognition, Otto-and-notebook-style, the response to the threat of cognitive bloat is to tighten the constraints on what counts as co-constituents of cognition. Clark proposes four extra constraints:(1)That the resource be reliably available and typically invoked. .(2)That any information thus retrieved be more or less automatically endorsed. It should not usually be subject to critical scrutiny . It should be deemed about as trustworthy as something retrieved clearly from biological memory.(3)That information contained in the resource should be easily accessible as and when required.(4)That the information in the notebook has been consciously endorsed at some point in the past and indeed is there as a consequence of this endorsement , 79.This does limit the possible candidate artifacts that may be said to extend cognition considerably. Arguably, the remaining problem is a matter of intuition. It is surely the case that even with these extra criteria our extended minds are bigger and more scattered than traditional brain-based or neo-Cartesian intuitions would make them out to be. But they are not so large and scattered that it is incoherent to think of them as single cognitive systems.One of the reasons for this is that the external items we are said to be coupled with are not themselves coupled with still further structures in ways that satisfy 1–4. But this is exactly the problem with socially extended cognition. If we are coupled with social institutions, we are coupled with structures that are constituted, among other things, by (very many) other human beings. These human beings are themselves coupled with further structures in the same way we are coupled with them. And this makes the cognitive system implausibly large and scattered—if we are able to draw boundaries at all. For this reason, even philosophers who are sympathetic to the idea that human cognition involves massive coupling with our external niches are reluctant to think of social institutions as co-constituents of our cognitive systems . AccordiThe point I wish to make here is not that socially extended cognition clearly falls prey to the problem of cognitive bloat. Rather, the point is that (i) it would fall prey to the problem of cognitive bloat if socially extended cognition is a proposal that is modeled completely on the idea of artifact-extended cognition, and (ii) if it is interpreted in this way it falls prey to the problem of cognitive bloat so obviously and blatantly that it seems unlikely that socially extended cognition is intended to be modeled completely on artifact-extended cognition.Gallagher is ambivalent here. On the one hand he does present socially extended cognition as a proposal that is somehow derived from the idea of artifact-extended cognition . On the other hand, however, he distances himself from Clark’s functionalism and the way Clark deals with the problem of cognitive bloat. Tightening the restrictions on what counts as proper cognitive extension in the way Clark does, emphasizes the idea that the brain is still the central hub of any cognitive system, however extended this system is. And it is precisely such brain-centeredness that Gallagher wishes to overcome with the idea of socially extended cognition. But now the question arises: how is avoiding brain-centeredness and including social practices and institutions in the list of co-constituents of our cognitive processes going to help sidestep the problem of cognitive bloat?I believe the answer here is to distance the idea of socially extended cognition even more from the idea of artifact-extended cognition than Gallagher does.2 of the cognitive system responsible for that repertoire. Differently put: some of the cognitive work in our interactions with the world has to be performed by items external to our brains and bodies. I believe there are different ways in which these descriptions can be made more precise. And I believe that the way in which we do this depends on our views of what cognition consists of. In this section I will sketch two different ways of unpacking the idea of cognitive extension. One is tailor-made for the functionalist view of cognition that underlies Clark-style artifact-extended cognition. The other is more suitable for Gallagher-style enactivist views of cognition—even though I am less sure he would accept it.To say that cognition is extended is to say that items external to our brains and bodies expand our cognitive repertoire in such a way that they can somehow be said to co-constitute the “mechanisms”The meaning of cognitive extension that fits a functionalist outlook on cognition such as Clark’s best is what I will label “implementation extension.” According to functionalists, cognitive states and processes are to be characterized as functional role states and transitions from one set of functional states to another .Feature (iv) does not imply that reciprocal co-constitution of cognitive abilities is necessarily symmetrical. It may well be that by playing different roles in the same social structure we co-constitute different cognitive abilities in each other.Feature (v) is deliberately vague about the nature of social structures. The term might refer to social institutions, but this need not be the case. There is structure in human interactions when there are identifiable roles that interact in ways that allow us to discern regularities. The sense in which social structures “pre-exist” before symbiotic cognitive processes can occur is metaphysical, and not necessarily temporal : without the context of a social structure, a symbiotic cognitive process cannot exist as such.Various forms of collective cognitive activity satisfy (i–v), without being instances of the type of cognition Gallagher refers to, i.e., cognition in the context of social institutions. Group-memory is a well-researched case in point. While some researchers argue that memory storage and retrieval by groups is impaired relative to the sum memory abilities of the individual members of a group , there iIn general, task division in couples that live together for some time often rigidifies into shared routines, that are usually based on tacit knowledge of individual proclivities and talents, and that usually amount to the automatic complementing of each other’s cognitive efforts. Such routines would make the couple into a symbiotic cognitive systems in terms of the above definition. Let me take the following, simplified case as an example: when on vacation, my wife always takes care of train- plane- or boat-tickets and the planning of when we should go where and what to see, whereas I do navigation and hotel arrangements, tents (in which case my wife determines the campsite) and guesthouses.This (simplified) arrangement satisfies (ii–v):(ii)My actions of arranging tent-gear and navigating result in having a complete vacation, including interesting trips, a nice campsite, a boat trip, etcetera, because they are done in the context of a (weakly) symbiotic system. This is a form of impact extension; outside of this context the same actions would not have that effect.(iii)There is most certainly a kind of normativity involved in our division of cognitive labor. This is based on precedent and on shared assessment of talents which leads to mutual expectations.(iv)We co-constitute each other’s cognitive abilities. By dividing complementary cognitive tasks and by using many automatized interaction routines that let us share information when necessary (and not when not necessary), we co-constitute each other’s ability to realize a full vacation with roughly half the effort.(v)These routines—our implicit knowledge of the way in which we divide cognitive labor and share results when necessary—counts as social structure of the relevant kind .5 but even if we disregard these, such cases are not instances of symbiotic cognition. For first, and most importantly, these relations do not satisfy (iv): the cognitive extension is a one-way affair and not reciprocal—teachers extend the cognition of students, but not vice versa and writers extend the cognitive abilities of readers, but not vice versa. This might be argued to affect (ii), (iii), and (v) as well. To start with (v), the social structures involved are not structures of the right kind because they do not involve mutual dependency. Also, these relations do not involve the right kind of normativity. There may certainly be normativity involved in these relations or in playing the relevant roles involved, but not necessarily normativity of the kind that renders the behavior of others predictable so that cognitive engagements by the agent are impact-extended. Which means that (ii) is not satisfied either. Having said that, though, nothing hinges on these assessments of the applicability of (ii), (iii), and (v); the non-applicability of (iv) suffices to rule out these cases as cases of symbiotic cognition.6Are there forms of socially extended cognition that do not satisfy (ii–v)? I believe that that is possible, depending on how widely we apply the term “socially extended.” For example, the relation between a student and a teacher might be described as socially extended cognition—the student’s cognition is extended by the teacher’s . Likewise, a reader’s cognitive abilities might be thought of as being extended by the cognitive activities of a writer. There are reasons to be cautious here in describing such cases as instances of socially extended cognition,within the context of their respective institutions. Many of our daily cognitive activities have this property. Signing a contract is not intelligible in abstraction from a legal system, voting is not intelligible in abstraction from a social structure which allows for joint decision making, being polite by shaking hands is not intelligible in abstraction from a system of cultural conventions, etcetera. What I will label “strong” or full-fledged symbiotic cognition, then, adds one more requirement to (i–v):I have labeled forms of symbiotic cognition that do not involve social institutions “weak symbiotic cognition,” because they differ in one important respect from socially extended cognition of the type Gallagher discusses. I believe that the discussion of the previous sections suffices to show that (i–v) apply to Gallagher’s cases. But these cases have a striking feature that is lacking in the case of a married couple jointly planning and having a vacation or the case of collective memory. The cognitive engagements Gallagher discusses are only intelligible (vi)possible and intelligible only within the context of a social institution.Cognitive processes are are intelligible in abstraction from the system. My activity of navigating or booking a hotel does not require my wife’s activity of planning trips and booking tickets to be intelligible. Neither does the individual memory-contribution of an individual to a transactive memory system require reference to other people to be intelligible as a memory process.7 Weak symbiotic cognitive systems combine individual cognitive processes, that do not require the system to exist, into a larger system that is beneficial to participants. Strong symbiotic cognition, by contrast, cannot be reduced to a collection of individually intelligible cognitive processes. It is only in connection with the whole system that strongly symbiotic cognitive processes are cognitive processes at all. It is not just that the whole is more than the sum of its parts (see footnote 7), the point is rather that there are no identifiable relevant parts without the notion of the whole.Crucially, the example of married couples with ingrained automatized routines, or transactional memory systems, are not examples of strong symbiotic cognition. For the individual cognitive processes within such symbiotic systems means to sign a contract involves reference to a very complex social structure in which rights and obligations exist and can be changed. “Rights and obligations” refers to very specific norm-guided, socially structured behavior. It is not possible to identify that behavior fully, in turn, without referring back to contracts. The roles and regularities of the social structures involved in strong symbiotic cognition are holistically inter-defined . Shaking hands as a greeting opens up a new space of social interaction possibilities due to the fact that those involved all participate in the same system of cultural conventions—it is a “move” within the “game” of social etiquette that is meaningless or weird to anyone who does not share your conventions.The holistic inter-defining of roles and regularities implies that strongly symbiotic cognitive engagements or processes are necessarily aimed at accomplishing a given state of affairs Feature (vi), then, transforms weak symbiotic cognition into a qualitatively different kind of cognition. If (vi) is added to (ii–v), and the five features together are taken as interconnected, then (ii–v) are substantially strengthened. Of course feature (v) is further defined by limiting the pre-existing social structures to social institutions. But this affects the other features too. Impact extension (ii) within a strongly symbiotic system is substantially more encompassing than impact extension in a weakly symbiotic system. Setting a whole reorganization of a company in motion by raising a single hand illustrates the point. This is a different scale of impact-extension than having a whole vacation with half the work. (iii) The normativity involved in social institutions is not merely dependent on precedent and implicit assessment of talents and proclivities. Precisely because it applies to much larger groups, it is usually reinforced, either explicitly, as in legal systems, or implicitly, as in a system of social etiquette. (iv) The co-constitution of cognitive abilities in strongly symbiotic systems is much more elaborate than in weakly symbiotic systems. First of all this is because many more people are involved. But secondly this is because most social institutions, instill a wide range of “new” cognitive abilities in those who help to enact them.As said, I take Gallagher to refer to strong of full-fledged symbiotic cognition in his discussion of socially extended cognition. In the remainder of this paper I will refer to this type of cognition simply as “symbiotic cognition.”So far, I have limited the discussion to the literature on extended cognition, arguing that symbiotic cognition differs from “normal,” artifact-extended cognition in some important respects. There are other theories about the essential embeddedness of our cognitive systems. Richard Menary’s notion of cognitive integration does emphasize the expansion of our cognitive repertoire by engaging with a wide variety of cultural items, including social structures, but without making claims about the extension of our cognitive systems as such. Edwin Hutchins’ notion of socially distributed cognition, by contrast, allows for whole social institutions to count as cognitive systems. I have argued that the literature on extended cognition has swept an important distinction under the carpet; it has not sufficiently recognized that socially extended cognition is—at least very often—a type of cognition of its own, fundamentally different from artifact-extended cognition. But it may well be that this distinction is respected by the notions of integrated cognition or distributed cognition. I which case I may have said nothing new. I will briefly argue, however, that neither cognitive integration, nor distributed cognition is very sensitive to the distinction I have argued for above.The idea of cognitive integration is in many respects very close to the idea of extended cognition. Cognitive integration is also close to the enactivist view in that it emphasizes that cognition consists of bodily manipulations of the world, often involving man-made cognitive devices . Cognitive processes are cognitive practices, and these can be hugely expanded by involving a host of different items. The items mentioned in the cognitive integration literature fall in the same (wide) range as the devices referred to by extended cognition theorists. The crucial difference with extended cognition is that while according to Menary items such as linguistic symbols, smart phones, abacuses and social institutions allow for a whole new range of cognitive practices, they are enabling conditions for such practices, rather parts of our minds. In this respect, Menary is closer to those who argue that external devices scaffold our cognition, rather than extend it e.g., .It should be noted that the notion of cognitive extension that Menary rejects is a variant of what I have labeled “implementation extension” above. Even though he tends toward an enactivist notion of cognition rather than a classical functionalist one, he still speaks of cognition “supervening” on a realization base and thinks of cognitive extension in terms of enlarging this base. This raises the question whether perhaps impact-extension might be compatible with the idea of cognitive integration. The similarity between the enactivist notion of cognitive engagement and Menary’s notion of cognitive practices might suggest this. Indeed, there are clear similarities. Menary speaks of the “transformation” of our minds by cognitive artifacts and our interactions with them in a way that suggests that manipulating these artifacts has a cognitive yield in the context of cognitive practices that the same manipulation would not have outside of such practices. The impact of an ignorant infant who happens to manipulate numeric symbols such that they accidentally represent a calculation differs from the impact of a mathematically trained person who performs the same manipulation. This is akin to the difference between someone coincidentally putting a scribble on a piece of paper and someone signing a real contract. The practice extends the impact of the manipulation.However, even though it may be argued that this type of “transformation” of cognitive processes is very much like impact-extension, this does not mean that the idea of cognitive integration already contains or implies the notion of symbiotic cognition. On Menary’s view, all cognitive integration is somewhat like impact-extension. The contrast between socially extended/integrated and artifact-extended/integrated cognition—or between what I would prefer to call extended and symbiotic cognition—is not made. Hence, in this respect it will not help to abandon extended-cognition talk in favor of cognitive integration.What about socially distributed cognition? On Hutchins’ original proposal,  sociallyIn many respects, therefore, symbiotic cognition can be viewed as a variant of the cognitive ecosystems view implied by later versions of the idea of distributed cognition. The one thing that is missing, however, like in the case of cognitive integration, is the relevant contrast between extended and symbiotic cognition. I have argued that there is an important distinction between cognitive extension as the extension of the causal-functional implementation base of cognitive processes, which is best applicable in cases where cognition is extended by physical artifacts only, and cognitive extension as the idea that our cognitive engagements with the world have massively enhanced impact in the context of normative, rule-based coordination of actions in a social practice. Though both types of cognition might equally well be called “extended,” they are extended in radically different ways. In order to mark this difference, and given the reciprocal cognitive co-constitution between humans in impact-extended cognition, I have proposed to label what is now known as socially extended cognition “symbiotic cognition.”The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Our previous study demonstrated that α2AP knockout mice exhibit spatial memory impairment in comparison to wild-type mice, suggesting that α2AP is necessary for the fetal and/or neonatal development of the neural network for spatial memory. However, it is still unclear whether α2AP plays a role in the memory process. The present study demonstrated that adult hippocampal neurogenesis and remote spatial memory were enhanced by the injection of an anti-α2AP neutralizing antibody in WT mice, while the injection of α2AP reduced hippocampal neurogenesis and impaired remote spatial memory, suggesting that α2AP is a negative regulator in memory processing. The present study also found that the levels of α2AP in the brains of old mice were higher than those in young mice, and a negative correlation between the α2AP level and spatial working memory. In addition, aging-dependent brain oxidative stress and hippocampal inflammation were attenuated by α2AP deficiency. Thus, an age-related increase in α2AP might cause cognitive decline accompanied by brain oxidative stress and neuroinflammation. Taken together, our findings suggest that α2AP is a key regulator of the spatial memory process, and that it may represent a promising target to effectively regulate healthy brain aging. Lysine In addition to α2AP, plasmin presents in the mouse brain , 7, and −/−) mice in comparison to α2AP+/+  mice [−/−mice exhibit impaired memory, including working memory, spatial memory and fear conditioning memory, in comparison to WT mice [Our previous study demonstrated that the length of dendrites was markedly shorter and the number of dendritic branches was markedly lower in the hippocampal neurons from α2AP knockout (α2APWT) mice . Given tWT) mice , the conWT) mice . Thus, α WT mice . ConsideIt is widely accepted that the hippocampus is a crucial brain region for learning and memory. Adult neurogenesis in the hippocampus is one of the most important mechanisms for the spatial memory process; the inhibition of adult neurogenesis by irradiation impairs long-term spatial memory, while the enhancement of neurogenesis after running facilitates LTP and spatial memory , 21. Fur−/−) mice were generated by homologous recombination using embryonic stem cells, as described previously [Male 12-week-old C57BL/6J mice were purchased from Japan Charles River . α2AP-deficient . The alteration of their behavior was calculated as the ratio of the number of alterations to the total number of arm entries minus 2.Mice received visible platform pre-training on the first day, followed by hidden platform training for two days. In the hidden platform training, two sessions consisting of four trials per session were performed on two days. Mice were placed into the pool from four different directions in each of the four trials, and the escape latency was measured by a video-tracking system . The second day of training was performed the following day, followed by a 60-s probe test without a platform. The time in each quadrant was analyzed by a video-tracking system (SMART). To evaluate long-term memory, the probe tests were performed 1 and 3 months after hidden platform training.After the first day of MWM training, mice were anesthetized with 1.8–1.9% isoflurane, and then were injected 200 nM of α2AP  or 0.1 μg/μL of an anti-α2AP neutralizing goat antibody  as well as saline or normal goat IgG control, respectively  in a total volume of 20 μL into the lateral ventricle  with a two-step needle  attached to a glass syringe . After injection, the needle was held at the site for 1 min to prevent reverse flow.5-Bromo-2′-deoxyuridine (BrdU)  was dissolved in saline, and injected intraperitoneally at 24-h intervals for 7 days at 50 mg/kg. The mice were perfused with PBS and 4% paraformaldehyde (PFA) in PBS, and the brains were then fixed in 4% PFA for 48 h, then soaked in 30% sucrose for 5 days. Thirty-micrometer-thick frozen coronal sections were prepared and stained with anti-BrdU mouse antibody  and anti-Ki67 rabbit antibody  after 2 N HCl-treatment at 37℃ for 30 min, neutralization with 0.1 M of boric acid pH 8.5 at room temperature for 10 min, and blocking with a Mouse on Mouse blocking kit  and blocking solution  in PBS). The sections were then incubated with anti-BrdU and anti-Ki67 antibodies at room temperature at 4 °C overnight. After washing with PBS, the sections were treated with Alexa 488-conjugated goat anti-mouse IgG  and Alexa 546-conjugated goat anti-rabbit IgG , and then coverslipped in Prolong Gold™ antifade reagent (Invitrogen). The specimens were observed using a confocal laser microscope NIKON A1R .2O2 in methanol at room temperature for 10 min. The sections in Retrievagen A (pH 6.0) (BD Biosciences) were autoclaved and washed in PBS, and then incubated with blocking solution at room temperature for 1 h, and treated with anti-doublecortin (Dcx) mouse antibody  at 4 °C overnight. After washing with PBS, they were incubated with biotinylated anti-rabbit IgG antibody  at room temperature for 30 min. The detection of antibody-antigen complexes was accomplished using a Vectastain Elite ABC kit (Vector Laboratories) and Metal-Enhanced DAB Substrate kit . The immunostained sections were photographed using a microscope with a digital camera . Images were taken at full resolution with a single image dimension set at 1360 × 1024 pixels.Thirty-micrometer-thick frozen coronal sections were treated with 0.3% HAfter perfusion with PBS, the hippocampi and cerebral cortexes from the mice were homogenized and sonicated in lysis buffer: 10 mM Tris–HCl buffer (pH 7.5) containing 1% SDS, 1% Triton X-100, and a protease inhibitor cocktail . The protein concentration in each lysate was measured using a BCA protein assay kit . Lysates containing equal amounts of protein were subjected to SDS–polyacrylamide gel electrophoresis on a 10% acrylamide gel. Proteins were transferred onto PVDF or nitrocellulose membranes. After blocking with 3% skim milk in Tris-buffered saline containing 0.05% Tween-20 (TBS-T), the membranes were incubated with anti-α2AP goat antibody , anti-hexanoyl-lysine (HEL) mouse antibody , or anti-glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mouse antibody  at 4 °C overnight. After washing with TBS-T, the membranes were incubated with horseradish peroxidase-conjugated rabbit anti-goat IgG , or goat anti-mouse IgG  for 1 h. After washing again, immunoreactive bands were detected using Chemi-Lumi One Super  with an LAS-3000 mini-image analysis system . The band intensities were quantified using the ImageJ software program. The same sample as a loading control was included in each Western blot analysis, and the band intensities were normalized.2. The outflow fraction for the first 3 h was discarded, and then the dialysis sample perfused with ACSF at a flow rate of 1 μL/min was collected for 1 h under anesthesia with 1.5% isoflurane.The α2AP levels in the cerebrospinal fluid and plasma were measured with a mouse α2AP ELISA kit . The cerebrospinal fluid was collected as detailed below. Briefly, a guide cannula  was implanted into the lateral right ventricle , fixed to the skull with dental cement , and was then occluded with a dummy cannula . The mice were returned to their home cage and allowed to recover for 2 days. A microdialysis probe  was inserted into the lateral right ventricle through the guide cannula under anesthesia with 1.5% isoflurane. The probe was perfused continuously at a flow rate of 10 μL/min with artificial cerebrospinal fluid (ACSF) containing 147 mM NaCl, 4 mM KCl and 3 mM CaCl3, centrifugation was performed at 15,000×g for 15 min. The resultant supernatants were each mixed with an equal volume of 2-propanol. After centrifugation, the pellets were rinsed with 75% ethanol/diethylpyrocarbonate (DEPC)-treated water and then dried. The pellets were each dissolved in an appropriate volume of DEPC-treated water as total RNA fractions. RNA from each sample (1 µg) was transcribed using ReverTra Ace-α  according to the manufacturer’s protocol. Quantitative PCR was performed to analyze the murine IL-6, TNF-α and IL-1β mRNA expression relative to the GAPDH mRNA expression using a MiniOpticon real-time PCR system . We used the following primers: IL-6, 5′-GTTCTCTGGGAAATCGTGGA-3′ (sense) and 5′-GGAAATTGGGGTAGGAAGGA-3′ (antisense); TNF-α, 5′-AAATGGGCTTTCCGAATTCA-3′ (sense) and 5′-CAGGGAAGAATCTGGAAAGGT-3′ (antisense); IL-1β, 5′-CAAATCTCGCAGCAGCACA-3′ (sense) and 5′-TCATGTCCTCATCCTGGAAGG-3′ (antisense); and GAPDH, 5′-TGTGTCCGTCGTGGATCTGA-3′ (sense) and 5′-TTGCTGTTGAAGTCGCAGGAG-3′ (antisense). The fold-change in the expression levels of IL-6, TNF-α and IL-1β relative to the GAPDH expression as an endogenous control gene were determined by the − ∆Ct method.Total RNA was isolated from the hippocampi and cerebral cortexes using TRIsure . After the addition of CHClt-test. P values of < 0.05 were considered to indicate statistical significance.Data are reported as the mean ± standard error of the mean (SE). Differences among mean values were analyzed using a one-way analysis of variance (ANOVA) followed by an LSD post-hoc test or Student’s To first determine whether α2AP mediates adult neurogenesis in the dentate gyrus (DG) of the hippocampus, we examined the effect of a neutralizing antibody against α2AP on neurogenesis. The number of BrdU-positive and Ki67-negative cells that had exited the cell cycle was significantly increased in the DG of the anti-α2AP antibody-injected mice in comparison to that of the control mice, although there were few Ki67-positive cells, proliferating cells, in the DG of the mature adult mice Fig. a, b. We We next investigated the effect of α2AP injection on adult hippocampal neurogenesis and the spatial memory process. The number of BrdU-positive and Ki67-negative cells in the DG was significantly decreased by the injection of α2AP in comparison to the control mice Fig. a, b. AccThe degree of α2AP levels and functions in the brain was suggested to affect the hippocampus-dependent spatial memory. Thus, in order to elucidate the relationship between the α2AP levels and brain aging accompanied by cognitive decline, we first compared the levels of α2AP in the brain between young and old mice. The expression of α2AP in both the hippocampus and cerebral cortex in old mice was remarkably higher in comparison to young mice, while there was no significant difference in the expression of plasmin Fig. a, b. TheWe next analyzed correlations between the relative levels of α2AP in the hippocampus and cerebral cortex, and spontaneous activity and the working spatial memory scores in the Y-maze test Fig. c, d. The−/− mice. The degree of oxidative stress was assessed by detecting the levels of 13-hydroperoxyoctadecanoic acid (13-HPODE)-modified proteins, which reacts specifically with an anti-HEL antibody  was implanted into the lateral right ventricle , fixed to the skull with dental cement, and then occluded with a dummy cannula . The mice were returned to their home cage and allowed to recover for 1 week. The Y-maze test was performed 1 week after the ventricular cannulation. On the next day of the Y-maze test, an injection cannula  was connected through polyethylene tubing to a Hamilton syringe that had been preloaded with 0.1 μg/μL of an anti-α2AP neutralizing goat antibody (R&D System) or normal goat IgG control (R&D System), and inserted into the guide cannula in the awake mice. Each solution was injected in a total volume of 20 μL. On the next day, the Y-maze test was performed again.Additional file 1: Figure S1. Impaired spatial working memory in old mice in comparison to young mice. The Y-maze test was performed in young and old C57BL/6J mice . The mice were placed in the center and allowed to explore the apparatus for 8 min. The alteration of behavior was calculated as the ratio of the number of alterations to the total number of arm entries minus 2. The values represent the mean ± S.E. Statistical significance was evaluated using Student’s t-test. *P < 0.05.Additional file 2: Figure S2. The effects of anti-α2AP neutralizing antibodies on spatial working memory in young and old mice. The Y-maze test was performed before and after an intraventricular injection of anti-α2AP neutralizing antibodies or control IgG in young and old C57BL/6J mice . The values represent the mean ± S.E. Statistical significance was evaluated using a paired t-test. *P < 0.05.Additional file 3: Figure S3. Comparison of the levels of inflammatory cytokines in the brain between young and old mice. The mRNA levels of IL-6, IL-1β and TNF-α in the hippocampus (A) and the cerebral cortex (B) were determined by real-time PCR . Statistical significance was evaluated using Student’s t-test. *P < 0.05.Additional file 4: Figure S4. The effects of excess plasmin on spatial memory. (A) Plasmin or saline was intracerebroventricularly injected in 12-week-old C57BL/6J mice after the first day of training in the MWM test. On the second day, mice were repeatedly trained, and probe tests were performed 30 minutes and 1 month later. (B) The results of the training sessions. The latency to the target in each trial was measured. The values represent the mean values of 4 trials in each session. There was no difference in latency to the platform between the plasmin-injected mice and the control mice. (C) The results of the probe tests 30 minutes after training. The time in the target quadrant was longer than the other quadrants in both groups of mice, and the time in each quadrant did not differ between the two groups. The swimming velocity of the plasmin-injected mice and the control mice did not differ to a statistically significant extent . (D) The results of the probe tests at 1 month after training. The time spent by the plasmin-injected mice in the target quadrant was significantly shorter in comparison to the control mice, although the time in the target quadrant was still longer than the time in the opposite quadrant in both groups of mice. (E) The values represent the mean ± S.E. . Statistical significance was evaluated using an ANOVA with an LSD post-hoc test. *P < 0.05, **P < 0.01.


Background: To analyze the clinical characteristics of nephrotic syndrome (NS) with complications of cerebral sinovenous thrombosis (CSVT) in children.Method: Clinical, radiographic, laboratory, and treatment data obtained from 10 confirmed cases of NS with complications of CSVT were analyzed. All patients were followed up for at least 18 months. CSVT was diagnosed by cerebral computed tomography (CT) and/or magnetic resonance imaging (MRI) with or without magnetic resonance venography (MRV) of the cerebral vessels.Results: Among 10 cases reported, 4 were steroid-sensitive NS with frequent relapse, 5 were steroid-resistant , and 1 was steroid-sensitive with one relapse. Common clinical manifestations were headache or ophthalmodynia complicated by vomiting, dizziness, convulsion, and coma. Neuropathologic signs were positive in some cases. Papilledema appeared in only one case with winding of vein. Cerebrospinal fluid was examined in three cases with elevated pressure but normal cytological and biochemical results. D dimer and fibrinogen levels were elevated while prothrombin time and activated partial thromboplastin time were shortened. Five out of seven cases who had performed cranial CT were suspicious for cerebral thrombosis. Nine cases had cranial MRI with abnormal signs in seven cases. All of the cases received MRV, confirming the diagnosis of CVST.Conclusion: Clinical manifestations of NS with CSVT are not specific but varied. Therefore, CSVT should be considered once nervous manifestations present. MRV is a better method in the diagnosis of CSVT. Thrombosis is one of the common complications of nephrotic syndrome (NS). Renal veins, veins of the lower extremities, and pulmonary artery are the most common sites of thrombosis. The incidence of cerebral sinovenous thrombosis (CSVT) in children is much lower than that in adults, though the true incidence may be underestimated for many events that are asymptomatic or had a delay in diagnosis . HoweverTen children with NS complicated by CSVT were included in this study. They were admitted to the children kidney disease center in the First Affiliated Hospital of Sun Yat-sen University between August 2005 and August 2020. NS was diagnosed according to the criteria defined as heavy proteinuria (urine protein > 50 mg/kg/day), hypoalbuminemia (ALB <25 g/L), hypercholesteremia (cholesterol > 5.7 mM/L), and clinical edema. We excluded patients with NS secondary to systemic disorders including systemic lupus erythematosus, hepatitis B-related nephropathy, vasculitis, and congenital NS. CSVT was diagnosed by cerebral computed tomography (CT) and/or MRI with or without MRV of the cerebral vessels , 15.Urine protein was qualitative determined in degree from negative to positive with one plus to four plus (–~ 4+). Biochemical indexes including serum albumin, cholesterin, and serum creatinine were determined by automatic biochemical analyzer. Blood coagulation function test including D dimer, prothrombin time (PT), activated partial thromboplastin time (APTT), and fibrinogen were detected with ELISA assay. Blood samples for coagulation function test were collected in negative pressure vacuum anticoagulant tube while other samples were also collected and examined in appropriate processes at the time of thrombosis.Cases enrolled were retrospectively analyzed. Detailed clinical, radiographic, laboratory, and treatment data were obtained at the time of thrombosis. All patients have been followed up for at least 18 months.The study was conducted in accordance with the principles outlined in the 1964 Declaration of Helsinki and with approval from the ethics committee of the First Affiliated Hospital of Sun Yat-sen University. Written informed consent was obtained from all of the patients' parents or guardians.A total of 10 patients were enrolled in the analysis in our center over a period of 15 years (about 200 children diagnosed with NS are admitted into the center every year). There were nine males and one female aged 3 to 10 years old with an average age of 6.1 years old. There were no thrombotic events for family history or previous thrombotic events occurred in all 10 cases. Thrombotic screening with Doppler ultrasound for other sites such as lower limb veins and abdominal vessels were done in all cases and turned out to be negative. The detailed characteristics of those patients are shown in The course of NS ranged from 1.5 months to 5 years. Four cases were steroid-sensitive nephrotic syndrome (SSNS). None of the 10 patients had hypovolemic shock. For blood volume assessment, we conducted the test of passive leg raising (PLR) and rehydration test in all patients, which could predict volume responsiveness, as well as with comprehensive assessment of hematocrit, urine specific gravity, urine volume, blood pressure, and capillary refill time (CRT), none of the patients developed hypovolemia. Except for case 4, case 9, and case 10, the other seven patients had edema. Patients with SSNS were also diagnosed as frequent-relapsing NS (FRNS), among them two developed CSVT during relapses while the other two were in remission. Five cases were steroid-resistant NS (SRNS) including two cases of minimal change disease (MCD) and one case of IgA nephropathy (IgAN) confirmed by subsequent renal biopsies. There was one steroid-dependent NS (SDNS) with one relapse. At the time of CSVT being diagnosed, steroid was administered in nine cases. The other case was given Cyclosporin A (CsA) only and steroid was discontinued 1 week before neurological symptoms appeared. Four cases had received diuretic treatments. Clinical characteristics of those patients are summarized in CSVT could be classified into three clinical types according to the interval between onset of neurological symptoms and suspicion of CVST. Interval of <48 h, 48 h to 1 month, and more than 1 month are referred to as acute, subacute, and chronic CSVT, respectively. In this study, five patients presented with acute CSVT, in which convulsion occurred in one patient within 6 h after renal biopsy under basal anesthesia by ketamine. Three appeared as subacute and the other two patients with SSNS were chronic. CSVT was mainly manifested by headache, dizziness, convulsion, vomiting, altered consciousness, and even papilledema; these symptoms can present in isolation or in association with other symptoms. Eight out of the 10 patients presented with headache predominantly in the forehead. Headache in case nine remitted spontaneously but later recurred and progressed to paroxysmal and severe headache 1 week before admission. Case 10 had predominant pulvinar headache that occurred as paroxysm during the day but remitted when he fell asleep. Case 8 also had paroxysmal headache and had been misdiagnosed as nasosinusitis. Only one case presented with ophthalmodynia in the right eye. Seven patients had non-projectile vomiting. One patient exhibited irritability. An episode of coma had occurred in case 4 with loss of awareness and response to external stimuli. Papilledema occurred in case 9 with fundoscopy demonstrating elevation and blurring of optic disc and swelling veins along its margins without retinal hard exudates. Focal neurological deficits were not observed in patients. Meanwhile, abnormal neurological signs included low muscle tone of extremities, absent patellar tendon reflex, neck rigidity, and Babinski's sign. Yet, four patients showed no abnormal neurological signs. Nine patients had normal blood pressure except for case 5. None of them had intracranial infection . Imaging of case 4 showed a low-intensity shadow in the corona radiata of posterior limb of internal capsule, which was suspected to be a flow artifact. Neuroimaging obtained by MRI was conducted in nine patients. Summary of results is listed as follows . The duration of anti-coagulation therapy was 6 months. Consciousness recovered gradually from coma for Case 4, the patient was able to walk but incontinent, subsequent recovery was not available because of withdrawal 18 months later. Case 5 had fully recovered nearly 20 days after treatment. The condition of the other eight patients also improved gradually. Case 9 occasionally complained of headache and dizziness. Initial symptoms along with positive signs of nervous system all disappeared for the other cases. Urine protein was found to be negative for cases 9 and 10. For cases 1 and 2, proteinuria showed remission once CsA was administered and did not fluctuate after steroid withdrawal. For case 4, there was remission of proteinuria after 4 months. For cases 3 and 6, urine protein was persistently positive during the process of steroid reduction until tacrolimus was added. Case 7 had three relapses in 1 year; hence, tacrolimus was administered in combination with steroid, similarly in case 8. Reexamination of MRV in cases 1 and 10 showed no abnormal findings after 6 months. The thrombi became smaller in cases 5 and 7 after 1 month and 10 days, respectively. The thrombi had shrank 1 month later and disappeared 4 months later in case 8  and MRI (77.8%), proposing that MRV is a better modality in the diagnosis of CSVT. Symptoms and signs could be fully recovered in 3 months to a year under comprehensive therapies of anti-coagulation, thrombolysis, and control of NS. Risk factors and predictive indexes for thrombosis in NS children need to be further explored.The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by ICE for Clinical Research and Animal Trials of the First Affiliated Hospital of Sun Yat-sen University. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. Written informed consent was obtained from the minor(s)' legal guardian/next of kin for the publication of any potentially identifiable images or data included in this article.YM and XJ designed the study, and reviewed and revised the manuscript. LR and LC carried out the initial analyses and drafted the initial manuscript. ZD and LR were responsible for the image collection. HZ and ZL coordinated and supervised the data collection. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.